Which package/function should be used to estimate the parameters of distribution in Bayesian inference in R language? I am trying to estimate the parameters of Weibull distribution in case of Bayesian statistics considering that both the parameters have Gamma priors.
Kindly help me out. Specially for MCMC method.
Applied researchers interested in Bayesian statistics are increasingly attracted to R because of the ease of which one can code algorithms to sample from posterior distributions as well as the significant number of packages contributed to the Comprehensive R Archive Network (CRAN) that provide tools for Bayesian inference
This task view catalogs these tools. In this task view, we divide those packages into four groups based on the scope and focus of the packages
Thanx for gracious participation respected Dr Rajneesh Kumar
We first review R packages that provide Bayesian estimation tools for a wide range of models. We then discuss packages that address specific Bayesian models or specialized methods in Bayesian statistics
The arm package contains R functions for Bayesian inference using lm, glm, mer and polr objects.
BACCO is an R bundle for Bayesian analysis of random functions. BACCO contains three sub-packages: emulator, calibrator, and approximator, that perform Bayesian emulation and calibration of computer programs
bayesm provides R functions for Bayesian inference for various models widely used in marketing and micro-econometrics.
The models include linear regression models, multinomial logit, multinomial probit, multivariate probit, multivariate mixture of normals (including clustering), density estimation using finite mixtures of normals as well as Dirichlet Process priors, hierarchical linear models, hierarchical multinomial logit, hierarchical negative binomial regression models, and linear instrumental variable models
LaplacesDemon seeks to provide a complete Bayesian environment, including numerous MCMC algorithms, Laplace Approximation with multiple optimization algorithms, scores of examples, dozens of additional probability distributions, numerous MCMC diagnostics, Bayes factors, posterior predictive checks, a variety of plots, elicitation, parameter and variable importance, and numerous additional utility functions
loo provides functions for efficient approximate leave-one-out cross-validation (LOO) for Bayesian models using Markov chain Monte Carlo. The approximation uses Pareto smoothed importance sampling (PSIS), a new procedure for regularizing importance weights. As a byproduct of the calculations, loo also provides standard errors for estimated predictive errors and for the comparison of predictive errors between models. The package also provides methods for using stacking and other model weighting techniques to average Bayesian predictive distributions
The approximation uses Pareto smoothed importance sampling (PSIS), a new procedure for regularizing importance weights. As a byproduct of the calculations, loo also provides standard errors for estimated predictive errors
The mcmc package consists of an R function for a random-walk Metropolis algorithm for a continuous random vector
The nimble package provides a general MCMC system that allows customizable MCMC for models written in the BUGS/JAGS model language. Users can choose samplers and write new samplers. Models and samplers are automatically compiled via generated C++. The package also supports other methods such as particle filtering or whatever users write in its algorithm language
Thanks for gracious contribution dear Dr Guniyanj Bause Bause
Thanks for gracious contribution dear Dr Schwincher Fernandance
Thanks for gracious contribution dear Dr Schwincher Fernandance
BACCO is an R bundle for Bayesian analysis of random functions. BACCO contains three sub-packages: emulator, calibrator, and approximator, that perform Bayesian emulation and calibration of computer programs
MCMCpack provides model-specific Markov chain Monte Carlo (MCMC) algorithms for wide range of models commonly used in the social and behavioral sciences. It contains R functions to fit a number of regression models (linear regression, logit, ordinal probit, probit, Poisson regression, etc.), measurement models (item response theory and factor models), changepoint models (linear regression, binary probit, ordinal probit, Poisson, panel), and models for ecological inference. It also contains a generic Metropolis sampler that can be used to fit arbitrary models
Continuous data can take on any numeric value, and it can be meaningfully divided into smaller increments, including fractional and decimal values.
acebayes finds optimal Bayesian experimental design using the approximate coordinate exchange (ACE) algorithm.
AdMit provides functions to perform the fitting of an adapative mixture of Student-t distributions to a target density through its kernel function. The mixture approximation can be used as the importance density in importance sampling or as the candidate density in the Metropolis-Hastings algorithm
The BaBooN package contains two variants of Bayesian Bootstrap Predictive Mean Matching to multiply impute missing data.
bamlss provides an infrastructure for estimating probabilistic distributional regression models in a Bayesian framework. The distribution parameters may capture location, scale, shape, etc. and every parameter may depend on complex additive terms similar to a generalized additive model
The differences in the graphs might be caused by random sample error rather than an actual difference between production methods.
The BART package provide flexible nonparametric modeling of covariates for continuous, binary, categorical and time-to-event outcomes.
BAS is a package for Bayesian Variable Selection and Model Averaging in linear models and generalized linear models using stochastic or deterministic sampling without replacement from posterior distributions. Prior distributions on coefficients are from Zellner's g-prior or mixtures of g-priors corresponding to the Zellner-Siow Cauchy Priors or the mixture of g-priors for linear models or mixtures of g-priors in generalized linear models
It can be a costly mistake to base decisions on “results” that vary with each sample. Hypothesis tests factor in random error to improve our chances of making correct decisions.
The bayesGARCH package provides a function which perform the Bayesian estimation of the GARCH(1,1) model with Student's t innovations.
Bergm performs Bayesian analysis for exponential random graph models using advanced computational algorithms
BLR provides R functions to fit parametric regression models using different types of shrinkage methods
In clinical practice it is helpful to label individuals as having or not having an attribute, such as being “hypertensive” or “obese” or having ”high cholesterol,” depending on the value of a continuous variable.
BMS is Bayesian Model Averaging library for linear models with a wide choice of (customizable) priors. Built-in priors include coefficient priors (fixed, flexible and hyper-g priors), and 5 kinds of model priors
Bmix is a bare-bones implementation of sampling algorithms for a variety of Bayesian stick-breaking (marginally DP) mixture models, including particle learning and Gibbs sampling for static DP mixtures, particle learning for dynamic BAR stick-breaking, and DP mixture regression
bnlearn is a package for Bayesian network structure learning (via constraint-based, score-based and hybrid algorithms), parameter learning (via ML and Bayesian estimators) and inference
- bsts is a package for time series regression using dynamic linear models using MCMC.
- BVAR is a package for estimating hierarchical Bayesian vector autoregressive models
- causact provides R functions for visualizing and running inference on generative directed acyclic graphs (DAGs). Once a generative DAG is created, the package automates Bayesian inference via the greta package and TensorFlow .
- coalescentMCMC provides a flexible framework for coalescent analyses in R.
- conting performs Bayesian analysis of complete and incomplete contingency tables