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Intermediate-level workshop Introduction to NIMBLE: the fanciest member in the BUGS family Instructors: Perry de Valpine, University of California-Berkeley Date: 23–25 April 2018 Venue: Swiss Ornithological Institute, Sempach, Switzerland Computers: Bring your own laptop with latest R and NIMBLE Registration: CHFr. 350 (normal rate), CHFr. 200 (student rate) Complex hierarchical models are often fitted with software that uses the BUGS language for model description (e.g., JAGS, WinBUGS or OpenBUGS). For non-statisticians the simplicity of the BUGS language is a key to be able to fit such models in practice. However, the fitting algorithms implemented in these programs (MCMC) are mostly black boxes, and there are only very limited possibilities for the user to affect them. Therefore, in some cases convergence might be difficult to achieve at all or the algorithms may be inefficient. The new NIMBLE software (https://r-nimble.org/) offers solutions to these problems. It comes as an R package and uses the BUGS language to describe statistical models. In addition it gives very wide user control over the MCMC samplers. Moreover, NIMBLE interacts in a more direct way with R allowing the possibility to easily specify your own functions as needed. Perry de Valpine is a creator and main developer of NIMBLE and will give a comprehensive overview about the possibilities of this exciting new package. The workshop is geared towards scientists from ecology and related fields (e.g. fisheries and wildlife). The general topics in hierarchical statistical modeling and computational methods apply as well to many other applications. Contents include the following: 1. Introduction to NIMBLE Migrating from BUGS and JAGS The NIMBLE workflow in R Querying and manipulating models in R 2. MCMC in NIMBLE Configuring samplers Comparing different kinds of MCMC (within and outside of NIMBLE) Debugging 3. Programming in NIMBLE The nimbleFunction system for writing model-generic algorithms The NIMBLE compiler 4. Writing new samplers, new distributions and new functions to use in models 5. Maximum likelihood with NIMBLE 6. Sequential Monte Carlo (particle filtering) with NIMBLE In this intermediate-level workshop about 3/4 of the time is spent on lecturing and 1/4 on solving exercises. No previous experience with the NIMBLE software is assumed. However, a good working knowledge of modern regression methods (ANOVA, ANCOVA, GLMs) and of program R is required. Moreover, knowledge in of the programs using the BUGS language (JAGS, WinBUGS) is highly advantageous. Send your application to Michael Schaub (email@example.com), with CC to Marc Kéry (firstname.lastname@example.org); describing your background and knowledge in statistical modeling, R and WinBUGS/OpenBUGS/JAGS/NIMBLE and capture-recapture, by 28 February 2018 at the latest. Workshop invitations will be sent out immediately afterwards.
Instructors: Marc Kéry & Andy Royle, Swiss Ornithological Institute & USGS Patuxent Wildlife Research Center Date: 9–13 November 2015 Venue: Patuxent Wildlife Research Center, Laurel/MD Course fee: USD$550 (normal rate), USD$350 (student rate) The analysis of abundance and of the dynamic rates governing their change lies at the core of ecology and its applications such as conservation and wildlife management. Meta-population designs, where repeated measurements of some quantity such as counts or distance measurements are made at a collection of sites, underlie a vast number of studies in ecology and management. Inference about such data is conveniently based on hierarchical models, where one submodel describes the underlying true state of the process (e.g., abundance at a site) and another submodel describes the observation process that connects the true state to the observations. In recent years, much progress has been made in the development of methods and computer algorithms to fit hierarchical models. In particular, Bayesian statistical analysis and the general-purpose Bayesian software packages BUGS and JAGS have revolutionized the ways in which ecologists can conduct complex population analyses. On the other hand, the R package unmarked contains a wealth of functions for a frequentist analysis of hierarchical models of abundance. This course introduces key hierarchical models used in the analysis of abundance and survival and their spatial and temporal patterns, and provides both Bayesian and frequentist methods for their analysis. We use packages unmarked and wiqid and especially WinBUGS, OpenBUGS and JAGS to fit and understand some of the most widely used models for the analysis of animal and plant populations. These include: Binomial (Royle 2004) and multinomial N-mixture models (Dorazio et al. 2005, Royle et al. 2007) Conventional distance sampling and Hierarchical distance sampling (e.g., Royle et al. 2004, Sillett et al. 2012) Dynamic models of abundance for replicated counts (Dail & Madsen 2011) or distance sampling data (Sollmann et al. 2015) Cormack-Jolly-Seber (CJS) models, especially hierarchical CJS models for variation in survival over space (Saracco et al. 2010) or in a community of species This is an intermediate-level workshop with about 80% spent lecturing and 20% on solving exercises. A working knowledge of modern regression methods (GLMs, mixed models) and of program R is required. Previous experience with the BUGS language is beneficial. Please bring your own laptops and install a recent version of R, with the latest version of package unmarked, plus JAGS and/or WinBUGS 1.4. OpenBUGS works for most of what we do. Please apply here by 1 October 2015: https://docs.google.com/forms/d/1qqjfnIqk7TX8MTaYX2UzPl9OtdCtjodKQ206Qd8zSKY/viewform?usp=send_form
Instructors: Marc Kéry & Jérôme Guélat, Swiss Ornithological Institute Date: 9–11 February 2015 Venue: Universidade Federal de Viçosa, Minas Gerais, Brazil Computers: Bring your own laptop with latest R, JAGS and WinBUGS or OpenBUGS Costs: 1000 Reais (400 US$) This course gives an introduction to Bayesian statistical modeling using BUGS software and then introduces a key class of models for the analysis of species distribution, habitat selection, occurrence and abundance: site‐occupancy models (MacKenzie et al. 2002, 2003; Tyre et al. 2003). Model fitting is shown using the Bayesian BUGS software and the R package unmarked. The course follows the book “Bayesian population analysis using WinBUGS” (Academic Press, 2012) by Kéry & Schaub and the upcoming book “Applied hierarchical models in ecology” (Academic Press, 2015) by Marc Kéry & Andy Royle. See the attached flier for more details. Occupancy Workshop Announcement, Vicosa, 9-11 Feb 2015.pdf