Currently, the following backends are supported: DNF, APT.

Title: bspmma: Bayesian Semiparametric Models for Meta-Analysis
Description: Some functions for nonparametric and semiparametric
Bayesian models for random effects meta-analysis

Title: Bayesian Semiparametric Models for Meta-Analysis
Description: The main functions carry out Gibbs' sampler routines for nonparametric and semiparametric Bayesian models for random effects meta-analysis.

Title: Bayesian Spatial Regression Models
person(given = "Nikolas", family = "Kuschnig", role = c("aut", "cre"), email = "nikolas.kuschnig@wu.ac.at", comment = c(ORCID = "0000-0002-6642-2543"))
Author: Nikolas Kuschnig [aut, cre] (<https://orcid.org/0000-0002-6642-2543>)
Description: Fit Bayesian models with a focus on spillover effects. Implements linear specifications (with or without shrinkage), allows for spatial lags of the dependent, explanatories, and error term, parameterised connectivity, and stochastic volatility.
Imports: R6, Matrix, stats, graphics, utils, grDevices

Description: Fit Bayesian models with a focus on the spatial econometric models.

Title: Bayesian Inference of State Space Models
Description: Efficient methods for Bayesian inference of state space models
via particle Markov chain Monte Carlo and importance sampling type corrected
Markov chain Monte Carlo. Gaussian, Poisson, binomial, or negative binomial
observation densities and Gaussian state dynamics, as well as general
non-linear Gaussian models are supported.
LinkingTo: BH, Rcpp, RcppArmadillo, ramcmc

LinkingTo: BH, Rcpp, RcppArmadillo, ramcmc, sitmo

Title: Bayesian Inference of Non-Linear and Non-Gaussian State Space
via particle Markov chain Monte Carlo and parallel importance sampling type weighted
Markov chain Monte Carlo (Vihola, Helske, and Franks, 2017, <arXiv:1609.02541>).
Gaussian, Poisson, binomial, or negative binomial
observation densities and basic stochastic volatility models with Gaussian state
dynamics, as well as general non-linear Gaussian models and discretised diffusion models
Suggests: bayesplot, KFAS (>= 1.2.1), knitr (>= 1.11), MASS, ramcmc,
rmarkdown (>= 0.8.1), sde, sitmo, testthat
Author: Jouni Helske [aut, cre] (<https://orcid.org/0000-0001-7130-793X>),