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ropensci
dynamite:Bayesian Modeling and Causal Inference for Multivariate Longitudinal Data
Easy-to-use and efficient interface for Bayesian inference of complex panel (time series) data using dynamic multivariate panel models by Helske and Tikka (2024) <doi:10.1016/j.alcr.2024.100617>. 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, 2024) <doi:10.48550/arXiv.2302.01607>.
Maintained by Santtu Tikka. Last updated 4 days ago.
bayesian-inferencepanel-datastanstatistical-models
29 stars 7.90 score 20 scripts
uscbiostats
fmcmc:A friendly MCMC framework
Provides a friendly (flexible) Markov Chain Monte Carlo (MCMC) framework for implementing Metropolis-Hastings algorithm in a modular way allowing users to specify automatic convergence checker, personalized transition kernels, and out-of-the-box multiple MCMC chains using parallel computing. Most of the methods implemented in this package can be found in Brooks et al. (2011, ISBN 9781420079425). Among the methods included, we have: Haario (2001) <doi:10.1007/s11222-011-9269-5> Adaptive Metropolis, Vihola (2012) <doi:10.1007/s11222-011-9269-5> Robust Adaptive Metropolis, and Thawornwattana et al. (2018) <doi:10.1214/17-BA1084> Mirror transition kernels.
Maintained by George Vega Yon. Last updated 2 years ago.
adaptivebayesian-inferencemarkov-chain-monte-carlomcmcmetropolis-hastingsparallel-computing
16 stars 6.79 score 86 scripts 1 dependents