Title: Bayesian Dynamic Factor Analysis (DFA) with 'Stan'
person(c("Eric", "J."), "Ward", role = c("aut", "cre"),
person(c("Sean", "C."), "Anderson", role = "aut"),
person(c("Luis", "A."), "Damiano", role = "aut"),
person(c("Mary", "E."), "Hunsicker,", role = "ctb"),
person(c("Mike", "A."), "Litzow", role = "ctb"),
Description: Implements Bayesian dynamic factor analysis with 'Stan'. Dynamic
factor analysis is a dimension reduction tool for multivariate time series.
'bayesdfa' extends conventional dynamic factor models in several ways.
First, extreme events may be estimated in the latent trend by modeling
process error with a student-t distribution. Second, autoregressive and
moving average components can be optionally included. Third, the estimated
dynamic factors can be analyzed with hidden Markov models to evaluate
Suggests: testthat, parallel, knitr, rmarkdown, MARSS
Maintainer: Eric J. Ward <eric.ward@noaa.gov>

Imports: rstan (>= 2.18.2), rstantools (>= 1.5.1), ggplot2, loo (>=

person(c("Mark", "D."), "Scheuerell", role = "ctb"),
person(c("Elizabeth", "E."), "Holmes", role = "ctb"),
person(c("Nick",""), "Tolimieri", role = "ctb"),
process error with a student-t distribution. Second, alternative constraints
(including proportions are allowed). Third, the estimated
Imports: dplyr, ggplot2, loo (>= 2.0.0), methods, Rcpp (>= 0.12.0),
RcppParallel (>= 5.0.1), reshape2, rlang, rstan (>= 2.18.1),

Imports: dplyr, ggplot2, loo (>= 2.0.0), methods, mgcv (>= 1.8.13),
Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), reshape2, rlang,
rstan (>= 2.18.1), rstantools (>= 2.1.1), viridisLite

person("Emmanuel", "Tsyawo", email = "estsyawo@temple.edu", role = c("aut","cre")), person("Weige", "Huang", email = "weige.huang@temple.edu", role=c("aut")))
Description: Implements Bayesian Distribution Regression methods. This package contains functions for three estimators (non-asymptotic, semi-asymptotic and asymptotic) and related routines for Bayesian Distribution Regression in Huang and Tsyawo (2018) <doi:10.2139/ssrn.3048658> which is also the recommended reference to cite for this package. The functions can be grouped into three (3) categories. The first computes the logit likelihood function and posterior densities under uniform and normal priors. The second contains Independence and Random Walk Metropolis-Hastings Markov Chain Monte Carlo (MCMC) algorithms as functions and the third category of functions are useful for semi-asymptotic and asymptotic Bayesian distribution regression inference.