BEGIN:VCALENDAR VERSION:2.0 PRODID:-//PR Stats - ECPv6.15.20//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:PR Stats X-ORIGINAL-URL:https://prstats.org X-WR-CALDESC:Events for PR Stats REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:Europe/London BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20340326T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20341029T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20350325T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20351028T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20360330T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20361026T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20370329T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20371025T010000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;VALUE=DATE:20350809 DTEND;VALUE=DATE:20350812 DTSTAMP:20260420T031746 CREATED:20250731T202440Z LAST-MODIFIED:20260410T084328Z UID:10000494-2070230400-2070489599@prstats.org SUMMARY:Time Series Analysis and Forecasting (TSAFPR) DESCRIPTION:Model Selection and Model Simplification (MSMS06)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, November 18th\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – UK (GMT) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n \n \n \n \n \n \n \n \n \n \n About this course\n This course is aimed towards researchers analysing field observations\, who are often faced by data heterogeneities due to field sampling protocols changing from one project to another\, or through time over the lifespan of projects\, or trying to combine legacy data sets with new data collected by recording units. \nSuch heterogeneities can bias analyses when data sets are integrated inadequately or can lead to information loss when filtered and standardized to common standards. Accounting for these issues is important for better inference regarding status and trend of species and communities. \nAnalysis of such ‘messy’ data sets need to feel comfortable with manipulating the data\, need a full understanding the mechanics of the models being used (i.e. critically interpreting the results and acknowledging assumptions and limitations)\, and should be able to make informed choices when faced with methodological challenges. \nThe course emphasizes critical thinking and active learning through hands on programming exercises. We will use publicly available data sets to demonstrate the data manipulation and analysis. We will use freely available and open-source R packages. \nThe expected outcome of the course is a solid foundation for further professional development via increased confidence in applying these methods for field observations. \nBy the end of the course\, participants should be able to: \n\nUnderstand basic statistical concepts related to detection error\nWork with field collected data and data from automated recording units (ARU)\nKnow packages such as unmarked\, detect\, bSims\nCritically evaluate modelling options and assumptions using simulations\nFit N-mixture\, distance sampling\, and time-removal models to data\n\n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students working on projects related to avian data\nApplied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a programming language such as R for analysing point count data arising from avian field surveys\n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time Zone – UK (GMT) local time \nAvailability – 25 places \nDuration – 3 days\, 4 hours per day \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT \nLanguage – English \n \n \n \n \n \n Teaching Format\n Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data. \n \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical\, mathematical and physical concepts. Specifically\, generalised linear regression models\, including mixed models; basic knowledge of calculus. \n \n \n \n \n \n Assumed computer background\n Familiarity with R\, ability to import/export data\, manipulate data frames\, fit basic statistical models (up to GLM) and generate simple exploratory and diagnostic plots. \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n \n\n\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Programme\n \n \n \n \n \n \n \n \n \n \n Tuesday 18th\n Day 1 – Classes from 13:30 – 17:30 \nIntroduction \n\nIntroduction and background\nReview of field sampling techniques\nIntroduction to agent-based simulations\nOverview of regression techniques\nNaïve estimates of occupancy and abundance\nMultiple visits and N-mixture models\n\n \n \n \n \n \n Wednesday 19th\n Day 2 – Classes from 13:30 – 17:30 \nIntroduction to modelling \n\nBird behaviour\nTime-removal models\nObservation process\nDistance sampling\nCombining removal and distance sampling (QPAD)\n\n \n \n \n \n \n Thursday 20th\n Day 3 – Classes from 13:30 – 17:30 \nDifferent approaches \n\nSingle visit-based approaches (N-mixture and SQPAD)\nAnalysing data from recording units\nMulti-species models and using species traits and phylogeny\nDealing with roadside and other biases\nClosing remarks\n\n \n \n \n \n \n \n Course Instructor\n \nDr. Peter Solymos \nPéter is an ecologist and R programmer. He has worked with continental scale data sets and developed statistical techniques for estimating population density from messy data sets. He is the author of numerous well-known R packages\, including detect\, dclone\, vegan\, and ResourceSelection. He works currently as a data scientist helping utility companies improving their outage and impact prevention practices\, and is an adjunct professor at the University of Alberta in Edmonton\, Canada. \nGoogle Scholar \nWork Homepage \nPersonal Homepage URL:https://prstats.org/course/time-series-analysis-and-forecasting-tsafpr/ LOCATION:Recorded\, United Kingdom CATEGORIES:General Recorded Courses,Previously Recorded Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2025/07/TSAFPR.jpg GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20350810 DTEND;VALUE=DATE:20350811 DTSTAMP:20260420T031746 CREATED:20250922T092135Z LAST-MODIFIED:20260410T095249Z UID:10000539-2070316800-2070403199@prstats.org SUMMARY:Data Visualisation in R using ggplot2 (DVGGPR) DESCRIPTION:Model Selection and Model Simplification (MSMS06)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, December 1st\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – Portugal (GMT+1) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you). \n \n \n \n \n \n \n \n \n \n \n About This Course\n Have you built an Ecological Niche Model? If yes\, you have already encountered challenges on data preparation\, or have struggled with issues in models fitting and accuracy. This course will teach you how to overcome these challenges and improve the accuracy of your ecological niche models. By the end of 5-day practical course\, you will have the capacity to filter records and select your variables with variance inflation factor; to test effect of Maxent regularization parameter in models performance; to validate models performance and accuracy; to perform MESS analysis\, null models\, and mechanistic models\, as well as to build your “virtual species”. \nEcological niche\, species distribution\, habitat distribution\, or climatic envelope models are different names for mechanistic and correlative models\, which are empirical or mathematical approaches to the ecological niche of a species. These methods relate different types of ecogeographical variables (environmental\, topographical\, human) to species physiological data or geographical locations\, in order to identify the factors limiting and defining the species' niche. ENMs have become popular because of their efficiency in the design and implementation of conservation management. \nBy the end of 5-day practical course should be able to: \n\nfilter records and select your variables with variance inflation factor;\ntest the effect of Maxent regularization parameter in models performance;\nvalidate models performance and accuracy;\nperform MESS analysis\, null models\, and mechanistic models\, as well as to build your “virtual species”.\n\nStudents will learn to use functions implemented in the packages “usdm”; “dismo”; “ENMEval”; “SDMvspecies”; “spThin”; and “NicheMapper” among others. \n \n \n \n \n \n Intended Audiences\n This course is orientated to PhD and MSc students\, as well as other students and researchers working on biogeography\, spatial ecology\, or related disciplines\, with experience in ecological niche models. \n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Details\n Time Zone – Portugal (GMT+!) local time \nAvailability – 24 places \nDuration – 5 days\, 7 hours a day \nContact hours – Approx. 35 hours \nECT’s – Equal to 3ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n The course will be mainly practical\, with some theoretical lectures. All modelling processes and calculations will be performed with R\, the free software environment for statistical computing and graphics (http://www.r-project.org/). Students will learn to use functions implemented in the packages “usdm”; “dismo”; “ENMEval”; “SDMvspecies”; “spThin”; and “NicheMapper” among others. \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of ecological niche models and biogeography in general is required\, thus we will assume the attendees know how to run an ecological niche model. \n \n \n \n \n \n Assumed computer background\n Solid knowledge in Geographical Information Systems and R statistical package is necessary. It is also essential to have experience in ecological niche models. We will focus exclusively on advanced methods. If you need an introductory course on ecological niche models\, please consider attending our basic course on PRStatistics (www.prstats.org). \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n  \n \n \n \n \n \n \n \n \n \n \n Monday 1st\n Day 1 – Classes from 09:30 to 17:30 \n\nENM guide: how to model\nENM R packages.\nSources of environmental variables using geodata package.\nGetting species records with geodata package.\n\n \n \n \n \n \n Tuesday 2nd\n Day 2 – Classes from 09:30 to 17:30 \n\nVariable selection with variance inflation factor (VIF) and usdm packages.\nChoosing the correct study area.\nFiltering records using usdm/spThin packages.\nChoosing pseudo-absences with Biomod2 package.\n\n \n \n \n \n \n Wednesday 3rd\n Day 3 – Classes from 09:30 to 17:30 \n\nSplit records in training and test with ENMeval package.\nTest effect of Maxent regularization parameter.<.li>\nComparing correlative models with AIC\, with ENMeval package.\n\n \n \n \n \n \n Thursday 4th\n Day 4 – Classes from 09:30 to 17:30 \n\nMESS practice with Biomod2 package.\nValidate models null models.\nVirtualSpecies virtualspecies packages.\n\n \n \n \n \n \n Friday 5th\n Day 5 – Classes from 09:30 to 17:30 \n\nMechanistic model NicheMapper packages.\n\n \n \n \n \n \n \n \n \n Dr. Neftali Sillero\n \n Neftalí Sillero works in the analysis and identification of biodiversity spatial patterns\, from species to populations and individuals. For this\, he uses four powerful tools to better understand how space influence biodiversity: Geographical Information Systems\, Remote Sensing\, Ecological Niche Modelling\, and Spatial Statistics. His main areas of research are: application of new technologies on species’ distributions atlases\, ecological modelling of species’ ranges\, identification of biogeographical regions and species’ chorotypes\, mapping and modelling road-kill hotspots\, and spatial analyses of home ranges. \nHe has more than 10 years’ experience working in ecological niche models. He has authored >70 peer reviewed publications and he is since 2007 Chairman of the Mapping Committee of the Societas Herpetologica Europaea\, where he is the PI of the NA2RE project (www.na2re.ismai.pt)\, the New Atlas of Amphibians and Reptiles of Europe \nPersonal website \nWork Webpage \nResearchGate \nGoogleScholar \n \n \n \n \n \n \n \n \n \n \n \n \n Teaches\n \nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM)\n\n \n \n \n \n \n Teaches\n \nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM)\n\n \n \n \n \n \n \n \n \n Dr. Salvador Arenas-Castro\n \n Dr. Salvador Arenas-Castro is a broad-spectrum ecologist with interesting in differentintegrative perspective of the fundamental ecology\, macroecology and biogeographywith their both application and relationship to climate and land management. He is alsoexploring other research sources in agroecology\, forestry\, spatial ecology\, andecoinformatics\, all addressed by explicitly considering the spatial component ofecological processes\, mainly applying spatially explicit modelling approaches\, GIS andremote sensing techniques. Please check his webpage for further information:https://salvadorarenascastro.wordpress.com \nGoogle Scholar: https://scholar.google.com/citations?user=UAYiB5UAAAAJ&hl=es&oi=aoResearchGate: https://www.researchgate.net/profile/Salvador-Arenas-Castro URL:https://prstats.org/course/data-visualisation-in-r-using-ggplot2-dvggpr/ LOCATION:Recorded\, United Kingdom CATEGORIES:General Recorded Courses,Previously Recorded Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2025/09/DVGG05.jpg GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20350810 DTEND;VALUE=DATE:20350813 DTSTAMP:20260420T031746 CREATED:20250831T133217Z LAST-MODIFIED:20260410T090032Z UID:10000526-2070316800-2070575999@prstats.org SUMMARY:A Comprehensive Introduction to Machine Learning (CIMLPR) DESCRIPTION:Model Selection and Model Simplification (MSMS06)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, November 18th\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – UK (GMT) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n \n \n \n \n \n \n \n \n \n \n About this course\n This course is aimed towards researchers analysing field observations\, who are often faced by data heterogeneities due to field sampling protocols changing from one project to another\, or through time over the lifespan of projects\, or trying to combine legacy data sets with new data collected by recording units. \nSuch heterogeneities can bias analyses when data sets are integrated inadequately or can lead to information loss when filtered and standardized to common standards. Accounting for these issues is important for better inference regarding status and trend of species and communities. \nAnalysis of such ‘messy’ data sets need to feel comfortable with manipulating the data\, need a full understanding the mechanics of the models being used (i.e. critically interpreting the results and acknowledging assumptions and limitations)\, and should be able to make informed choices when faced with methodological challenges. \nThe course emphasizes critical thinking and active learning through hands on programming exercises. We will use publicly available data sets to demonstrate the data manipulation and analysis. We will use freely available and open-source R packages. \nThe expected outcome of the course is a solid foundation for further professional development via increased confidence in applying these methods for field observations. \nBy the end of the course\, participants should be able to: \n\nUnderstand basic statistical concepts related to detection error\nWork with field collected data and data from automated recording units (ARU)\nKnow packages such as unmarked\, detect\, bSims\nCritically evaluate modelling options and assumptions using simulations\nFit N-mixture\, distance sampling\, and time-removal models to data\n\n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students working on projects related to avian data\nApplied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a programming language such as R for analysing point count data arising from avian field surveys\n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time Zone – UK (GMT) local time \nAvailability – 25 places \nDuration – 3 days\, 4 hours per day \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT \nLanguage – English \n \n \n \n \n \n Teaching Format\n Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data. \n \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical\, mathematical and physical concepts. Specifically\, generalised linear regression models\, including mixed models; basic knowledge of calculus. \n \n \n \n \n \n Assumed computer background\n Familiarity with R\, ability to import/export data\, manipulate data frames\, fit basic statistical models (up to GLM) and generate simple exploratory and diagnostic plots. \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n \n\n\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Programme\n \n \n \n \n \n \n \n \n \n \n Tuesday 18th\n Day 1 – Classes from 13:30 – 17:30 \nIntroduction \n\nIntroduction and background\nReview of field sampling techniques\nIntroduction to agent-based simulations\nOverview of regression techniques\nNaïve estimates of occupancy and abundance\nMultiple visits and N-mixture models\n\n \n \n \n \n \n Wednesday 19th\n Day 2 – Classes from 13:30 – 17:30 \nIntroduction to modelling \n\nBird behaviour\nTime-removal models\nObservation process\nDistance sampling\nCombining removal and distance sampling (QPAD)\n\n \n \n \n \n \n Thursday 20th\n Day 3 – Classes from 13:30 – 17:30 \nDifferent approaches \n\nSingle visit-based approaches (N-mixture and SQPAD)\nAnalysing data from recording units\nMulti-species models and using species traits and phylogeny\nDealing with roadside and other biases\nClosing remarks\n\n \n \n \n \n \n \n Course Instructor\n \nDr. Peter Solymos \nPéter is an ecologist and R programmer. He has worked with continental scale data sets and developed statistical techniques for estimating population density from messy data sets. He is the author of numerous well-known R packages\, including detect\, dclone\, vegan\, and ResourceSelection. He works currently as a data scientist helping utility companies improving their outage and impact prevention practices\, and is an adjunct professor at the University of Alberta in Edmonton\, Canada. \nGoogle Scholar \nWork Homepage \nPersonal Homepage URL:https://prstats.org/course/a-comprehensive-introduction-to-machine-learning-cimlpr/ LOCATION:Recorded\, United Kingdom CATEGORIES:General Recorded Courses,Previously Recorded Courses ATTACH;FMTTYPE=image/png:https://prstats.org/wp-content/uploads/2025/08/CIMLPR.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20350823 DTEND;VALUE=DATE:20350828 DTSTAMP:20260420T031746 CREATED:20250828T125128Z LAST-MODIFIED:20251203T092539Z UID:10000520-2071440000-2071871999@prstats.org SUMMARY:Multivariate Analysis of Ecological Communities Using VEGAN DESCRIPTION:Model Selection and Model Simplification (MSMS06)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, September 15\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – Reunion (GMT+4) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \n \n \n \n \n \n \n \n \n \n \n About This Course\n This 5-day course covers R concepts\, methods\, and tools that can be used to analyse community ecology data using (but not limited to) the R package VEGAN. The course will review data processing techniques relevant to multivariate data sets. We will cover diversity indices\, distance measures and distance-based multivariate methods\, clustering\, classification and ordination techniques using the R package VEGAN. We will use real-world empirical data sets to motivate analyses\, such as describing patterns along gradients of environ-mental or anthropogenic disturbances\, quantifying the effects of continuous and discrete predictors. We will emphasise visualisation and reproducible workflows as well as good programming practices. The modules will consist of introductory lectures\, guided computer coding\, and participant exercises. The course is intended for intermediate users of R who are interested in community ecology\, particularly in the areas of terrestrial and wetland ecology\, microbial ecology\, and natural resource management. You are strongly encouraged to use your own data sets (they should be clean and already structured\, see the document: “recommendation if you participate with your data”. \nWe will cover the following:\n\n\nFundamentals of community ecology\,\nDiversity indices\,\nMethods to transform data and calculate distance measures\,\nClassifications (i.e.\, clustering methods) organise the data into synthetic groups and present them in a tree (dendrogram).\nOrdinations (i.e.\, unconstrained methods) reveal the multivariate dimension in only a few dimensions (axes).\nCanonical ordinations (i.e.\, constrained methods) test hypotheses related to multivariate patterns.\n\n\n\nIn addition the course provides lectures and practices on how to create reproducible workflows and use good programming practices in R.\n \nTopics covered during the course include: terrestrial and wetland ecology\, microbial ecology\, and natural resource management\, evolution\, palaeoecology.\n\n\n \nDuring the workshops you will follow guided computer coding exercises using either your own data or real empirical datasets to motivate analyses. Exercises include describing patterns along gradients of environmental or anthropogenic disturbance\, quantifying the effects of continuous and discrete predictors.\n \nYou are strongly encouraged to use your own datasets (they should be clean and already structured\, please contact use if you plan to do this\, we will help you to prepare the data). You will benefit from full support in applying multivariate methods to your dataset (defining of the research question\, transforming your data\, selecting the most appropriate method\, carrying out the analysis and interpreting the results).\n\n\n\n \n \n \n \n \n Intended Audiences\n Any researchers (PhD and MSc students\, post-docs\, primary investigators) and environmental professionals who are interested in implementing best practices and state-of-the-art methods for modelling species’ distributions or ecological niches\, with applications to biogeography\, spatial ecology\, biodiversity conservation and related disciplines. \n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time Zone – Reunion Island (GMT+4) local time \nAvailability – 20 places \nDuration – 5 days\, 8 hours a day \nContact hours – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English (with the option to discuss individually in French) \n \n \n \n \n \n Teaching Format\n The course will be divided into theoretical lectures to introduce and explain key concepts and theories\, and practices with workshop sessions on R. We will cover roughly 2 modules per day\, each module consists of ~1h30/2h lecture + coding\, break\, ~1h30/2h exercises + summary/discussion. \nThe schedule can be slightly modified according to the interest of the participants and to accommodate different timezones. \n \n \n \n \n \n Assumed quantitative knowledge\n We will assume that you are familiar with basic statistical concepts\, linear models\, and statistical tests (the equivalent of an undergraduate introductory statistics course will be sufficient to follow the course). \n \n \n \n \n \n Assumed computer background\n To take full advantage of this course\, minimal prior experience with R is required. Participants should be familiar with basic R syntax and commands\, know how to write code in the RStudio console and script editor\, load data from files (txt\, xls\, csv). \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Monday 15th\n Day 1 – Classes from 08:00 – 13:00 and 14:00 – 16:00 \n• Module 1: Introduction to community data analysis\, basics of programming in R• Module 2: Diversity analysis\, species-abundance distributions \n \n \n \n \n \n Tuesday 16th\n Day 2 – Classes from 08:00 – 13:00 and 14:00 – 16:00 \n• Module 3: Distance and transformation measures• Module 4: Clustering and classification analysis \n \n \n \n \n \n Wednesday 17th\n Day 3 – Classes from 08:00 – 13:00 and 14:00 – 16:00 \n• Module 5: Unconstrained ordinations: Principal Component Analysis• Module 6: Other unconstrained ordinations \n \n \n \n \n \n Thursday 18th\n Day 4 – Classes from 08:00 – 13:00 and 14:00 – 16:00 \n• Module 7: Constrained ordinations: RDA and other canonical analysis• Module 8: Statistical tests for multivariate data and variation partitioning \n \n \n \n \n \n Friday 19th\n Day 5 – Classes from 08:00 – 13:00 and 14:00 – 16:00 \n• Module 9: Overview of Spatial analysis\, and recent Hierarchical Modeling of Species Communities (HMSC) methods• Modules 10: Special topics and discussion\, analyzing participants’ data. \n \n \n \n \n \n \n \n \n Antoine Becker-Scarpitta\n \n Antoine is a community ecologist and forest ecologist working as a researcher at The French agricultural research and international cooperation organization\, working for the sustainable development of tropical and Mediterranean regions. Antoine was a postdoctoral researcher at the University of Helsinki and the Institute of Botany of the Academy of the Czech Republic. He holds a degree in Conservation Biology from the University of Paris-Sud-Orsay\, and he obtained his PhD in Biology/Ecology from the University of Sherbrooke (Canada). Antoine’s research focuses on the temporal dynamics of biodiversity\, particularly on the forest and Arctic vegetation. Antoine has taught community ecology\, plant ecology and evolution\, linear and multivariate statistics assisted on R. \nResearchGate \nGoogle Scholar \nORCID \nGitHub URL:https://prstats.org/course/multivariate-analysis-of-ecological-communities-using-vegan-vgnrpr/ LOCATION:Recorded\, United Kingdom CATEGORIES:Miscellaneous Ecology,Previously Recorded Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2021/12/VGNR08-1.jpg GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20350826 DTEND;VALUE=DATE:20350831 DTSTAMP:20260420T031746 CREATED:20250904T134550Z LAST-MODIFIED:20251203T092217Z UID:10000533-2071699200-2072131199@prstats.org SUMMARY:Stable Isotope Mixing Models Using SIBER\, SIAR\, MixSIAR DESCRIPTION:Model Selection and Model Simplification (MSMS06)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, September 15\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – Reunion (GMT+4) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \n \n \n \n \n \n \n \n \n \n \n About This Course\n This 5-day course covers R concepts\, methods\, and tools that can be used to analyse community ecology data using (but not limited to) the R package VEGAN. The course will review data processing techniques relevant to multivariate data sets. We will cover diversity indices\, distance measures and distance-based multivariate methods\, clustering\, classification and ordination techniques using the R package VEGAN. We will use real-world empirical data sets to motivate analyses\, such as describing patterns along gradients of environ-mental or anthropogenic disturbances\, quantifying the effects of continuous and discrete predictors. We will emphasise visualisation and reproducible workflows as well as good programming practices. The modules will consist of introductory lectures\, guided computer coding\, and participant exercises. The course is intended for intermediate users of R who are interested in community ecology\, particularly in the areas of terrestrial and wetland ecology\, microbial ecology\, and natural resource management. You are strongly encouraged to use your own data sets (they should be clean and already structured\, see the document: “recommendation if you participate with your data”. \nWe will cover the following:\n\n\nFundamentals of community ecology\,\nDiversity indices\,\nMethods to transform data and calculate distance measures\,\nClassifications (i.e.\, clustering methods) organise the data into synthetic groups and present them in a tree (dendrogram).\nOrdinations (i.e.\, unconstrained methods) reveal the multivariate dimension in only a few dimensions (axes).\nCanonical ordinations (i.e.\, constrained methods) test hypotheses related to multivariate patterns.\n\n\n\nIn addition the course provides lectures and practices on how to create reproducible workflows and use good programming practices in R.\n \nTopics covered during the course include: terrestrial and wetland ecology\, microbial ecology\, and natural resource management\, evolution\, palaeoecology.\n\n\n \nDuring the workshops you will follow guided computer coding exercises using either your own data or real empirical datasets to motivate analyses. Exercises include describing patterns along gradients of environmental or anthropogenic disturbance\, quantifying the effects of continuous and discrete predictors.\n \nYou are strongly encouraged to use your own datasets (they should be clean and already structured\, please contact use if you plan to do this\, we will help you to prepare the data). You will benefit from full support in applying multivariate methods to your dataset (defining of the research question\, transforming your data\, selecting the most appropriate method\, carrying out the analysis and interpreting the results).\n\n\n\n \n \n \n \n \n Intended Audiences\n Any researchers (PhD and MSc students\, post-docs\, primary investigators) and environmental professionals who are interested in implementing best practices and state-of-the-art methods for modelling species’ distributions or ecological niches\, with applications to biogeography\, spatial ecology\, biodiversity conservation and related disciplines. \n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time Zone – Reunion Island (GMT+4) local time \nAvailability – 20 places \nDuration – 5 days\, 8 hours a day \nContact hours – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English (with the option to discuss individually in French) \n \n \n \n \n \n Teaching Format\n The course will be divided into theoretical lectures to introduce and explain key concepts and theories\, and practices with workshop sessions on R. We will cover roughly 2 modules per day\, each module consists of ~1h30/2h lecture + coding\, break\, ~1h30/2h exercises + summary/discussion. \nThe schedule can be slightly modified according to the interest of the participants and to accommodate different timezones. \n \n \n \n \n \n Assumed quantitative knowledge\n We will assume that you are familiar with basic statistical concepts\, linear models\, and statistical tests (the equivalent of an undergraduate introductory statistics course will be sufficient to follow the course). \n \n \n \n \n \n Assumed computer background\n To take full advantage of this course\, minimal prior experience with R is required. Participants should be familiar with basic R syntax and commands\, know how to write code in the RStudio console and script editor\, load data from files (txt\, xls\, csv). \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Monday 15th\n Day 1 – Classes from 08:00 – 13:00 and 14:00 – 16:00 \n• Module 1: Introduction to community data analysis\, basics of programming in R• Module 2: Diversity analysis\, species-abundance distributions \n \n \n \n \n \n Tuesday 16th\n Day 2 – Classes from 08:00 – 13:00 and 14:00 – 16:00 \n• Module 3: Distance and transformation measures• Module 4: Clustering and classification analysis \n \n \n \n \n \n Wednesday 17th\n Day 3 – Classes from 08:00 – 13:00 and 14:00 – 16:00 \n• Module 5: Unconstrained ordinations: Principal Component Analysis• Module 6: Other unconstrained ordinations \n \n \n \n \n \n Thursday 18th\n Day 4 – Classes from 08:00 – 13:00 and 14:00 – 16:00 \n• Module 7: Constrained ordinations: RDA and other canonical analysis• Module 8: Statistical tests for multivariate data and variation partitioning \n \n \n \n \n \n Friday 19th\n Day 5 – Classes from 08:00 – 13:00 and 14:00 – 16:00 \n• Module 9: Overview of Spatial analysis\, and recent Hierarchical Modeling of Species Communities (HMSC) methods• Modules 10: Special topics and discussion\, analyzing participants’ data. \n \n \n \n \n \n \n \n \n Antoine Becker-Scarpitta\n \n Antoine is a community ecologist and forest ecologist working as a researcher at The French agricultural research and international cooperation organization\, working for the sustainable development of tropical and Mediterranean regions. Antoine was a postdoctoral researcher at the University of Helsinki and the Institute of Botany of the Academy of the Czech Republic. He holds a degree in Conservation Biology from the University of Paris-Sud-Orsay\, and he obtained his PhD in Biology/Ecology from the University of Sherbrooke (Canada). Antoine’s research focuses on the temporal dynamics of biodiversity\, particularly on the forest and Arctic vegetation. Antoine has taught community ecology\, plant ecology and evolution\, linear and multivariate statistics assisted on R. \nResearchGate \nGoogle Scholar \nORCID \nGitHub URL:https://prstats.org/course/stable-isotope-mixing-models-using-siber-siar-mixsiar-simmpr/ LOCATION:Recorded\, United Kingdom CATEGORIES:Miscellaneous Ecology,Previously Recorded Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2025/09/SIMMPR-1.jpg GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20350903 DTEND;VALUE=DATE:20350908 DTSTAMP:20260420T031746 CREATED:20250904T140005Z LAST-MODIFIED:20251005T161259Z UID:10000534-2072390400-2072822399@prstats.org SUMMARY:Remote sensing data analysis and coding in R for ecology DESCRIPTION:Model Selection and Model Simplification (MSMS06)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, December 1st\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – Portugal (GMT+1) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you). \n \n \n \n \n \n \n \n \n \n \n About This Course\n Have you built an Ecological Niche Model? If yes\, you have already encountered challenges on data preparation\, or have struggled with issues in models fitting and accuracy. This course will teach you how to overcome these challenges and improve the accuracy of your ecological niche models. By the end of 5-day practical course\, you will have the capacity to filter records and select your variables with variance inflation factor; to test effect of Maxent regularization parameter in models performance; to validate models performance and accuracy; to perform MESS analysis\, null models\, and mechanistic models\, as well as to build your “virtual species”. \nEcological niche\, species distribution\, habitat distribution\, or climatic envelope models are different names for mechanistic and correlative models\, which are empirical or mathematical approaches to the ecological niche of a species. These methods relate different types of ecogeographical variables (environmental\, topographical\, human) to species physiological data or geographical locations\, in order to identify the factors limiting and defining the species' niche. ENMs have become popular because of their efficiency in the design and implementation of conservation management. \nBy the end of 5-day practical course should be able to: \n\nfilter records and select your variables with variance inflation factor;\ntest the effect of Maxent regularization parameter in models performance;\nvalidate models performance and accuracy;\nperform MESS analysis\, null models\, and mechanistic models\, as well as to build your “virtual species”.\n\nStudents will learn to use functions implemented in the packages “usdm”; “dismo”; “ENMEval”; “SDMvspecies”; “spThin”; and “NicheMapper” among others. \n \n \n \n \n \n Intended Audiences\n This course is orientated to PhD and MSc students\, as well as other students and researchers working on biogeography\, spatial ecology\, or related disciplines\, with experience in ecological niche models. \n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Details\n Time Zone – Portugal (GMT+!) local time \nAvailability – 24 places \nDuration – 5 days\, 7 hours a day \nContact hours – Approx. 35 hours \nECT’s – Equal to 3ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n The course will be mainly practical\, with some theoretical lectures. All modelling processes and calculations will be performed with R\, the free software environment for statistical computing and graphics (http://www.r-project.org/). Students will learn to use functions implemented in the packages “usdm”; “dismo”; “ENMEval”; “SDMvspecies”; “spThin”; and “NicheMapper” among others. \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of ecological niche models and biogeography in general is required\, thus we will assume the attendees know how to run an ecological niche model. \n \n \n \n \n \n Assumed computer background\n Solid knowledge in Geographical Information Systems and R statistical package is necessary. It is also essential to have experience in ecological niche models. We will focus exclusively on advanced methods. If you need an introductory course on ecological niche models\, please consider attending our basic course on PRStatistics (www.prstats.org). \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n  \n \n \n \n \n \n \n \n \n \n \n Monday 1st\n Day 1 – Classes from 09:30 to 17:30 \n\nENM guide: how to model\nENM R packages.\nSources of environmental variables using geodata package.\nGetting species records with geodata package.\n\n \n \n \n \n \n Tuesday 2nd\n Day 2 – Classes from 09:30 to 17:30 \n\nVariable selection with variance inflation factor (VIF) and usdm packages.\nChoosing the correct study area.\nFiltering records using usdm/spThin packages.\nChoosing pseudo-absences with Biomod2 package.\n\n \n \n \n \n \n Wednesday 3rd\n Day 3 – Classes from 09:30 to 17:30 \n\nSplit records in training and test with ENMeval package.\nTest effect of Maxent regularization parameter.<.li>\nComparing correlative models with AIC\, with ENMeval package.\n\n \n \n \n \n \n Thursday 4th\n Day 4 – Classes from 09:30 to 17:30 \n\nMESS practice with Biomod2 package.\nValidate models null models.\nVirtualSpecies virtualspecies packages.\n\n \n \n \n \n \n Friday 5th\n Day 5 – Classes from 09:30 to 17:30 \n\nMechanistic model NicheMapper packages.\n\n \n \n \n \n \n \n \n \n Dr. Neftali Sillero\n \n Neftalí Sillero works in the analysis and identification of biodiversity spatial patterns\, from species to populations and individuals. For this\, he uses four powerful tools to better understand how space influence biodiversity: Geographical Information Systems\, Remote Sensing\, Ecological Niche Modelling\, and Spatial Statistics. His main areas of research are: application of new technologies on species’ distributions atlases\, ecological modelling of species’ ranges\, identification of biogeographical regions and species’ chorotypes\, mapping and modelling road-kill hotspots\, and spatial analyses of home ranges. \nHe has more than 10 years’ experience working in ecological niche models. He has authored >70 peer reviewed publications and he is since 2007 Chairman of the Mapping Committee of the Societas Herpetologica Europaea\, where he is the PI of the NA2RE project (www.na2re.ismai.pt)\, the New Atlas of Amphibians and Reptiles of Europe \nPersonal website \nWork Webpage \nResearchGate \nGoogleScholar \n \n \n \n \n \n \n \n \n \n \n \n \n Teaches\n \nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM)\n\n \n \n \n \n \n Teaches\n \nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM)\n\n \n \n \n \n \n \n \n \n Dr. Salvador Arenas-Castro\n \n Dr. Salvador Arenas-Castro is a broad-spectrum ecologist with interesting in differentintegrative perspective of the fundamental ecology\, macroecology and biogeographywith their both application and relationship to climate and land management. He is alsoexploring other research sources in agroecology\, forestry\, spatial ecology\, andecoinformatics\, all addressed by explicitly considering the spatial component ofecological processes\, mainly applying spatially explicit modelling approaches\, GIS andremote sensing techniques. Please check his webpage for further information:https://salvadorarenascastro.wordpress.com \nGoogle Scholar: https://scholar.google.com/citations?user=UAYiB5UAAAAJ&hl=es&oi=aoResearchGate: https://www.researchgate.net/profile/Salvador-Arenas-Castro URL:https://prstats.org/course/remote-sensing-data-analysis-and-coding-in-r-for-ecology-rsdapr/ LOCATION:Recorded\, United Kingdom CATEGORIES:Previously Recorded Courses,Spatial Ecology ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2025/09/RSEDPR.jpg GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20350922 DTEND;VALUE=DATE:20350925 DTSTAMP:20260420T031746 CREATED:20250801T121433Z LAST-MODIFIED:20250805T182627Z UID:10000497-2074032000-2074291199@prstats.org SUMMARY:Introduction to Generalised Linear Mixed Models for Ecologists DESCRIPTION:Model Selection and Model Simplification (MSMS06)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, November 18th\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – UK (GMT) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n \n \n \n \n \n \n \n \n \n \n About this course\n This course is aimed towards researchers analysing field observations\, who are often faced by data heterogeneities due to field sampling protocols changing from one project to another\, or through time over the lifespan of projects\, or trying to combine legacy data sets with new data collected by recording units. \nSuch heterogeneities can bias analyses when data sets are integrated inadequately or can lead to information loss when filtered and standardized to common standards. Accounting for these issues is important for better inference regarding status and trend of species and communities. \nAnalysis of such ‘messy’ data sets need to feel comfortable with manipulating the data\, need a full understanding the mechanics of the models being used (i.e. critically interpreting the results and acknowledging assumptions and limitations)\, and should be able to make informed choices when faced with methodological challenges. \nThe course emphasizes critical thinking and active learning through hands on programming exercises. We will use publicly available data sets to demonstrate the data manipulation and analysis. We will use freely available and open-source R packages. \nThe expected outcome of the course is a solid foundation for further professional development via increased confidence in applying these methods for field observations. \nBy the end of the course\, participants should be able to: \n\nUnderstand basic statistical concepts related to detection error\nWork with field collected data and data from automated recording units (ARU)\nKnow packages such as unmarked\, detect\, bSims\nCritically evaluate modelling options and assumptions using simulations\nFit N-mixture\, distance sampling\, and time-removal models to data\n\n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students working on projects related to avian data\nApplied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a programming language such as R for analysing point count data arising from avian field surveys\n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time Zone – UK (GMT) local time \nAvailability – 25 places \nDuration – 3 days\, 4 hours per day \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT \nLanguage – English \n \n \n \n \n \n Teaching Format\n Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data. \n \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical\, mathematical and physical concepts. Specifically\, generalised linear regression models\, including mixed models; basic knowledge of calculus. \n \n \n \n \n \n Assumed computer background\n Familiarity with R\, ability to import/export data\, manipulate data frames\, fit basic statistical models (up to GLM) and generate simple exploratory and diagnostic plots. \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n \n\n\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Programme\n \n \n \n \n \n \n \n \n \n \n Tuesday 18th\n Day 1 – Classes from 13:30 – 17:30 \nIntroduction \n\nIntroduction and background\nReview of field sampling techniques\nIntroduction to agent-based simulations\nOverview of regression techniques\nNaïve estimates of occupancy and abundance\nMultiple visits and N-mixture models\n\n \n \n \n \n \n Wednesday 19th\n Day 2 – Classes from 13:30 – 17:30 \nIntroduction to modelling \n\nBird behaviour\nTime-removal models\nObservation process\nDistance sampling\nCombining removal and distance sampling (QPAD)\n\n \n \n \n \n \n Thursday 20th\n Day 3 – Classes from 13:30 – 17:30 \nDifferent approaches \n\nSingle visit-based approaches (N-mixture and SQPAD)\nAnalysing data from recording units\nMulti-species models and using species traits and phylogeny\nDealing with roadside and other biases\nClosing remarks\n\n \n \n \n \n \n \n Course Instructor\n \nDr. Peter Solymos \nPéter is an ecologist and R programmer. He has worked with continental scale data sets and developed statistical techniques for estimating population density from messy data sets. He is the author of numerous well-known R packages\, including detect\, dclone\, vegan\, and ResourceSelection. He works currently as a data scientist helping utility companies improving their outage and impact prevention practices\, and is an adjunct professor at the University of Alberta in Edmonton\, Canada. \nGoogle Scholar \nWork Homepage \nPersonal Homepage URL:https://prstats.org/course/introduction-to-generalised-linear-mixed-models-for-ecologists-mmiepr/ LOCATION:Recorded\, United Kingdom CATEGORIES:General Ecology,Previously Recorded Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2025/07/MMIE01-1.jpg GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20350926 DTEND;VALUE=DATE:20350929 DTSTAMP:20260420T031746 CREATED:20250802T183513Z LAST-MODIFIED:20250828T100139Z UID:10000503-2074377600-2074636799@prstats.org SUMMARY:Species Distribution Modelling (SDMs) and Ecological Niche Modelling (ENMs) DESCRIPTION:Model Selection and Model Simplification (MSMS06)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, November 18th\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – UK (GMT) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n \n \n \n \n \n \n \n \n \n \n About this course\n This course is aimed towards researchers analysing field observations\, who are often faced by data heterogeneities due to field sampling protocols changing from one project to another\, or through time over the lifespan of projects\, or trying to combine legacy data sets with new data collected by recording units. \nSuch heterogeneities can bias analyses when data sets are integrated inadequately or can lead to information loss when filtered and standardized to common standards. Accounting for these issues is important for better inference regarding status and trend of species and communities. \nAnalysis of such ‘messy’ data sets need to feel comfortable with manipulating the data\, need a full understanding the mechanics of the models being used (i.e. critically interpreting the results and acknowledging assumptions and limitations)\, and should be able to make informed choices when faced with methodological challenges. \nThe course emphasizes critical thinking and active learning through hands on programming exercises. We will use publicly available data sets to demonstrate the data manipulation and analysis. We will use freely available and open-source R packages. \nThe expected outcome of the course is a solid foundation for further professional development via increased confidence in applying these methods for field observations. \nBy the end of the course\, participants should be able to: \n\nUnderstand basic statistical concepts related to detection error\nWork with field collected data and data from automated recording units (ARU)\nKnow packages such as unmarked\, detect\, bSims\nCritically evaluate modelling options and assumptions using simulations\nFit N-mixture\, distance sampling\, and time-removal models to data\n\n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students working on projects related to avian data\nApplied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a programming language such as R for analysing point count data arising from avian field surveys\n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time Zone – UK (GMT) local time \nAvailability – 25 places \nDuration – 3 days\, 4 hours per day \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT \nLanguage – English \n \n \n \n \n \n Teaching Format\n Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data. \n \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical\, mathematical and physical concepts. Specifically\, generalised linear regression models\, including mixed models; basic knowledge of calculus. \n \n \n \n \n \n Assumed computer background\n Familiarity with R\, ability to import/export data\, manipulate data frames\, fit basic statistical models (up to GLM) and generate simple exploratory and diagnostic plots. \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n \n\n\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Programme\n \n \n \n \n \n \n \n \n \n \n Tuesday 18th\n Day 1 – Classes from 13:30 – 17:30 \nIntroduction \n\nIntroduction and background\nReview of field sampling techniques\nIntroduction to agent-based simulations\nOverview of regression techniques\nNaïve estimates of occupancy and abundance\nMultiple visits and N-mixture models\n\n \n \n \n \n \n Wednesday 19th\n Day 2 – Classes from 13:30 – 17:30 \nIntroduction to modelling \n\nBird behaviour\nTime-removal models\nObservation process\nDistance sampling\nCombining removal and distance sampling (QPAD)\n\n \n \n \n \n \n Thursday 20th\n Day 3 – Classes from 13:30 – 17:30 \nDifferent approaches \n\nSingle visit-based approaches (N-mixture and SQPAD)\nAnalysing data from recording units\nMulti-species models and using species traits and phylogeny\nDealing with roadside and other biases\nClosing remarks\n\n \n \n \n \n \n \n Course Instructor\n \nDr. Peter Solymos \nPéter is an ecologist and R programmer. He has worked with continental scale data sets and developed statistical techniques for estimating population density from messy data sets. He is the author of numerous well-known R packages\, including detect\, dclone\, vegan\, and ResourceSelection. He works currently as a data scientist helping utility companies improving their outage and impact prevention practices\, and is an adjunct professor at the University of Alberta in Edmonton\, Canada. \nGoogle Scholar \nWork Homepage \nPersonal Homepage URL:https://prstats.org/course/species-distribution-modelling-and-ecological-niche-modelling-sdmrpr/ LOCATION:Recorded\, United Kingdom CATEGORIES:Previously Recorded Courses,Spatial Ecology ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2022/03/SDMR06-1.jpg GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20351002 DTEND;VALUE=DATE:20351005 DTSTAMP:20260420T031746 CREATED:20250827T165923Z LAST-MODIFIED:20251010T185617Z UID:10000508-2074896000-2075155199@prstats.org SUMMARY:Movement Ecology (the Analysis of Movement Data) DESCRIPTION:Model Selection and Model Simplification (MSMS06)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, November 18th\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – UK (GMT) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n \n \n \n \n \n \n \n \n \n \n About this course\n This course is aimed towards researchers analysing field observations\, who are often faced by data heterogeneities due to field sampling protocols changing from one project to another\, or through time over the lifespan of projects\, or trying to combine legacy data sets with new data collected by recording units. \nSuch heterogeneities can bias analyses when data sets are integrated inadequately or can lead to information loss when filtered and standardized to common standards. Accounting for these issues is important for better inference regarding status and trend of species and communities. \nAnalysis of such ‘messy’ data sets need to feel comfortable with manipulating the data\, need a full understanding the mechanics of the models being used (i.e. critically interpreting the results and acknowledging assumptions and limitations)\, and should be able to make informed choices when faced with methodological challenges. \nThe course emphasizes critical thinking and active learning through hands on programming exercises. We will use publicly available data sets to demonstrate the data manipulation and analysis. We will use freely available and open-source R packages. \nThe expected outcome of the course is a solid foundation for further professional development via increased confidence in applying these methods for field observations. \nBy the end of the course\, participants should be able to: \n\nUnderstand basic statistical concepts related to detection error\nWork with field collected data and data from automated recording units (ARU)\nKnow packages such as unmarked\, detect\, bSims\nCritically evaluate modelling options and assumptions using simulations\nFit N-mixture\, distance sampling\, and time-removal models to data\n\n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students working on projects related to avian data\nApplied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a programming language such as R for analysing point count data arising from avian field surveys\n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time Zone – UK (GMT) local time \nAvailability – 25 places \nDuration – 3 days\, 4 hours per day \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT \nLanguage – English \n \n \n \n \n \n Teaching Format\n Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data. \n \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical\, mathematical and physical concepts. Specifically\, generalised linear regression models\, including mixed models; basic knowledge of calculus. \n \n \n \n \n \n Assumed computer background\n Familiarity with R\, ability to import/export data\, manipulate data frames\, fit basic statistical models (up to GLM) and generate simple exploratory and diagnostic plots. \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n \n\n\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Programme\n \n \n \n \n \n \n \n \n \n \n Tuesday 18th\n Day 1 – Classes from 13:30 – 17:30 \nIntroduction \n\nIntroduction and background\nReview of field sampling techniques\nIntroduction to agent-based simulations\nOverview of regression techniques\nNaïve estimates of occupancy and abundance\nMultiple visits and N-mixture models\n\n \n \n \n \n \n Wednesday 19th\n Day 2 – Classes from 13:30 – 17:30 \nIntroduction to modelling \n\nBird behaviour\nTime-removal models\nObservation process\nDistance sampling\nCombining removal and distance sampling (QPAD)\n\n \n \n \n \n \n Thursday 20th\n Day 3 – Classes from 13:30 – 17:30 \nDifferent approaches \n\nSingle visit-based approaches (N-mixture and SQPAD)\nAnalysing data from recording units\nMulti-species models and using species traits and phylogeny\nDealing with roadside and other biases\nClosing remarks\n\n \n \n \n \n \n \n Course Instructor\n \nDr. Peter Solymos \nPéter is an ecologist and R programmer. He has worked with continental scale data sets and developed statistical techniques for estimating population density from messy data sets. He is the author of numerous well-known R packages\, including detect\, dclone\, vegan\, and ResourceSelection. He works currently as a data scientist helping utility companies improving their outage and impact prevention practices\, and is an adjunct professor at the University of Alberta in Edmonton\, Canada. \nGoogle Scholar \nWork Homepage \nPersonal Homepage URL:https://prstats.org/course/movement-ecology-the-analysis-of-movement-data-movepr/ LOCATION:Recorded\, United Kingdom CATEGORIES:Previously Recorded Courses,Spatial Ecology ATTACH;FMTTYPE=image/png:https://prstats.org/wp-content/uploads/2025/08/MOVEPR.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20351004 DTEND;VALUE=DATE:20351007 DTSTAMP:20260420T031746 CREATED:20250828T101908Z LAST-MODIFIED:20250828T131820Z UID:10000518-2075068800-2075327999@prstats.org SUMMARY:Hidden Markov Models for Movement\, Acceleration and other ecological Data (an introduction using moveHMM and momentuHMM) DESCRIPTION:Model Selection and Model Simplification (MSMS06)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, November 18th\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – UK (GMT) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n \n \n \n \n \n \n \n \n \n \n About this course\n This course is aimed towards researchers analysing field observations\, who are often faced by data heterogeneities due to field sampling protocols changing from one project to another\, or through time over the lifespan of projects\, or trying to combine legacy data sets with new data collected by recording units. \nSuch heterogeneities can bias analyses when data sets are integrated inadequately or can lead to information loss when filtered and standardized to common standards. Accounting for these issues is important for better inference regarding status and trend of species and communities. \nAnalysis of such ‘messy’ data sets need to feel comfortable with manipulating the data\, need a full understanding the mechanics of the models being used (i.e. critically interpreting the results and acknowledging assumptions and limitations)\, and should be able to make informed choices when faced with methodological challenges. \nThe course emphasizes critical thinking and active learning through hands on programming exercises. We will use publicly available data sets to demonstrate the data manipulation and analysis. We will use freely available and open-source R packages. \nThe expected outcome of the course is a solid foundation for further professional development via increased confidence in applying these methods for field observations. \nBy the end of the course\, participants should be able to: \n\nUnderstand basic statistical concepts related to detection error\nWork with field collected data and data from automated recording units (ARU)\nKnow packages such as unmarked\, detect\, bSims\nCritically evaluate modelling options and assumptions using simulations\nFit N-mixture\, distance sampling\, and time-removal models to data\n\n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students working on projects related to avian data\nApplied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a programming language such as R for analysing point count data arising from avian field surveys\n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time Zone – UK (GMT) local time \nAvailability – 25 places \nDuration – 3 days\, 4 hours per day \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT \nLanguage – English \n \n \n \n \n \n Teaching Format\n Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data. \n \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical\, mathematical and physical concepts. Specifically\, generalised linear regression models\, including mixed models; basic knowledge of calculus. \n \n \n \n \n \n Assumed computer background\n Familiarity with R\, ability to import/export data\, manipulate data frames\, fit basic statistical models (up to GLM) and generate simple exploratory and diagnostic plots. \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n \n\n\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Programme\n \n \n \n \n \n \n \n \n \n \n Tuesday 18th\n Day 1 – Classes from 13:30 – 17:30 \nIntroduction \n\nIntroduction and background\nReview of field sampling techniques\nIntroduction to agent-based simulations\nOverview of regression techniques\nNaïve estimates of occupancy and abundance\nMultiple visits and N-mixture models\n\n \n \n \n \n \n Wednesday 19th\n Day 2 – Classes from 13:30 – 17:30 \nIntroduction to modelling \n\nBird behaviour\nTime-removal models\nObservation process\nDistance sampling\nCombining removal and distance sampling (QPAD)\n\n \n \n \n \n \n Thursday 20th\n Day 3 – Classes from 13:30 – 17:30 \nDifferent approaches \n\nSingle visit-based approaches (N-mixture and SQPAD)\nAnalysing data from recording units\nMulti-species models and using species traits and phylogeny\nDealing with roadside and other biases\nClosing remarks\n\n \n \n \n \n \n \n Course Instructor\n \nDr. Peter Solymos \nPéter is an ecologist and R programmer. He has worked with continental scale data sets and developed statistical techniques for estimating population density from messy data sets. He is the author of numerous well-known R packages\, including detect\, dclone\, vegan\, and ResourceSelection. He works currently as a data scientist helping utility companies improving their outage and impact prevention practices\, and is an adjunct professor at the University of Alberta in Edmonton\, Canada. \nGoogle Scholar \nWork Homepage \nPersonal Homepage URL:https://prstats.org/course/hidden-markov-models-for-movement-acceleration-and-other-ecological-data-an-introduction-using-movehmm-and-momentuhmm-hmmmpr/ LOCATION:Recorded\, United Kingdom CATEGORIES:Previously Recorded Courses,Spatial Ecology ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2025/08/HMMMPR.jpg GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20351018 DTEND;VALUE=DATE:20351029 DTSTAMP:20260420T031746 CREATED:20231213T115657Z LAST-MODIFIED:20250827T100927Z UID:10000364-2076278400-2077228799@prstats.org SUMMARY:Path Analysis\, Structural Equations\, and Causal Inference for Biologists DESCRIPTION:Model Selection and Model Simplification (MSMS06)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, October 20th\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructors will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nCOURSE PROGRAM\nTIME ZONE – Quebec (Canada) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you). \n \n \n \n \n \n \n \n \n \n \n About This Course\n This course\, based primarily on my 2016 book\, teaches you how to use path analysis and structural equations modelling to test causal hypotheses using observational data that is typical of research in ecology and evolution. It is taught in half-day sessions so that you can practice individually after each half-day session. You will learn how to conduct these tests\, why (andwhen) they are justified\, and how to interpret the results. The first few lectures will primarily present the theory but practical sessions will become more prominent later in the course. Thepractical work will be based on R and RStudio. Students will receive R script\, datasets\, and a list of R packages to install. It is highly recommended that each student have a copy of my 2016 book for the course\, but not essential. \n\nBy the end of the course\, participants should be able to: Understand the logical relationships between d-separation\, data\, and causal hypotheses. Know when to use piecewise SEM\, when to use covariance- based SEM\, and the advantages/disadvantages and assumptions of each Be able to construct\, test\, and interpret measurement models involving latent variables Be able to construct and identify equivalent models Be able to incorporate nested or mixed models\, multigroup models\, and non-normal distributions into SEM\n\nParticipants are encouraged to bring their own data\, as there will be opportunities throughout the course to plan\, analyze\, and receive feedback on structural equation models. \n \n \n \n \n \n Intended Audiences\n Scientists generally\, and ecologists specifically\, who want to test hypotheses concerning cause-and-effect relationships involving several variables\, especially involving observational data. \n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time Zone – Quebec (Canada) local time \nAvailability – TBC \nDuration – 9 days\, 4 hours per day \nContact hours – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n This course involves a mixture of theory and practical work. Data and analytical approaches will be presented in a lecture format to explain key concepts. Statistical analyses will then be presented using R. All R script that the instructor uses during these sessions will be shared with participants\, and R script will be presented and explained. \n \n \n \n \n \n Assumed quantative knowledge\n \nExperience in using R and RStudio for statistical analysis.\nA basic understanding of statistical inference and regression methods.\nA familiarity of more advanced regression models (mixed models\, generalized linear models) is an asset but is not essential.\n\n \n \n \n \n \n Assumed computer background\n Proficiency with R programming language\, including: importing/exporting data; manipulating data in the R environment; constructing and evaluating basic statistical models (e.g.\, lm()).\n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \nPLEASE READ – CANCELLATION POLICY \n\n\nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited \n\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Monday 20th\n Day 1 – Classes from 08:30 – 12:30 \nCausal inference using experiments vs. observations (4h) \n\nRandomised experiments are the gold standard\nLimitations on randomised experiments\nThe logic of controlled experiments\nLimitations of controlled experiments\nPhysical control vs. observational control DAGs\, d-separation and data (2h)\\nTranslating from the language of causality to the language of statistics\nDirected acyclic graphs (DAGS) and d-separation\nD-separation and statistical conditioning\nThe difference between experimental control and statistical conditioning\nThe logic of causal inference using d-separation\n\n \n \n \n \n \n Tuesday 21st\n Day 2 – Classes from 08:30 – 12:30 \nPath analysis using piecewise structural equation modelling (1h30) \n\nD-separation basis sets of a DAG\nThe steps in conducting a piecewise SEM\nRejecting or provisionally accepting your path model\nPath coefficients as measures of direct causal effect\nDecomposing causal effects\n\nPractical work (2h) \n \n \n \n \n \n Wednesday 22nd\n Day 3 – Classes from 08:30 – 12:30 \nPath analysis using piecewiseSEM (2h30) \n\nThe piecewiseSEM library in R\n\nPractical work (1h) \n  \n \n \n \n \n \n Thursday 23rd\n Day 4 – Classes from 08:30 – 12:30 \nEquivalent models and AIC statistics (2h) \n\nStatistical power in SEM\nProvisionally accepting a causal hypothesis\nWhat is a “d-separation equivalent” DAG\nRules for identifying equivalent models\nAIC statistic to compare between non-equivalent models\nHow to interpret AIC statistics\n\nPractical work (1h30) \n \n \n \n \n \n Friday 24th\n Day 5 – Classes from 08:30 – 12:30 \nCovariance-based path analysis (2h) \n\nTranslating the DAG into “structural equations”\nThe model-predicted covariance matrix\nAn intuitive explanation of maximum likelihood estimating\nEstimating the free parameters via ML\nThe concept of “degrees of freedom”\nThe ML chi-squared statistic of model fit\nRejecting (or not) your SE model\n\nCovariance-based path analysis using lavaan (1h30) \n \n \n \n \n \n Monday 27th\n Day 6 – Classes from 08:30 – 12:30 \nLatent variables and measurement models (3h) \n\nRemoving latent variables from a DAG\nDAGs and MAGs\nDAG.to.MAG() function\nWhen you can’t remove a latent: measurement models\nMeasurement models and ML estimation\nFixing the scale of a latent variable\nMeasurement models and minimum degrees of freedom\nMeasurement models in lavaan\nEmpirical example: measuring soil fertility\n\n \n \n \n \n \n Tuesday 28th\n Day 7 – Classes from 08:30 – 12:30 \nPractical using measurement models (1h) \nThe full structural equation model (2h30) \n\nModel identification: structural and empirical\nComposite variables and composite latents\nConsequences and solutions for small sample sizes\nConsequences and solutions for non-normal data\nMeasures of approximate fit\nMissing data\nReporting results in publications\n\n \n \n \n \n \n Wednesday 29th\n Day 8 – Classes from 08:30 – 12:30 \nMultigroup models (2h) \n\nWhat is causal heterogeneity?\nThe concept of nested models\nHow to fit multigroup models in lavaan\n\nPractical: putting everything together (1h30) \n \n \n \n \n \n Thursday 30th\n Day 9 – Classes from 08:30 – 12:30 \nPractical and group presentations of results \n \n \n \n \n \n \n \n \n Bill Shipley\n \n Bill Shipley is an experienced researcher and teacher in plant ecology and statistical ecology.  He has published four scientific monographs and over 170 peer-reviewed papers. URL:https://prstats.org/course/path-analysis-structural-equations-and-causal-inference-for-biologists-pscbpr/ LOCATION:Recorded\, United Kingdom CATEGORIES:Miscellaneous Ecology,Previously Recorded Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2023/12/PCSB03.jpg GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20351022 DTEND;VALUE=DATE:20351024 DTSTAMP:20260420T031746 CREATED:20250828T125825Z LAST-MODIFIED:20250828T130158Z UID:10000521-2076624000-2076796799@prstats.org SUMMARY:Network Analysis for Ecologists DESCRIPTION:Model Selection and Model Simplification (MSMS06)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, December 1st\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – Portugal (GMT+1) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you). \n \n \n \n \n \n \n \n \n \n \n About This Course\n Have you built an Ecological Niche Model? If yes\, you have already encountered challenges on data preparation\, or have struggled with issues in models fitting and accuracy. This course will teach you how to overcome these challenges and improve the accuracy of your ecological niche models. By the end of 5-day practical course\, you will have the capacity to filter records and select your variables with variance inflation factor; to test effect of Maxent regularization parameter in models performance; to validate models performance and accuracy; to perform MESS analysis\, null models\, and mechanistic models\, as well as to build your “virtual species”. \nEcological niche\, species distribution\, habitat distribution\, or climatic envelope models are different names for mechanistic and correlative models\, which are empirical or mathematical approaches to the ecological niche of a species. These methods relate different types of ecogeographical variables (environmental\, topographical\, human) to species physiological data or geographical locations\, in order to identify the factors limiting and defining the species' niche. ENMs have become popular because of their efficiency in the design and implementation of conservation management. \nBy the end of 5-day practical course should be able to: \n\nfilter records and select your variables with variance inflation factor;\ntest the effect of Maxent regularization parameter in models performance;\nvalidate models performance and accuracy;\nperform MESS analysis\, null models\, and mechanistic models\, as well as to build your “virtual species”.\n\nStudents will learn to use functions implemented in the packages “usdm”; “dismo”; “ENMEval”; “SDMvspecies”; “spThin”; and “NicheMapper” among others. \n \n \n \n \n \n Intended Audiences\n This course is orientated to PhD and MSc students\, as well as other students and researchers working on biogeography\, spatial ecology\, or related disciplines\, with experience in ecological niche models. \n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Details\n Time Zone – Portugal (GMT+!) local time \nAvailability – 24 places \nDuration – 5 days\, 7 hours a day \nContact hours – Approx. 35 hours \nECT’s – Equal to 3ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n The course will be mainly practical\, with some theoretical lectures. All modelling processes and calculations will be performed with R\, the free software environment for statistical computing and graphics (http://www.r-project.org/). Students will learn to use functions implemented in the packages “usdm”; “dismo”; “ENMEval”; “SDMvspecies”; “spThin”; and “NicheMapper” among others. \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of ecological niche models and biogeography in general is required\, thus we will assume the attendees know how to run an ecological niche model. \n \n \n \n \n \n Assumed computer background\n Solid knowledge in Geographical Information Systems and R statistical package is necessary. It is also essential to have experience in ecological niche models. We will focus exclusively on advanced methods. If you need an introductory course on ecological niche models\, please consider attending our basic course on PRStatistics (www.prstats.org). \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n  \n \n \n \n \n \n \n \n \n \n \n Monday 1st\n Day 1 – Classes from 09:30 to 17:30 \n\nENM guide: how to model\nENM R packages.\nSources of environmental variables using geodata package.\nGetting species records with geodata package.\n\n \n \n \n \n \n Tuesday 2nd\n Day 2 – Classes from 09:30 to 17:30 \n\nVariable selection with variance inflation factor (VIF) and usdm packages.\nChoosing the correct study area.\nFiltering records using usdm/spThin packages.\nChoosing pseudo-absences with Biomod2 package.\n\n \n \n \n \n \n Wednesday 3rd\n Day 3 – Classes from 09:30 to 17:30 \n\nSplit records in training and test with ENMeval package.\nTest effect of Maxent regularization parameter.<.li>\nComparing correlative models with AIC\, with ENMeval package.\n\n \n \n \n \n \n Thursday 4th\n Day 4 – Classes from 09:30 to 17:30 \n\nMESS practice with Biomod2 package.\nValidate models null models.\nVirtualSpecies virtualspecies packages.\n\n \n \n \n \n \n Friday 5th\n Day 5 – Classes from 09:30 to 17:30 \n\nMechanistic model NicheMapper packages.\n\n \n \n \n \n \n \n \n \n Dr. Neftali Sillero\n \n Neftalí Sillero works in the analysis and identification of biodiversity spatial patterns\, from species to populations and individuals. For this\, he uses four powerful tools to better understand how space influence biodiversity: Geographical Information Systems\, Remote Sensing\, Ecological Niche Modelling\, and Spatial Statistics. His main areas of research are: application of new technologies on species’ distributions atlases\, ecological modelling of species’ ranges\, identification of biogeographical regions and species’ chorotypes\, mapping and modelling road-kill hotspots\, and spatial analyses of home ranges. \nHe has more than 10 years’ experience working in ecological niche models. He has authored >70 peer reviewed publications and he is since 2007 Chairman of the Mapping Committee of the Societas Herpetologica Europaea\, where he is the PI of the NA2RE project (www.na2re.ismai.pt)\, the New Atlas of Amphibians and Reptiles of Europe \nPersonal website \nWork Webpage \nResearchGate \nGoogleScholar \n \n \n \n \n \n \n \n \n \n \n \n \n Teaches\n \nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM)\n\n \n \n \n \n \n Teaches\n \nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM)\n\n \n \n \n \n \n \n \n \n Dr. Salvador Arenas-Castro\n \n Dr. Salvador Arenas-Castro is a broad-spectrum ecologist with interesting in differentintegrative perspective of the fundamental ecology\, macroecology and biogeographywith their both application and relationship to climate and land management. He is alsoexploring other research sources in agroecology\, forestry\, spatial ecology\, andecoinformatics\, all addressed by explicitly considering the spatial component ofecological processes\, mainly applying spatially explicit modelling approaches\, GIS andremote sensing techniques. Please check his webpage for further information:https://salvadorarenascastro.wordpress.com \nGoogle Scholar: https://scholar.google.com/citations?user=UAYiB5UAAAAJ&hl=es&oi=aoResearchGate: https://www.researchgate.net/profile/Salvador-Arenas-Castro URL:https://prstats.org/course/network-analysis-for-ecologists-nwaepr/ LOCATION:Recorded\, United Kingdom CATEGORIES:Miscellaneous Ecology,Previously Recorded Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2025/07/NWAE01-1.jpg GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20360501 DTEND;VALUE=DATE:20360504 DTSTAMP:20260420T031746 CREATED:20250827T190058Z LAST-MODIFIED:20250827T190212Z UID:10000512-2093212800-2093471999@prstats.org SUMMARY:Bayesian Approaches to Regression and Mixed Effects Models Using brms DESCRIPTION:Model Selection and Model Simplification (MSMS06)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, November 18th\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – UK (GMT) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n \n \n \n \n \n \n \n \n \n \n About this course\n This course is aimed towards researchers analysing field observations\, who are often faced by data heterogeneities due to field sampling protocols changing from one project to another\, or through time over the lifespan of projects\, or trying to combine legacy data sets with new data collected by recording units. \nSuch heterogeneities can bias analyses when data sets are integrated inadequately or can lead to information loss when filtered and standardized to common standards. Accounting for these issues is important for better inference regarding status and trend of species and communities. \nAnalysis of such ‘messy’ data sets need to feel comfortable with manipulating the data\, need a full understanding the mechanics of the models being used (i.e. critically interpreting the results and acknowledging assumptions and limitations)\, and should be able to make informed choices when faced with methodological challenges. \nThe course emphasizes critical thinking and active learning through hands on programming exercises. We will use publicly available data sets to demonstrate the data manipulation and analysis. We will use freely available and open-source R packages. \nThe expected outcome of the course is a solid foundation for further professional development via increased confidence in applying these methods for field observations. \nBy the end of the course\, participants should be able to: \n\nUnderstand basic statistical concepts related to detection error\nWork with field collected data and data from automated recording units (ARU)\nKnow packages such as unmarked\, detect\, bSims\nCritically evaluate modelling options and assumptions using simulations\nFit N-mixture\, distance sampling\, and time-removal models to data\n\n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students working on projects related to avian data\nApplied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a programming language such as R for analysing point count data arising from avian field surveys\n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time Zone – UK (GMT) local time \nAvailability – 25 places \nDuration – 3 days\, 4 hours per day \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT \nLanguage – English \n \n \n \n \n \n Teaching Format\n Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data. \n \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical\, mathematical and physical concepts. Specifically\, generalised linear regression models\, including mixed models; basic knowledge of calculus. \n \n \n \n \n \n Assumed computer background\n Familiarity with R\, ability to import/export data\, manipulate data frames\, fit basic statistical models (up to GLM) and generate simple exploratory and diagnostic plots. \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n \n\n\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Programme\n \n \n \n \n \n \n \n \n \n \n Tuesday 18th\n Day 1 – Classes from 13:30 – 17:30 \nIntroduction \n\nIntroduction and background\nReview of field sampling techniques\nIntroduction to agent-based simulations\nOverview of regression techniques\nNaïve estimates of occupancy and abundance\nMultiple visits and N-mixture models\n\n \n \n \n \n \n Wednesday 19th\n Day 2 – Classes from 13:30 – 17:30 \nIntroduction to modelling \n\nBird behaviour\nTime-removal models\nObservation process\nDistance sampling\nCombining removal and distance sampling (QPAD)\n\n \n \n \n \n \n Thursday 20th\n Day 3 – Classes from 13:30 – 17:30 \nDifferent approaches \n\nSingle visit-based approaches (N-mixture and SQPAD)\nAnalysing data from recording units\nMulti-species models and using species traits and phylogeny\nDealing with roadside and other biases\nClosing remarks\n\n \n \n \n \n \n \n Course Instructor\n \nDr. Peter Solymos \nPéter is an ecologist and R programmer. He has worked with continental scale data sets and developed statistical techniques for estimating population density from messy data sets. He is the author of numerous well-known R packages\, including detect\, dclone\, vegan\, and ResourceSelection. He works currently as a data scientist helping utility companies improving their outage and impact prevention practices\, and is an adjunct professor at the University of Alberta in Edmonton\, Canada. \nGoogle Scholar \nWork Homepage \nPersonal Homepage URL:https://prstats.org/course/bayesian-approaches-to-regression-and-mixed-effects-models-using-brms-barmpr/ LOCATION:Recorded\, United Kingdom CATEGORIES:Bayesian (General),Previously Recorded Courses ATTACH;FMTTYPE=image/png:https://prstats.org/wp-content/uploads/2025/08/BARMPR.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20360502 DTEND;VALUE=DATE:20360505 DTSTAMP:20260420T031747 CREATED:20250827T191302Z LAST-MODIFIED:20250827T191311Z UID:10000514-2093299200-2093558399@prstats.org SUMMARY:Bayesian Data Analysis DESCRIPTION:Model Selection and Model Simplification (MSMS06)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, November 18th\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – UK (GMT) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n \n \n \n \n \n \n \n \n \n \n About this course\n This course is aimed towards researchers analysing field observations\, who are often faced by data heterogeneities due to field sampling protocols changing from one project to another\, or through time over the lifespan of projects\, or trying to combine legacy data sets with new data collected by recording units. \nSuch heterogeneities can bias analyses when data sets are integrated inadequately or can lead to information loss when filtered and standardized to common standards. Accounting for these issues is important for better inference regarding status and trend of species and communities. \nAnalysis of such ‘messy’ data sets need to feel comfortable with manipulating the data\, need a full understanding the mechanics of the models being used (i.e. critically interpreting the results and acknowledging assumptions and limitations)\, and should be able to make informed choices when faced with methodological challenges. \nThe course emphasizes critical thinking and active learning through hands on programming exercises. We will use publicly available data sets to demonstrate the data manipulation and analysis. We will use freely available and open-source R packages. \nThe expected outcome of the course is a solid foundation for further professional development via increased confidence in applying these methods for field observations. \nBy the end of the course\, participants should be able to: \n\nUnderstand basic statistical concepts related to detection error\nWork with field collected data and data from automated recording units (ARU)\nKnow packages such as unmarked\, detect\, bSims\nCritically evaluate modelling options and assumptions using simulations\nFit N-mixture\, distance sampling\, and time-removal models to data\n\n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students working on projects related to avian data\nApplied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a programming language such as R for analysing point count data arising from avian field surveys\n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time Zone – UK (GMT) local time \nAvailability – 25 places \nDuration – 3 days\, 4 hours per day \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT \nLanguage – English \n \n \n \n \n \n Teaching Format\n Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data. \n \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical\, mathematical and physical concepts. Specifically\, generalised linear regression models\, including mixed models; basic knowledge of calculus. \n \n \n \n \n \n Assumed computer background\n Familiarity with R\, ability to import/export data\, manipulate data frames\, fit basic statistical models (up to GLM) and generate simple exploratory and diagnostic plots. \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n \n\n\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Programme\n \n \n \n \n \n \n \n \n \n \n Tuesday 18th\n Day 1 – Classes from 13:30 – 17:30 \nIntroduction \n\nIntroduction and background\nReview of field sampling techniques\nIntroduction to agent-based simulations\nOverview of regression techniques\nNaïve estimates of occupancy and abundance\nMultiple visits and N-mixture models\n\n \n \n \n \n \n Wednesday 19th\n Day 2 – Classes from 13:30 – 17:30 \nIntroduction to modelling \n\nBird behaviour\nTime-removal models\nObservation process\nDistance sampling\nCombining removal and distance sampling (QPAD)\n\n \n \n \n \n \n Thursday 20th\n Day 3 – Classes from 13:30 – 17:30 \nDifferent approaches \n\nSingle visit-based approaches (N-mixture and SQPAD)\nAnalysing data from recording units\nMulti-species models and using species traits and phylogeny\nDealing with roadside and other biases\nClosing remarks\n\n \n \n \n \n \n \n Course Instructor\n \nDr. Peter Solymos \nPéter is an ecologist and R programmer. He has worked with continental scale data sets and developed statistical techniques for estimating population density from messy data sets. He is the author of numerous well-known R packages\, including detect\, dclone\, vegan\, and ResourceSelection. He works currently as a data scientist helping utility companies improving their outage and impact prevention practices\, and is an adjunct professor at the University of Alberta in Edmonton\, Canada. \nGoogle Scholar \nWork Homepage \nPersonal Homepage URL:https://prstats.org/course/bayesian-data-analysis-badapr/ LOCATION:Recorded\, United Kingdom CATEGORIES:Bayesian (General),Previously Recorded Courses ATTACH;FMTTYPE=image/png:https://prstats.org/wp-content/uploads/2025/08/BADAPR.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20360505 DTEND;VALUE=DATE:20360508 DTSTAMP:20260420T031747 CREATED:20250901T074046Z LAST-MODIFIED:20260409T170044Z UID:10000525-2093558400-2093817599@prstats.org SUMMARY:FREE COURSE Introduction to Spatial Data visualisation and Mapping in R (FMAPPR) DESCRIPTION:Model Selection and Model Simplification (MSMS06)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, November 18th\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – UK (GMT) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n \n \n \n \n \n \n \n \n \n \n About this course\n This course is aimed towards researchers analysing field observations\, who are often faced by data heterogeneities due to field sampling protocols changing from one project to another\, or through time over the lifespan of projects\, or trying to combine legacy data sets with new data collected by recording units. \nSuch heterogeneities can bias analyses when data sets are integrated inadequately or can lead to information loss when filtered and standardized to common standards. Accounting for these issues is important for better inference regarding status and trend of species and communities. \nAnalysis of such ‘messy’ data sets need to feel comfortable with manipulating the data\, need a full understanding the mechanics of the models being used (i.e. critically interpreting the results and acknowledging assumptions and limitations)\, and should be able to make informed choices when faced with methodological challenges. \nThe course emphasizes critical thinking and active learning through hands on programming exercises. We will use publicly available data sets to demonstrate the data manipulation and analysis. We will use freely available and open-source R packages. \nThe expected outcome of the course is a solid foundation for further professional development via increased confidence in applying these methods for field observations. \nBy the end of the course\, participants should be able to: \n\nUnderstand basic statistical concepts related to detection error\nWork with field collected data and data from automated recording units (ARU)\nKnow packages such as unmarked\, detect\, bSims\nCritically evaluate modelling options and assumptions using simulations\nFit N-mixture\, distance sampling\, and time-removal models to data\n\n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students working on projects related to avian data\nApplied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a programming language such as R for analysing point count data arising from avian field surveys\n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time Zone – UK (GMT) local time \nAvailability – 25 places \nDuration – 3 days\, 4 hours per day \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT \nLanguage – English \n \n \n \n \n \n Teaching Format\n Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data. \n \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical\, mathematical and physical concepts. Specifically\, generalised linear regression models\, including mixed models; basic knowledge of calculus. \n \n \n \n \n \n Assumed computer background\n Familiarity with R\, ability to import/export data\, manipulate data frames\, fit basic statistical models (up to GLM) and generate simple exploratory and diagnostic plots. \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n \n\n\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Programme\n \n \n \n \n \n \n \n \n \n \n Tuesday 18th\n Day 1 – Classes from 13:30 – 17:30 \nIntroduction \n\nIntroduction and background\nReview of field sampling techniques\nIntroduction to agent-based simulations\nOverview of regression techniques\nNaïve estimates of occupancy and abundance\nMultiple visits and N-mixture models\n\n \n \n \n \n \n Wednesday 19th\n Day 2 – Classes from 13:30 – 17:30 \nIntroduction to modelling \n\nBird behaviour\nTime-removal models\nObservation process\nDistance sampling\nCombining removal and distance sampling (QPAD)\n\n \n \n \n \n \n Thursday 20th\n Day 3 – Classes from 13:30 – 17:30 \nDifferent approaches \n\nSingle visit-based approaches (N-mixture and SQPAD)\nAnalysing data from recording units\nMulti-species models and using species traits and phylogeny\nDealing with roadside and other biases\nClosing remarks\n\n \n \n \n \n \n \n Course Instructor\n \nDr. Peter Solymos \nPéter is an ecologist and R programmer. He has worked with continental scale data sets and developed statistical techniques for estimating population density from messy data sets. He is the author of numerous well-known R packages\, including detect\, dclone\, vegan\, and ResourceSelection. He works currently as a data scientist helping utility companies improving their outage and impact prevention practices\, and is an adjunct professor at the University of Alberta in Edmonton\, Canada. \nGoogle Scholar \nWork Homepage \nPersonal Homepage URL:https://prstats.org/course/free-course-introduction-to-spatial-data-visualisation-and-mapping-in-r-fmappr/ LOCATION:Recorded\, United Kingdom CATEGORIES:Free Courses,Previously Recorded Courses ATTACH;FMTTYPE=image/png:https://prstats.org/wp-content/uploads/2025/08/FMAP01.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20360509 DTEND;VALUE=DATE:20360512 DTSTAMP:20260420T031747 CREATED:20250827T182546Z LAST-MODIFIED:20250827T183030Z UID:10000509-2093904000-2094163199@prstats.org SUMMARY:Making Beautiful and Effective Maps in R DESCRIPTION:Model Selection and Model Simplification (MSMS06)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, November 18th\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – UK (GMT) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n \n \n \n \n \n \n \n \n \n \n About this course\n This course is aimed towards researchers analysing field observations\, who are often faced by data heterogeneities due to field sampling protocols changing from one project to another\, or through time over the lifespan of projects\, or trying to combine legacy data sets with new data collected by recording units. \nSuch heterogeneities can bias analyses when data sets are integrated inadequately or can lead to information loss when filtered and standardized to common standards. Accounting for these issues is important for better inference regarding status and trend of species and communities. \nAnalysis of such ‘messy’ data sets need to feel comfortable with manipulating the data\, need a full understanding the mechanics of the models being used (i.e. critically interpreting the results and acknowledging assumptions and limitations)\, and should be able to make informed choices when faced with methodological challenges. \nThe course emphasizes critical thinking and active learning through hands on programming exercises. We will use publicly available data sets to demonstrate the data manipulation and analysis. We will use freely available and open-source R packages. \nThe expected outcome of the course is a solid foundation for further professional development via increased confidence in applying these methods for field observations. \nBy the end of the course\, participants should be able to: \n\nUnderstand basic statistical concepts related to detection error\nWork with field collected data and data from automated recording units (ARU)\nKnow packages such as unmarked\, detect\, bSims\nCritically evaluate modelling options and assumptions using simulations\nFit N-mixture\, distance sampling\, and time-removal models to data\n\n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students working on projects related to avian data\nApplied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a programming language such as R for analysing point count data arising from avian field surveys\n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time Zone – UK (GMT) local time \nAvailability – 25 places \nDuration – 3 days\, 4 hours per day \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT \nLanguage – English \n \n \n \n \n \n Teaching Format\n Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data. \n \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical\, mathematical and physical concepts. Specifically\, generalised linear regression models\, including mixed models; basic knowledge of calculus. \n \n \n \n \n \n Assumed computer background\n Familiarity with R\, ability to import/export data\, manipulate data frames\, fit basic statistical models (up to GLM) and generate simple exploratory and diagnostic plots. \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n \n\n\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Programme\n \n \n \n \n \n \n \n \n \n \n Tuesday 18th\n Day 1 – Classes from 13:30 – 17:30 \nIntroduction \n\nIntroduction and background\nReview of field sampling techniques\nIntroduction to agent-based simulations\nOverview of regression techniques\nNaïve estimates of occupancy and abundance\nMultiple visits and N-mixture models\n\n \n \n \n \n \n Wednesday 19th\n Day 2 – Classes from 13:30 – 17:30 \nIntroduction to modelling \n\nBird behaviour\nTime-removal models\nObservation process\nDistance sampling\nCombining removal and distance sampling (QPAD)\n\n \n \n \n \n \n Thursday 20th\n Day 3 – Classes from 13:30 – 17:30 \nDifferent approaches \n\nSingle visit-based approaches (N-mixture and SQPAD)\nAnalysing data from recording units\nMulti-species models and using species traits and phylogeny\nDealing with roadside and other biases\nClosing remarks\n\n \n \n \n \n \n \n Course Instructor\n \nDr. Peter Solymos \nPéter is an ecologist and R programmer. He has worked with continental scale data sets and developed statistical techniques for estimating population density from messy data sets. He is the author of numerous well-known R packages\, including detect\, dclone\, vegan\, and ResourceSelection. He works currently as a data scientist helping utility companies improving their outage and impact prevention practices\, and is an adjunct professor at the University of Alberta in Edmonton\, Canada. \nGoogle Scholar \nWork Homepage \nPersonal Homepage URL:https://prstats.org/course/making-beautiful-and-effective-maps-in-r-maprpr/ LOCATION:Recorded\, United Kingdom CATEGORIES:Previously Recorded Courses,Spatial (General) ATTACH;FMTTYPE=image/png:https://prstats.org/wp-content/uploads/2025/08/MAPRPR.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20360523 DTEND;VALUE=DATE:20360526 DTSTAMP:20260420T031747 CREATED:20250801T161346Z LAST-MODIFIED:20250801T161540Z UID:10000498-2095113600-2095372799@prstats.org SUMMARY:Introduction to Bayesian Hierarchical Modelling DESCRIPTION:Model Selection and Model Simplification (MSMS06)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, November 18th\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – UK (GMT) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n \n \n \n \n \n \n \n \n \n \n About this course\n This course is aimed towards researchers analysing field observations\, who are often faced by data heterogeneities due to field sampling protocols changing from one project to another\, or through time over the lifespan of projects\, or trying to combine legacy data sets with new data collected by recording units. \nSuch heterogeneities can bias analyses when data sets are integrated inadequately or can lead to information loss when filtered and standardized to common standards. Accounting for these issues is important for better inference regarding status and trend of species and communities. \nAnalysis of such ‘messy’ data sets need to feel comfortable with manipulating the data\, need a full understanding the mechanics of the models being used (i.e. critically interpreting the results and acknowledging assumptions and limitations)\, and should be able to make informed choices when faced with methodological challenges. \nThe course emphasizes critical thinking and active learning through hands on programming exercises. We will use publicly available data sets to demonstrate the data manipulation and analysis. We will use freely available and open-source R packages. \nThe expected outcome of the course is a solid foundation for further professional development via increased confidence in applying these methods for field observations. \nBy the end of the course\, participants should be able to: \n\nUnderstand basic statistical concepts related to detection error\nWork with field collected data and data from automated recording units (ARU)\nKnow packages such as unmarked\, detect\, bSims\nCritically evaluate modelling options and assumptions using simulations\nFit N-mixture\, distance sampling\, and time-removal models to data\n\n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students working on projects related to avian data\nApplied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a programming language such as R for analysing point count data arising from avian field surveys\n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time Zone – UK (GMT) local time \nAvailability – 25 places \nDuration – 3 days\, 4 hours per day \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT \nLanguage – English \n \n \n \n \n \n Teaching Format\n Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data. \n \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical\, mathematical and physical concepts. Specifically\, generalised linear regression models\, including mixed models; basic knowledge of calculus. \n \n \n \n \n \n Assumed computer background\n Familiarity with R\, ability to import/export data\, manipulate data frames\, fit basic statistical models (up to GLM) and generate simple exploratory and diagnostic plots. \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n \n\n\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Programme\n \n \n \n \n \n \n \n \n \n \n Tuesday 18th\n Day 1 – Classes from 13:30 – 17:30 \nIntroduction \n\nIntroduction and background\nReview of field sampling techniques\nIntroduction to agent-based simulations\nOverview of regression techniques\nNaïve estimates of occupancy and abundance\nMultiple visits and N-mixture models\n\n \n \n \n \n \n Wednesday 19th\n Day 2 – Classes from 13:30 – 17:30 \nIntroduction to modelling \n\nBird behaviour\nTime-removal models\nObservation process\nDistance sampling\nCombining removal and distance sampling (QPAD)\n\n \n \n \n \n \n Thursday 20th\n Day 3 – Classes from 13:30 – 17:30 \nDifferent approaches \n\nSingle visit-based approaches (N-mixture and SQPAD)\nAnalysing data from recording units\nMulti-species models and using species traits and phylogeny\nDealing with roadside and other biases\nClosing remarks\n\n \n \n \n \n \n \n Course Instructor\n \nDr. Peter Solymos \nPéter is an ecologist and R programmer. He has worked with continental scale data sets and developed statistical techniques for estimating population density from messy data sets. He is the author of numerous well-known R packages\, including detect\, dclone\, vegan\, and ResourceSelection. He works currently as a data scientist helping utility companies improving their outage and impact prevention practices\, and is an adjunct professor at the University of Alberta in Edmonton\, Canada. \nGoogle Scholar \nWork Homepage \nPersonal Homepage URL:https://prstats.org/course/introduction-to-bayesian-hierarchical-modelling-ibhmpr/ LOCATION:Recorded\, United Kingdom CATEGORIES:Bayesian (General),Previously Recorded Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2025/08/IBHMPR.jpg GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20360523 DTEND;VALUE=DATE:20360526 DTSTAMP:20260420T031747 CREATED:20250801T162516Z LAST-MODIFIED:20250830T134115Z UID:10000499-2095113600-2095372799@prstats.org SUMMARY:Spatial Data Visualisation and Mapping using TMAP DESCRIPTION:Model Selection and Model Simplification (MSMS06)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, November 18th\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – UK (GMT) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n \n \n \n \n \n \n \n \n \n \n About this course\n This course is aimed towards researchers analysing field observations\, who are often faced by data heterogeneities due to field sampling protocols changing from one project to another\, or through time over the lifespan of projects\, or trying to combine legacy data sets with new data collected by recording units. \nSuch heterogeneities can bias analyses when data sets are integrated inadequately or can lead to information loss when filtered and standardized to common standards. Accounting for these issues is important for better inference regarding status and trend of species and communities. \nAnalysis of such ‘messy’ data sets need to feel comfortable with manipulating the data\, need a full understanding the mechanics of the models being used (i.e. critically interpreting the results and acknowledging assumptions and limitations)\, and should be able to make informed choices when faced with methodological challenges. \nThe course emphasizes critical thinking and active learning through hands on programming exercises. We will use publicly available data sets to demonstrate the data manipulation and analysis. We will use freely available and open-source R packages. \nThe expected outcome of the course is a solid foundation for further professional development via increased confidence in applying these methods for field observations. \nBy the end of the course\, participants should be able to: \n\nUnderstand basic statistical concepts related to detection error\nWork with field collected data and data from automated recording units (ARU)\nKnow packages such as unmarked\, detect\, bSims\nCritically evaluate modelling options and assumptions using simulations\nFit N-mixture\, distance sampling\, and time-removal models to data\n\n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students working on projects related to avian data\nApplied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a programming language such as R for analysing point count data arising from avian field surveys\n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time Zone – UK (GMT) local time \nAvailability – 25 places \nDuration – 3 days\, 4 hours per day \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT \nLanguage – English \n \n \n \n \n \n Teaching Format\n Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data. \n \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical\, mathematical and physical concepts. Specifically\, generalised linear regression models\, including mixed models; basic knowledge of calculus. \n \n \n \n \n \n Assumed computer background\n Familiarity with R\, ability to import/export data\, manipulate data frames\, fit basic statistical models (up to GLM) and generate simple exploratory and diagnostic plots. \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n \n\n\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Programme\n \n \n \n \n \n \n \n \n \n \n Tuesday 18th\n Day 1 – Classes from 13:30 – 17:30 \nIntroduction \n\nIntroduction and background\nReview of field sampling techniques\nIntroduction to agent-based simulations\nOverview of regression techniques\nNaïve estimates of occupancy and abundance\nMultiple visits and N-mixture models\n\n \n \n \n \n \n Wednesday 19th\n Day 2 – Classes from 13:30 – 17:30 \nIntroduction to modelling \n\nBird behaviour\nTime-removal models\nObservation process\nDistance sampling\nCombining removal and distance sampling (QPAD)\n\n \n \n \n \n \n Thursday 20th\n Day 3 – Classes from 13:30 – 17:30 \nDifferent approaches \n\nSingle visit-based approaches (N-mixture and SQPAD)\nAnalysing data from recording units\nMulti-species models and using species traits and phylogeny\nDealing with roadside and other biases\nClosing remarks\n\n \n \n \n \n \n \n Course Instructor\n \nDr. Peter Solymos \nPéter is an ecologist and R programmer. He has worked with continental scale data sets and developed statistical techniques for estimating population density from messy data sets. He is the author of numerous well-known R packages\, including detect\, dclone\, vegan\, and ResourceSelection. He works currently as a data scientist helping utility companies improving their outage and impact prevention practices\, and is an adjunct professor at the University of Alberta in Edmonton\, Canada. \nGoogle Scholar \nWork Homepage \nPersonal Homepage URL:https://prstats.org/course/spatial-data-visualisation-and-mapping-using-tmap-tmappr/ LOCATION:Recorded\, United Kingdom CATEGORIES:Previously Recorded Courses,Spatial (General) ATTACH;FMTTYPE=image/png:https://prstats.org/wp-content/uploads/2025/07/TMAP02.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20360523 DTEND;VALUE=DATE:20360526 DTSTAMP:20260420T031747 CREATED:20250827T192255Z LAST-MODIFIED:20250827T194246Z UID:10000515-2095113600-2095372799@prstats.org SUMMARY:Introduction to Stan for Bayesian Data Analysis DESCRIPTION:Model Selection and Model Simplification (MSMS06)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, November 18th\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – UK (GMT) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n \n \n \n \n \n \n \n \n \n \n About this course\n This course is aimed towards researchers analysing field observations\, who are often faced by data heterogeneities due to field sampling protocols changing from one project to another\, or through time over the lifespan of projects\, or trying to combine legacy data sets with new data collected by recording units. \nSuch heterogeneities can bias analyses when data sets are integrated inadequately or can lead to information loss when filtered and standardized to common standards. Accounting for these issues is important for better inference regarding status and trend of species and communities. \nAnalysis of such ‘messy’ data sets need to feel comfortable with manipulating the data\, need a full understanding the mechanics of the models being used (i.e. critically interpreting the results and acknowledging assumptions and limitations)\, and should be able to make informed choices when faced with methodological challenges. \nThe course emphasizes critical thinking and active learning through hands on programming exercises. We will use publicly available data sets to demonstrate the data manipulation and analysis. We will use freely available and open-source R packages. \nThe expected outcome of the course is a solid foundation for further professional development via increased confidence in applying these methods for field observations. \nBy the end of the course\, participants should be able to: \n\nUnderstand basic statistical concepts related to detection error\nWork with field collected data and data from automated recording units (ARU)\nKnow packages such as unmarked\, detect\, bSims\nCritically evaluate modelling options and assumptions using simulations\nFit N-mixture\, distance sampling\, and time-removal models to data\n\n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students working on projects related to avian data\nApplied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a programming language such as R for analysing point count data arising from avian field surveys\n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time Zone – UK (GMT) local time \nAvailability – 25 places \nDuration – 3 days\, 4 hours per day \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT \nLanguage – English \n \n \n \n \n \n Teaching Format\n Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data. \n \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical\, mathematical and physical concepts. Specifically\, generalised linear regression models\, including mixed models; basic knowledge of calculus. \n \n \n \n \n \n Assumed computer background\n Familiarity with R\, ability to import/export data\, manipulate data frames\, fit basic statistical models (up to GLM) and generate simple exploratory and diagnostic plots. \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n \n\n\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Programme\n \n \n \n \n \n \n \n \n \n \n Tuesday 18th\n Day 1 – Classes from 13:30 – 17:30 \nIntroduction \n\nIntroduction and background\nReview of field sampling techniques\nIntroduction to agent-based simulations\nOverview of regression techniques\nNaïve estimates of occupancy and abundance\nMultiple visits and N-mixture models\n\n \n \n \n \n \n Wednesday 19th\n Day 2 – Classes from 13:30 – 17:30 \nIntroduction to modelling \n\nBird behaviour\nTime-removal models\nObservation process\nDistance sampling\nCombining removal and distance sampling (QPAD)\n\n \n \n \n \n \n Thursday 20th\n Day 3 – Classes from 13:30 – 17:30 \nDifferent approaches \n\nSingle visit-based approaches (N-mixture and SQPAD)\nAnalysing data from recording units\nMulti-species models and using species traits and phylogeny\nDealing with roadside and other biases\nClosing remarks\n\n \n \n \n \n \n \n Course Instructor\n \nDr. Peter Solymos \nPéter is an ecologist and R programmer. He has worked with continental scale data sets and developed statistical techniques for estimating population density from messy data sets. He is the author of numerous well-known R packages\, including detect\, dclone\, vegan\, and ResourceSelection. He works currently as a data scientist helping utility companies improving their outage and impact prevention practices\, and is an adjunct professor at the University of Alberta in Edmonton\, Canada. \nGoogle Scholar \nWork Homepage \nPersonal Homepage URL:https://prstats.org/course/introduction-to-stan-for-bayesian-data-analysis-isbdpr/ LOCATION:Recorded\, United Kingdom CATEGORIES:Bayesian (General),Previously Recorded Courses ATTACH;FMTTYPE=image/png:https://prstats.org/wp-content/uploads/2025/08/STANPR-1.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20360531 DTEND;VALUE=DATE:20360603 DTSTAMP:20260420T031747 CREATED:20250827T183745Z LAST-MODIFIED:20250904T141429Z UID:10000510-2095804800-2096063999@prstats.org SUMMARY:Adapting to the Recent Changes in R Spatial Packages (sf\, terra\, PROJ library) DESCRIPTION:Model Selection and Model Simplification (MSMS06)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, November 18th\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – UK (GMT) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n \n \n \n \n \n \n \n \n \n \n About this course\n This course is aimed towards researchers analysing field observations\, who are often faced by data heterogeneities due to field sampling protocols changing from one project to another\, or through time over the lifespan of projects\, or trying to combine legacy data sets with new data collected by recording units. \nSuch heterogeneities can bias analyses when data sets are integrated inadequately or can lead to information loss when filtered and standardized to common standards. Accounting for these issues is important for better inference regarding status and trend of species and communities. \nAnalysis of such ‘messy’ data sets need to feel comfortable with manipulating the data\, need a full understanding the mechanics of the models being used (i.e. critically interpreting the results and acknowledging assumptions and limitations)\, and should be able to make informed choices when faced with methodological challenges. \nThe course emphasizes critical thinking and active learning through hands on programming exercises. We will use publicly available data sets to demonstrate the data manipulation and analysis. We will use freely available and open-source R packages. \nThe expected outcome of the course is a solid foundation for further professional development via increased confidence in applying these methods for field observations. \nBy the end of the course\, participants should be able to: \n\nUnderstand basic statistical concepts related to detection error\nWork with field collected data and data from automated recording units (ARU)\nKnow packages such as unmarked\, detect\, bSims\nCritically evaluate modelling options and assumptions using simulations\nFit N-mixture\, distance sampling\, and time-removal models to data\n\n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students working on projects related to avian data\nApplied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a programming language such as R for analysing point count data arising from avian field surveys\n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time Zone – UK (GMT) local time \nAvailability – 25 places \nDuration – 3 days\, 4 hours per day \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT \nLanguage – English \n \n \n \n \n \n Teaching Format\n Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data. \n \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical\, mathematical and physical concepts. Specifically\, generalised linear regression models\, including mixed models; basic knowledge of calculus. \n \n \n \n \n \n Assumed computer background\n Familiarity with R\, ability to import/export data\, manipulate data frames\, fit basic statistical models (up to GLM) and generate simple exploratory and diagnostic plots. \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n \n\n\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Programme\n \n \n \n \n \n \n \n \n \n \n Tuesday 18th\n Day 1 – Classes from 13:30 – 17:30 \nIntroduction \n\nIntroduction and background\nReview of field sampling techniques\nIntroduction to agent-based simulations\nOverview of regression techniques\nNaïve estimates of occupancy and abundance\nMultiple visits and N-mixture models\n\n \n \n \n \n \n Wednesday 19th\n Day 2 – Classes from 13:30 – 17:30 \nIntroduction to modelling \n\nBird behaviour\nTime-removal models\nObservation process\nDistance sampling\nCombining removal and distance sampling (QPAD)\n\n \n \n \n \n \n Thursday 20th\n Day 3 – Classes from 13:30 – 17:30 \nDifferent approaches \n\nSingle visit-based approaches (N-mixture and SQPAD)\nAnalysing data from recording units\nMulti-species models and using species traits and phylogeny\nDealing with roadside and other biases\nClosing remarks\n\n \n \n \n \n \n \n Course Instructor\n \nDr. Peter Solymos \nPéter is an ecologist and R programmer. He has worked with continental scale data sets and developed statistical techniques for estimating population density from messy data sets. He is the author of numerous well-known R packages\, including detect\, dclone\, vegan\, and ResourceSelection. He works currently as a data scientist helping utility companies improving their outage and impact prevention practices\, and is an adjunct professor at the University of Alberta in Edmonton\, Canada. \nGoogle Scholar \nWork Homepage \nPersonal Homepage URL:https://prstats.org/course/adapting-to-the-recent-changes-in-r-spatial-packages-sf-terra-proj-library-projpr/ LOCATION:Recorded\, United Kingdom CATEGORIES:Previously Recorded Courses,Spatial (General) ATTACH;FMTTYPE=image/png:https://prstats.org/wp-content/uploads/2025/08/PROJPR.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20361022 DTEND;VALUE=DATE:20361025 DTSTAMP:20260420T031747 CREATED:20250802T123021Z LAST-MODIFIED:20260410T095153Z UID:10000501-2108246400-2108505599@prstats.org SUMMARY:Introduction to Machine Learning (IMLRPR) DESCRIPTION:Model Selection and Model Simplification (MSMS06)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, November 18th\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – UK (GMT) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n \n \n \n \n \n \n \n \n \n \n About this course\n This course is aimed towards researchers analysing field observations\, who are often faced by data heterogeneities due to field sampling protocols changing from one project to another\, or through time over the lifespan of projects\, or trying to combine legacy data sets with new data collected by recording units. \nSuch heterogeneities can bias analyses when data sets are integrated inadequately or can lead to information loss when filtered and standardized to common standards. Accounting for these issues is important for better inference regarding status and trend of species and communities. \nAnalysis of such ‘messy’ data sets need to feel comfortable with manipulating the data\, need a full understanding the mechanics of the models being used (i.e. critically interpreting the results and acknowledging assumptions and limitations)\, and should be able to make informed choices when faced with methodological challenges. \nThe course emphasizes critical thinking and active learning through hands on programming exercises. We will use publicly available data sets to demonstrate the data manipulation and analysis. We will use freely available and open-source R packages. \nThe expected outcome of the course is a solid foundation for further professional development via increased confidence in applying these methods for field observations. \nBy the end of the course\, participants should be able to: \n\nUnderstand basic statistical concepts related to detection error\nWork with field collected data and data from automated recording units (ARU)\nKnow packages such as unmarked\, detect\, bSims\nCritically evaluate modelling options and assumptions using simulations\nFit N-mixture\, distance sampling\, and time-removal models to data\n\n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students working on projects related to avian data\nApplied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a programming language such as R for analysing point count data arising from avian field surveys\n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time Zone – UK (GMT) local time \nAvailability – 25 places \nDuration – 3 days\, 4 hours per day \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT \nLanguage – English \n \n \n \n \n \n Teaching Format\n Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data. \n \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical\, mathematical and physical concepts. Specifically\, generalised linear regression models\, including mixed models; basic knowledge of calculus. \n \n \n \n \n \n Assumed computer background\n Familiarity with R\, ability to import/export data\, manipulate data frames\, fit basic statistical models (up to GLM) and generate simple exploratory and diagnostic plots. \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n \n\n\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Programme\n \n \n \n \n \n \n \n \n \n \n Tuesday 18th\n Day 1 – Classes from 13:30 – 17:30 \nIntroduction \n\nIntroduction and background\nReview of field sampling techniques\nIntroduction to agent-based simulations\nOverview of regression techniques\nNaïve estimates of occupancy and abundance\nMultiple visits and N-mixture models\n\n \n \n \n \n \n Wednesday 19th\n Day 2 – Classes from 13:30 – 17:30 \nIntroduction to modelling \n\nBird behaviour\nTime-removal models\nObservation process\nDistance sampling\nCombining removal and distance sampling (QPAD)\n\n \n \n \n \n \n Thursday 20th\n Day 3 – Classes from 13:30 – 17:30 \nDifferent approaches \n\nSingle visit-based approaches (N-mixture and SQPAD)\nAnalysing data from recording units\nMulti-species models and using species traits and phylogeny\nDealing with roadside and other biases\nClosing remarks\n\n \n \n \n \n \n \n Course Instructor\n \nDr. Peter Solymos \nPéter is an ecologist and R programmer. He has worked with continental scale data sets and developed statistical techniques for estimating population density from messy data sets. He is the author of numerous well-known R packages\, including detect\, dclone\, vegan\, and ResourceSelection. He works currently as a data scientist helping utility companies improving their outage and impact prevention practices\, and is an adjunct professor at the University of Alberta in Edmonton\, Canada. \nGoogle Scholar \nWork Homepage \nPersonal Homepage URL:https://prstats.org/course/introduction-to-machine-learning-imlrpr/ LOCATION:Recorded\, United Kingdom CATEGORIES:General Recorded Courses,Previously Recorded Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2025/07/NWAE01-2.jpg GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20361024 DTEND;VALUE=DATE:20361027 DTSTAMP:20260420T031747 CREATED:20250904T125855Z LAST-MODIFIED:20260410T095513Z UID:10000530-2108419200-2108678399@prstats.org SUMMARY:Machine Learning Intermediate to Advanced (MLIAPR) DESCRIPTION:Model Selection and Model Simplification (MSMS06)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, November 18th\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – UK (GMT) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n \n \n \n \n \n \n \n \n \n \n About this course\n This course is aimed towards researchers analysing field observations\, who are often faced by data heterogeneities due to field sampling protocols changing from one project to another\, or through time over the lifespan of projects\, or trying to combine legacy data sets with new data collected by recording units. \nSuch heterogeneities can bias analyses when data sets are integrated inadequately or can lead to information loss when filtered and standardized to common standards. Accounting for these issues is important for better inference regarding status and trend of species and communities. \nAnalysis of such ‘messy’ data sets need to feel comfortable with manipulating the data\, need a full understanding the mechanics of the models being used (i.e. critically interpreting the results and acknowledging assumptions and limitations)\, and should be able to make informed choices when faced with methodological challenges. \nThe course emphasizes critical thinking and active learning through hands on programming exercises. We will use publicly available data sets to demonstrate the data manipulation and analysis. We will use freely available and open-source R packages. \nThe expected outcome of the course is a solid foundation for further professional development via increased confidence in applying these methods for field observations. \nBy the end of the course\, participants should be able to: \n\nUnderstand basic statistical concepts related to detection error\nWork with field collected data and data from automated recording units (ARU)\nKnow packages such as unmarked\, detect\, bSims\nCritically evaluate modelling options and assumptions using simulations\nFit N-mixture\, distance sampling\, and time-removal models to data\n\n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students working on projects related to avian data\nApplied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a programming language such as R for analysing point count data arising from avian field surveys\n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time Zone – UK (GMT) local time \nAvailability – 25 places \nDuration – 3 days\, 4 hours per day \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT \nLanguage – English \n \n \n \n \n \n Teaching Format\n Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data. \n \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical\, mathematical and physical concepts. Specifically\, generalised linear regression models\, including mixed models; basic knowledge of calculus. \n \n \n \n \n \n Assumed computer background\n Familiarity with R\, ability to import/export data\, manipulate data frames\, fit basic statistical models (up to GLM) and generate simple exploratory and diagnostic plots. \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n \n\n\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Programme\n \n \n \n \n \n \n \n \n \n \n Tuesday 18th\n Day 1 – Classes from 13:30 – 17:30 \nIntroduction \n\nIntroduction and background\nReview of field sampling techniques\nIntroduction to agent-based simulations\nOverview of regression techniques\nNaïve estimates of occupancy and abundance\nMultiple visits and N-mixture models\n\n \n \n \n \n \n Wednesday 19th\n Day 2 – Classes from 13:30 – 17:30 \nIntroduction to modelling \n\nBird behaviour\nTime-removal models\nObservation process\nDistance sampling\nCombining removal and distance sampling (QPAD)\n\n \n \n \n \n \n Thursday 20th\n Day 3 – Classes from 13:30 – 17:30 \nDifferent approaches \n\nSingle visit-based approaches (N-mixture and SQPAD)\nAnalysing data from recording units\nMulti-species models and using species traits and phylogeny\nDealing with roadside and other biases\nClosing remarks\n\n \n \n \n \n \n \n Course Instructor\n \nDr. Peter Solymos \nPéter is an ecologist and R programmer. He has worked with continental scale data sets and developed statistical techniques for estimating population density from messy data sets. He is the author of numerous well-known R packages\, including detect\, dclone\, vegan\, and ResourceSelection. He works currently as a data scientist helping utility companies improving their outage and impact prevention practices\, and is an adjunct professor at the University of Alberta in Edmonton\, Canada. \nGoogle Scholar \nWork Homepage \nPersonal Homepage URL:https://prstats.org/course/machine-learning-intermediate-to-advanced-mliapr/ LOCATION:Recorded\, United Kingdom CATEGORIES:General Recorded Courses,Previously Recorded Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2025/09/MLIAPR.jpg GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20361025 DTEND;VALUE=DATE:20361028 DTSTAMP:20260420T031747 CREATED:20250904T130617Z LAST-MODIFIED:20260410T102132Z UID:10000527-2108505600-2108764799@prstats.org SUMMARY:Machine Learning using Python (MLUPPR) DESCRIPTION:Model Selection and Model Simplification (MSMS06)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, November 18th\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – UK (GMT) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n \n \n \n \n \n \n \n \n \n \n About this course\n This course is aimed towards researchers analysing field observations\, who are often faced by data heterogeneities due to field sampling protocols changing from one project to another\, or through time over the lifespan of projects\, or trying to combine legacy data sets with new data collected by recording units. \nSuch heterogeneities can bias analyses when data sets are integrated inadequately or can lead to information loss when filtered and standardized to common standards. Accounting for these issues is important for better inference regarding status and trend of species and communities. \nAnalysis of such ‘messy’ data sets need to feel comfortable with manipulating the data\, need a full understanding the mechanics of the models being used (i.e. critically interpreting the results and acknowledging assumptions and limitations)\, and should be able to make informed choices when faced with methodological challenges. \nThe course emphasizes critical thinking and active learning through hands on programming exercises. We will use publicly available data sets to demonstrate the data manipulation and analysis. We will use freely available and open-source R packages. \nThe expected outcome of the course is a solid foundation for further professional development via increased confidence in applying these methods for field observations. \nBy the end of the course\, participants should be able to: \n\nUnderstand basic statistical concepts related to detection error\nWork with field collected data and data from automated recording units (ARU)\nKnow packages such as unmarked\, detect\, bSims\nCritically evaluate modelling options and assumptions using simulations\nFit N-mixture\, distance sampling\, and time-removal models to data\n\n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students working on projects related to avian data\nApplied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a programming language such as R for analysing point count data arising from avian field surveys\n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time Zone – UK (GMT) local time \nAvailability – 25 places \nDuration – 3 days\, 4 hours per day \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT \nLanguage – English \n \n \n \n \n \n Teaching Format\n Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data. \n \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical\, mathematical and physical concepts. Specifically\, generalised linear regression models\, including mixed models; basic knowledge of calculus. \n \n \n \n \n \n Assumed computer background\n Familiarity with R\, ability to import/export data\, manipulate data frames\, fit basic statistical models (up to GLM) and generate simple exploratory and diagnostic plots. \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n \n\n\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Programme\n \n \n \n \n \n \n \n \n \n \n Tuesday 18th\n Day 1 – Classes from 13:30 – 17:30 \nIntroduction \n\nIntroduction and background\nReview of field sampling techniques\nIntroduction to agent-based simulations\nOverview of regression techniques\nNaïve estimates of occupancy and abundance\nMultiple visits and N-mixture models\n\n \n \n \n \n \n Wednesday 19th\n Day 2 – Classes from 13:30 – 17:30 \nIntroduction to modelling \n\nBird behaviour\nTime-removal models\nObservation process\nDistance sampling\nCombining removal and distance sampling (QPAD)\n\n \n \n \n \n \n Thursday 20th\n Day 3 – Classes from 13:30 – 17:30 \nDifferent approaches \n\nSingle visit-based approaches (N-mixture and SQPAD)\nAnalysing data from recording units\nMulti-species models and using species traits and phylogeny\nDealing with roadside and other biases\nClosing remarks\n\n \n \n \n \n \n \n Course Instructor\n \nDr. Peter Solymos \nPéter is an ecologist and R programmer. He has worked with continental scale data sets and developed statistical techniques for estimating population density from messy data sets. He is the author of numerous well-known R packages\, including detect\, dclone\, vegan\, and ResourceSelection. He works currently as a data scientist helping utility companies improving their outage and impact prevention practices\, and is an adjunct professor at the University of Alberta in Edmonton\, Canada. \nGoogle Scholar \nWork Homepage \nPersonal Homepage URL:https://prstats.org/course/machine-learning-using-python-mluppr/ LOCATION:Recorded\, United Kingdom CATEGORIES:General Recorded Courses,Previously Recorded Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2025/09/MLUPPR.jpg GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20361029 DTEND;VALUE=DATE:20361101 DTSTAMP:20260420T031747 CREATED:20250904T130605Z LAST-MODIFIED:20260410T102307Z UID:10000529-2108851200-2109110399@prstats.org SUMMARY:Machine Vision using Python (MVUPPR) DESCRIPTION:Model Selection and Model Simplification (MSMS06)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, November 18th\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – UK (GMT) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n \n \n \n \n \n \n \n \n \n \n About this course\n This course is aimed towards researchers analysing field observations\, who are often faced by data heterogeneities due to field sampling protocols changing from one project to another\, or through time over the lifespan of projects\, or trying to combine legacy data sets with new data collected by recording units. \nSuch heterogeneities can bias analyses when data sets are integrated inadequately or can lead to information loss when filtered and standardized to common standards. Accounting for these issues is important for better inference regarding status and trend of species and communities. \nAnalysis of such ‘messy’ data sets need to feel comfortable with manipulating the data\, need a full understanding the mechanics of the models being used (i.e. critically interpreting the results and acknowledging assumptions and limitations)\, and should be able to make informed choices when faced with methodological challenges. \nThe course emphasizes critical thinking and active learning through hands on programming exercises. We will use publicly available data sets to demonstrate the data manipulation and analysis. We will use freely available and open-source R packages. \nThe expected outcome of the course is a solid foundation for further professional development via increased confidence in applying these methods for field observations. \nBy the end of the course\, participants should be able to: \n\nUnderstand basic statistical concepts related to detection error\nWork with field collected data and data from automated recording units (ARU)\nKnow packages such as unmarked\, detect\, bSims\nCritically evaluate modelling options and assumptions using simulations\nFit N-mixture\, distance sampling\, and time-removal models to data\n\n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students working on projects related to avian data\nApplied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a programming language such as R for analysing point count data arising from avian field surveys\n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time Zone – UK (GMT) local time \nAvailability – 25 places \nDuration – 3 days\, 4 hours per day \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT \nLanguage – English \n \n \n \n \n \n Teaching Format\n Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data. \n \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical\, mathematical and physical concepts. Specifically\, generalised linear regression models\, including mixed models; basic knowledge of calculus. \n \n \n \n \n \n Assumed computer background\n Familiarity with R\, ability to import/export data\, manipulate data frames\, fit basic statistical models (up to GLM) and generate simple exploratory and diagnostic plots. \n \n \n \n \n \n Equipment and software requirements\n \nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n \n \n \n \n \n \n\n\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Programme\n \n \n \n \n \n \n \n \n \n \n Tuesday 18th\n Day 1 – Classes from 13:30 – 17:30 \nIntroduction \n\nIntroduction and background\nReview of field sampling techniques\nIntroduction to agent-based simulations\nOverview of regression techniques\nNaïve estimates of occupancy and abundance\nMultiple visits and N-mixture models\n\n \n \n \n \n \n Wednesday 19th\n Day 2 – Classes from 13:30 – 17:30 \nIntroduction to modelling \n\nBird behaviour\nTime-removal models\nObservation process\nDistance sampling\nCombining removal and distance sampling (QPAD)\n\n \n \n \n \n \n Thursday 20th\n Day 3 – Classes from 13:30 – 17:30 \nDifferent approaches \n\nSingle visit-based approaches (N-mixture and SQPAD)\nAnalysing data from recording units\nMulti-species models and using species traits and phylogeny\nDealing with roadside and other biases\nClosing remarks\n\n \n \n \n \n \n \n Course Instructor\n \nDr. Peter Solymos \nPéter is an ecologist and R programmer. He has worked with continental scale data sets and developed statistical techniques for estimating population density from messy data sets. He is the author of numerous well-known R packages\, including detect\, dclone\, vegan\, and ResourceSelection. He works currently as a data scientist helping utility companies improving their outage and impact prevention practices\, and is an adjunct professor at the University of Alberta in Edmonton\, Canada. \nGoogle Scholar \nWork Homepage \nPersonal Homepage URL:https://prstats.org/course/machine-vision-using-python-mvuppr/ LOCATION:Recorded\, United Kingdom CATEGORIES:General Recorded Courses,Previously Recorded Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2025/09/MVUPPR-1.jpg GEO:55.378051;-3.435973 END:VEVENT END:VCALENDAR