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            "name": "R-baggr",
            "description": "Bayesian aggregate treatment effects"
        },
        {
            "name": "R-baguette",
            "description": "Efficient model functions for bagging"
        },
        {
            "name": "R-bain",
            "description": "Bayes Factors for Informative hypotheses"
        },
        {
            "name": "R-baizer",
            "description": "Useful functions for data processing"
        },
        {
            "name": "R-bamlss",
            "description": "Bayesian additive models for location, scale and shape"
        },
        {
            "name": "R-BAMMtools",
            "description": "Analysis and visualization of macroevolutionary dynamics on phylogenetic trees"
        },
        {
            "name": "R-BANAM",
            "description": "Bayesian Analysis of the Network Autocorrelation Model"
        },
        {
            "name": "R-bandit",
            "description": "Functions for simple a/b split test and multi-armed bandit analysis"
        },
        {
            "name": "R-bang",
            "description": "Bayesian Analysis, No Gibbs"
        },
        {
            "name": "R-bannerCommenter",
            "description": "Make banner comments with a consistent format"
        },
        {
            "name": "R-BANOVA",
            "description": "Hierarchical Bayesian ANOVA models"
        },
        {
            "name": "R-bark",
            "description": "Bayesian Additive Regression Kernels"
        },
        {
            "name": "R-BART",
            "description": "Bayesian Additive Regression Trees"
        },
        {
            "name": "R-bartBMA",
            "description": "Bayesian Additive Regression Trees using Bayesian Model Averaging"
        },
        {
            "name": "R-bartCause",
            "description": "Causal inference using Bayesian Additive Regression Trees"
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        {
            "name": "R-bartcs",
            "description": "Bayesian Additive Regression Trees for Confounder Selection"
        },
        {
            "name": "R-BAS",
            "description": "Bayesian variable selection and model averaging via Bayesian adaptive sampling"
        },
        {
            "name": "R-basad",
            "description": "Bayesian variable selection with shrinking and diffusing priors"
        },
        {
            "name": "R-base64enc",
            "description": "Tools for base64 encoding"
        },
        {
            "name": "R-base64url",
            "description": "Fast and URL-safe Base64 encoder/decoder"
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        {
            "name": "R-basefun",
            "description": "Infrastructure for computing with basis functions"
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        {
            "name": "R-basemodels",
            "description": "Baseline models for classification and regression"
        },
        {
            "name": "R-BaseSet",
            "description": "Work with sets the tidy way"
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        {
            "name": "R-basicMCMCplots",
            "description": "Trace plots, density plots and chain comparisons for MCMC samples"
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        {
            "name": "R-BASS",
            "description": "Bayesian Adaptive Spline Surfaces"
        },
        {
            "name": "R-BatchJobs",
            "description": "Batch computing with R"
        },
        {
            "name": "R-batchmeans",
            "description": "Consistent batch means estimation of Monte Carlo standard errors"
        },
        {
            "name": "R-batchtools",
            "description": "Tools for computation on batch systems"
        },
        {
            "name": "R-baycn",
            "description": "Bayesian inference for causal networks"
        },
        {
            "name": "R-bayefdr",
            "description": "Bayesian estimation and optimisation of expected FDR and expected FNR"
        },
        {
            "name": "R-bayes4psy",
            "description": "User-friendly Bayesian data analysis for psychology"
        },
        {
            "name": "R-bayesAB",
            "description": "Fast Bayesian methods for A/B testing"
        },
        {
            "name": "R-bayesammi",
            "description": "Bayesian estimation of the Additive Main effects and Multiplicative Interaction model"
        },
        {
            "name": "R-bayesanova",
            "description": "Bayesian inference in the Analysis of Variance via Markov Chain Monte Carlo in gaussian mixture models"
        },
        {
            "name": "R-BayesBinMix",
            "description": "Bayesian estimation of mixtures of multivariate Bernoulli distributions"
        },
        {
            "name": "R-BayesBP",
            "description": "Bayesian estimation using Bernstein polynomial fits rate matrix"
        },
        {
            "name": "R-bayesbr",
            "description": "Bayesian Beta regression in R"
        },
        {
            "name": "R-bayescopulareg",
            "description": "Bayesian copula regression"
        },
        {
            "name": "R-bayescount",
            "description": "Power calculations and Bayesian analysis of count distributions and FECRT Data using MCMC"
        },
        {
            "name": "R-BayesDA",
            "description": "Functions and datasets for Bayesian Data Analysis (2nd ed.)"
        },
        {
            "name": "R-bayesDccGarch",
            "description": "Methods and tools for Bayesian analysis of DCC-GARCH(1,1) model"
        },
        {
            "name": "R-BayesDesign",
            "description": "Bayesian single-arm design with survival endpoints"
        },
        {
            "name": "R-bayesdfa",
            "description": "Bayesian Dynamic Factor Analysis (DFA) with Stan"
        },
        {
            "name": "R-BayesESS",
            "description": "Determines effective sample size of a parametric prior distribution in Bayesian models"
        },
        {
            "name": "R-BayesFactor",
            "description": "Computation of Bayes factors for common designs"
        },
        {
            "name": "R-bayesforecast",
            "description": "Bayesian time series modeling with Stan"
        },
        {
            "name": "R-bayesGAM",
            "description": "Multivariate response generalized additive models using Hamiltonian Monte Carlo"
        },
        {
            "name": "R-bayesGARCH",
            "description": "Bayesian estimation of the GARCH(1,1) model with Student-t innovations"
        },
        {
            "name": "R-BayesGOF",
            "description": "Bayesian modeling via frequentist goodness-of-fit"
        },
        {
            "name": "R-bayesian",
            "description": "Bindings for Bayesian TidyModels"
        }
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}