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:20250330T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20251026T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20260329T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20261025T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20270328T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20271031T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20280326T010000 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END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20331030T010000 END:STANDARD 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:20260409 DTEND;VALUE=DATE:20360410 DTSTAMP:20260413T010730 CREATED:20260409T161126Z LAST-MODIFIED:20260410T080622Z UID:10000606-1775692800-2091398399@prstats.org SUMMARY:Mechanistic Species Distribution Modelling / Ecological Niche Modelling with NicheMapR (MSDMPR) DESCRIPTION: URL:https://prstats.org/course/mechanistic-species-distribution-modelling-ecological-niche-modelling-with-nichemapr-msdmpr/ CATEGORIES:Previously Recorded Courses,Spatial Ecology ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2026/01/SDMS01-1.jpg END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260410 DTEND;VALUE=DATE:20360411 DTSTAMP:20260413T010730 CREATED:20260410T101455Z LAST-MODIFIED:20260410T101646Z UID:10000609-1775779200-2091484799@prstats.org SUMMARY:Advanced Python for Ecologists and Evolutionary Biologists (APYBPR) DESCRIPTION: URL:https://prstats.org/course/advanced-python-for-ecologists-and-evolutionary-biologists-apybpr/ CATEGORIES:Molecular Ecology,Previously Recorded Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.org/wp-content/uploads/2025/04/AYPB01.jpg END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260413 DTEND;VALUE=DATE:20260418 DTSTAMP:20260413T010730 CREATED:20260126T121108Z LAST-MODIFIED:20260127T163252Z UID:10000582-1776038400-1776470399@prstats.org SUMMARY:Machine Learning for Ecological Time Series (METR01) DESCRIPTION:Advanced Python for Ecologists and Evolutionary Biologists (APYBPR)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, September 22nd\, 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 \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. \nCourse Program\nTIME ZONE – Spain (GMT+2) local time UTC+2 – 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 Course Details\n The aim of the course is to introduce you to Bayesian inference using the integrated nested Laplace approximation (INLA) method and its associated R-INLA package for the analysis of spatial and spatio-temporal data. This course will cover the basics on the INLA methodology as well as practical modelling of different types of spatial and spatio-temporaldata. \nBy the end of the course participants should be able to: \n\nKnow the different types of spatial and spatio-temporal data available and how to work with them in R.\nKnow the different modelling approaches for spatial and spatio-temporal data.\nKnow how to visualize and produce maps of spatial and spatio-temporal data.\nBe able to fit models with the R-INLA package.\nKnow how to interpret the output from model fitting.\nBe confident with the use of INLA for data analysis.\nUnderstand the different models that can be fit with INLA to spatial and spatio-temporal data.\nKnow how to define the different parts of a model with INLA.\nHave the confidence to use INLA for their own projects.\n\n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students working on projects related to spatial and spatio-temporal data analysis and modelling and who want to add the INLA methodology for Bayesian inference to their toolbox.\nApplied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a command-line language such as R.\nThe course is designed for intermediate-to-advanced R users interested in data analysis and modelling. Ideally\, they should have some background on probability\, statistics and data analysis.\n\n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Information\n Time zone – Spain (GMT+2) local time \nAvailability – 20 places \nDuration – 5 days \nContact hours – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n  \nPLEASE READ – CANCELLATION POLICY: Cancellations 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 Teaching Format\n \n\nThe course will be a mixture of theoretical and practical sessions. Each concept will be first described and explained\, and next there will be a time to exercise the topics using provided data sets. Participants are also very welcome to bring their own data. \n\n\n \n \n \n \n \n Assumed quantitative knowledge\n The course is designed for intermediate-to-advanced R users interested in Bayesian inference for data analysis and R beginners who have prior experience with Bayesian inference. Although an introduction to the INLA method will be given\, attendants are expected to be familiar with Bayesian inference. This includes how to define simple Bayesian models and have a basic understanding of some typical methods to compute or approximate the prior distributions (such as models with conjugate priors\, MCMC methods\, etc.). \n \n \n \n \n \n \n Assumed computer background\n Attendees should already have experience with R and be familiar with data from different formats (csv\, tab\, etc.)\, create simple plots\, and manipulate data frames. Furthermore\, knowledge of how to fit generalized linear (mixed) models using typical R functions (such as glm and lme4) will be useful. No previous background on handling of spatial and spatio-temporal data will be assumed.\n \n \n \n \n \n Equipment and software requirements\n \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 Tickets \n \n \n \n \n \n \n The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities. \n\n\n\n \n \n APYBPR RECORDED\n \n APYBPR RECORDED\n\n \n \n \n \n £\n 480.00\n \n \n\n \n Unlimited \n \n \n Open the ticket description.\n More \n \n Close the ticket description.\n Less \n \n \n\n \n \n Decrease ticket quantity for APYBPR RECORDED\n -\n \n \n Quantity \n \n\n \n Increase ticket quantity for APYBPR RECORDED\n +\n \n \n \n \n\n \n \n Quantity: \n 0\n\n \n \n Total: \n \n \n \n £\n 0.00\n \n \n\n \n Get Tickets\n \n\n \n \n \n\n \n \n\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 If you are unsure about course suitability\, please get in touch by email to find out more \noliverhooker@prstatistics.com\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 22nd\n Day 1 – Classes from 14:00 to 21:00 \n\nSession 1 – Intro to INLA\nPractical 1 – Intro to INLA\nSession 2 – Model fitting with INLA\nPractical 2 – Model fitting with INLA\nSession 3 – GLMM’s with INLA\nPractical 3 – GLMM’s with INLA\nQ and A and end of day summary\n\n \n \n \n \n \n Tuesday 23rd\n Day 2 – Classes from 14:00 to 21:00 \n\nSession 4 – Spatial Data\nPractical 4 – Spatial Data\nSession 5 – Spatio-Temporal Data\nPractical 5 – Spatio-Temporal Data\nSession 6 – Advanced Visualisation\nPractical 6 – Advanced Visualisation\nQ and A and end of day summary\n\n \n \n \n \n \n Wednesday 24th\n Day 3 – Classes from 14:00 to 21:00 \n\nSession 7 – Spatial Models for Lattice Data\nPractical 7 – Spatial Models for Lattice Data\nSession 8 – Spatial Models for Continuous Data\nPractical 8 – Spatial Models for Continuous Data\nSession 9 – Spatial Models for Point Patterns\nPractical 9 – Spatial Models for Point Patterns\nQ and A and end of day summary\n\n \n \n \n \n \n Thursday 25th\n Day 4 – Classes from 14:00 to 21:00 \n\nSession 10 – Spatio-Temporal Models for Lattice Data\nPractical 10 – Spatio-Temporal Models for Lattice Data\nSession 11 – Spatio-Temporal Models  for Continuous Data\nPractical 11 – Spatio-Temporal Models  for Continuous Data\nSession 12 – Spatio-Temporal Models  for Point Patterns\nPractical 12 – Spatio-Temporal Models  for Point Patterns\nQ and A and end of day summary\n\n \n \n \n \n \n Friday 26th\n Day 5 – Classes from 14:00 to 21:00 \n\nCase studies\, own data and problem solving.\n\n \n \n \n \n \n \n \n \n Dr Virgillio Gomez Rubio\n \n Virgilio has ample experience in Bayesian inference and statistical modeling as well as developing packages for the R programming language. His book Bayesian inference with INLA has been widely adopted for Bayesian modeling and it has been awarded the 2022 SEIO-BBVA Foundation Award in the category of Data Science and Big Data. You can find more information about him on here\n\n\nResearchgate\n\nGoogle Scholar\n\nORCID\n\nGitHub URL:https://prstats.org/course/machine-learning-for-ecological-time-series-metr01/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.org/wp-content/uploads/2026/01/METR01.png GEO:55.378051;-3.435973 END:VEVENT END:VCALENDAR