Enables binary package installations on Linux distributions.
Provides functions to manage packages via the distribution's package
manager. Also provides transparent integration with R's install.packages()
and a fallback mechanism. When installed as a system package, interacts
with the system's package manager without requiring administrative
privileges via an integrated D-Bus service; otherwise, uses sudo.
Currently, the following backends are supported: DNF, APT.

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

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

person(given = "Nikolas", family = "Kuschnig", role = c("aut", "cre"), email = "nikolas.kuschnig@wu.ac.at", comment = c(ORCID = "0000-0002-6642-2543"))
Nikolas Kuschnig [aut, cre] (<https://orcid.org/0000-0002-6642-2543>)
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.
R6, Matrix, stats, graphics, utils, grDevices

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

Bayesian Inference of State Space Models
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.
KFAS (>= 1.2.1), knitr (>= 1.11), rmarkdown (>= 0.8.1),
testthat, bayesplot

Bayesian Inference of Non-Linear and Non-Gaussian State Space
Models
c(person(given = "Jouni",
family = "Helske",
role = c("aut", "cre"),
email = "jouni.helske@iki.fi",
comment = c(ORCID = "0000-0001-7130-793X")),
person(given = "Matti",
family = "Vihola",
role = "aut",
comment = c(ORCID = "0000-0002-8041-7222")))
Efficient methods for Bayesian inference of state space models
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
are supported.
bayesplot, KFAS (>= 1.2.1), knitr (>= 1.11), MASS, ramcmc,
rmarkdown (>= 0.8.1), sde, sitmo, testthat