🚨 Course Alert🚨 Want to learn how to tackle the complexities of ecological time-series data in R? 📈 Join us to learn the magic of @mcmc_stan #rstatswww.physalia-courses.org/courses-work...
24-28 March 2025 To foster international participation, this course will be held online
panel models... https://docs.ropensci.org/dynamite/index.html#bayes#mcmc#rstatshttps://bayes.club/@mcmc_stan/112461195025039977 2/2
Easy-to-use and efficient interface for Bayesian inference of complex panel (time series) data using dynamic multivariate panel models by Helske and Tikka (2022) <doi:10.31235/osf.io/mdwu5>. The package supports joint modeling of multiple measurements per individual, time-varying and time-invariant effects, and a wide range of discrete and continuous distributions. Estimation of these dynamic multivariate panel models is carried out via Stan. For an in-depth tutorial of the package, see (Tikka and Helske, 2023) <arxiv:2302.01607>.
"Past, Present and Future of Software for Bayesian Inference" by Štrumbelj et al. (2024). Statistical Science. doi.org/10.1214/23-S...#rstats#Python#JuliaLanguage#mcmc_stan#INLA#MCMC#BUGS#blackjax#JAGS#HMC#Matlab#Mathematica#ABC#Turing.jl