c(person("Jiqiang", "Guo", email = "guojq28@gmail.com", role = "aut"),
person("Daniel", "Lee", role = "ctb"),
person("Krzysztof", "Sakrejda", role = "ctb"),
person("Jonah", "Gabry", role = "aut"),
person("Ben", "Goodrich", email = "benjamin.goodrich@columbia.edu", role = c("cre", "aut")),
person("Joel", "de Guzman", role = "cph", comment = "Boost"),
person("Eric", "Niebler", role = "cph",   comment = "Boost"),
person("Thomas", "Heller", role = "cph",  comment = "Boost"),
person("John", "Fletcher", role = "cph",  comment = "Boost"))
Rcpp, RcppEigen, BH (>= 1.58), StanHeaders (>= 2.8.0)
RUnit, RcppEigen, BH (>= 1.58), StanHeaders (>= 2.8.0),
parallel, KernSmooth, RCurl, loo, rstudioapi, Matrix
Jiqiang Guo [aut],
Daniel Lee [ctb],
Krzysztof Sakrejda [ctb],
Jonah Gabry [aut],
Ben Goodrich [cre, aut],
Joel de Guzman [cph] (Boost),
Eric Niebler [cph] (Boost),
Thomas Heller [cph] (Boost),
John Fletcher [cph] (Boost)

methods, stats4, inline, gridExtra (>= 2.0.0), Rcpp (>=
0.11.0)

User-facing R functions are provided by this package to parse, compile, test, estimate, and analyze Stan models by accessing the header-only Stan library provided by the 'StanHeaders' package. The Stan project develops a probabilistic programming language that implements full Bayesian statistical inference via Markov Chain Monte Carlo, rough Bayesian inference via variational approximation, and (optionally penalized) maximum likelihood estimation via optimization. In all three cases, automatic differentiation is used to quickly and accurately evaluate gradients without burdening the user with the need to derive the partial derivatives.
Rcpp, RcppEigen, BH (>= 1.58), StanHeaders (>= 2.9.0)
RUnit, RcppEigen, BH (>= 1.58), StanHeaders (>= 2.9.0),
parallel, KernSmooth, RCurl, loo, rstudioapi, Matrix

Bayesian Applied Regression Modeling via Stan
c(person("Jonah", "Gabry", email = "jsg2201@columbia.edu", role = "aut"),
person("Trustees of", "Columbia University", role = "cph"),
person("R Core", "Deveopment Team", role = "cph",
comment = "R/pp_data.R, R/stan_aov.R"),
person("Douglas", "Bates", role = "cph", comment = "R/pp_data.R"),
person("Martin", "Maechler", role = "cph", comment = "R/pp_data.R"),
person("Ben", "Bolker", role = "cph", comment = "R/pp_data.R"),
person("Steve", "Walker", role = "cph", comment = "R/pp_data.R"),
person("Brian", "Ripley", role = "cph",
comment = "R/stan_aov.R, R/stan_polr.R"),
person("William", "Venables", role = "cph", comment = "R/stan_polr.R"),
person("Ben", "Goodrich", email = "benjamin.goodrich@columbia.edu",
role = c("cre", "aut")))
Estimates pre-compiled regression models using the 'rstan' package, which provides
the R interface to the Stan C++ library for Bayesian estimation. Users specify models via the
customary R syntax with a formula and data.frame plus some additional arguments for priors.
arm, gamm4, gridExtra, HSAUR3, KernSmooth, knitr, MASS,
rmarkdown, roxygen2, testthat (>= 1.0.2)
Jonah Gabry [aut],
Trustees of Columbia University [cph],
R Core Deveopment Team [cph] (R/pp_data.R, R/stan_aov.R),
Douglas Bates [cph] (R/pp_data.R),
Martin Maechler [cph] (R/pp_data.R),
Ben Bolker [cph] (R/pp_data.R),
Steve Walker [cph] (R/pp_data.R),
Brian Ripley [cph] (R/stan_aov.R, R/stan_polr.R),
William Venables [cph] (R/stan_polr.R),
Ben Goodrich [cre, aut]