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Making Bayes fast since 2011

Stan is a state-of-the-art platform for statistical modeling and high-performance statistical computation. Thousands of users rely on Stan for statistical modeling, data analysis, and prediction in the social, biological, and physical sciences, engineering, and business.

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Joined November 10, 2022

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Stan @mcmc_stan@bayes.club

· 29mo ago

Mvgam: an R package for time series modeling and forecasting (built with Stan) by Nicholas Clark

... Dynamic Generalized Additive Models (DGAMs) for time series with dynamic trend components. It uses a State-Space framework with a formula syntax based on that of the package mgcv to provide a familiar GAM modelling interface. There is also built-in support for the increasingly powerful marginaleffects package to make interpretation easy.

https://discourse.mc-stan.org/t/mvgam-an-r-package-for-time-series-modeling-and-forecasting/35065

#Bayes #mcmc #rstats

The Stan Forums

Mvgam: an R package for time series modeling and forecasting

Happy to announce that I have released a new package to CRAN called mvgam. The goal of mvgam is to estimate parameters of Dynamic Generalized Additive Models (DGAMs) for time series with dynamic trend components. It uses a State-Space framework with a formula syntax based on that of the package mgcv to provide a familiar GAM modelling interface. There is also built-in support for the increasingly powerful marginaleffects package to make interpretation easy. The package is quite broad, all

Stan @mcmc_stan@bayes.club

· 29mo ago

dynamite: Bayesian Modeling and Causal Inference for Multivariate Longitudinal Data (built with Stan) by Santtu Tikka and @jouni_helske@fediscience.org

... complex panel (time series) data comprising of multiple measurements per multiple individuals measured in time via dynamic multivariate panel models...

https://docs.ropensci.org/dynamite/index.html

#bayes #mcmc #rstats

Bayesian Modeling and Causal Inference for Multivariate Longitudinal Data

docs.ropensci.org

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 (Ti