WORKSHOPS
1. SESYNC Short Course: Interactive Web-Based Visualizations and Decision Support Tools in Shiny/R for Quantitative Scientists. Scheduled for Feb. 10-12, 2021 at the National Socio-Environmental Synthesis Center (SESYNC). Course material is available here
COURSES
1. Introduction to Applied Statistics for Agricultural and Life sciences
2. Introduction to Bayesian Statistics in Life Sciences. Part of the material for this course is posted in the website https://drvalle1.github.io/
TUTORIALS/CODE
5. Tutorial to fit the presence/absence LDA model with truncated stick-breaking prior (based on Valle et al. 2018 Global Change Biology)
4. Tutorial to fit the new clustering method proposed for movement data (based on Valle et al., 2017 Scientific Reports).
3. Tutorial to fit the Leishmaniasis dispersion model (based on Seva et al., 2017 PLOS Neglected Tropical Diseases).
2. Code and simulated data to fit the Latent Dirichlet Allocation (LDA) model (based on Valle et al. 2014 Ecology Letters).
1. Code and simulated data (1, 2, and 3) to fit a Bayesian logistic regression model while accounting for imperfect detection in various ways (based on Valle et al., 2015 Malaria Journal).
1. SESYNC Short Course: Interactive Web-Based Visualizations and Decision Support Tools in Shiny/R for Quantitative Scientists. Scheduled for Feb. 10-12, 2021 at the National Socio-Environmental Synthesis Center (SESYNC). Course material is available here
COURSES
1. Introduction to Applied Statistics for Agricultural and Life sciences
2. Introduction to Bayesian Statistics in Life Sciences. Part of the material for this course is posted in the website https://drvalle1.github.io/
TUTORIALS/CODE
5. Tutorial to fit the presence/absence LDA model with truncated stick-breaking prior (based on Valle et al. 2018 Global Change Biology)
4. Tutorial to fit the new clustering method proposed for movement data (based on Valle et al., 2017 Scientific Reports).
3. Tutorial to fit the Leishmaniasis dispersion model (based on Seva et al., 2017 PLOS Neglected Tropical Diseases).
2. Code and simulated data to fit the Latent Dirichlet Allocation (LDA) model (based on Valle et al. 2014 Ecology Letters).
1. Code and simulated data (1, 2, and 3) to fit a Bayesian logistic regression model while accounting for imperfect detection in various ways (based on Valle et al., 2015 Malaria Journal).