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        {
            "name": "R-blindreview",
            "description": "Blind review using forward search procedures"
        },
        {
            "name": "R-blink",
            "description": "Record linkage for empirically motivated priors"
        },
        {
            "name": "R-bliss",
            "description": "Bayesian functional linear regression with sparse step functions"
        },
        {
            "name": "R-blme",
            "description": "Bayesian Linear Mixed Effect models"
        },
        {
            "name": "R-blob",
            "description": "Simple S3 class for representing vectors of binary data"
        },
        {
            "name": "R-blockcluster",
            "description": "Co-clustering package for binary, categorical, contingency and continuous data-sets"
        },
        {
            "name": "R-BlockCov",
            "description": "Estimation of large block covariance matrices"
        },
        {
            "name": "R-blockmodels",
            "description": "Latent and stochastic block model estimation by a V-EM algorithm"
        },
        {
            "name": "R-BLSM",
            "description": "Bayesian Latent Space Model"
        },
        {
            "name": "R-bltm",
            "description": "Bayesian Latent Threshold Modelling"
        },
        {
            "name": "R-bluster",
            "description": "Clustering algorithms for Bioconductor"
        },
        {
            "name": "R-BMA",
            "description": "Bayesian Model Averaging"
        },
        {
            "name": "R-bmass",
            "description": "Bayesian Multivariate Analysis of Summary Statistics"
        },
        {
            "name": "R-bmemLavaan",
            "description": "Mediation analysis with missing data and non-normal data"
        },
        {
            "name": "R-bmgarch",
            "description": "Bayesian multivariate GARCH models"
        },
        {
            "name": "R-BMisc",
            "description": "Miscellaneous functions for panel data, quantiles and printing results"
        },
        {
            "name": "R-bmlm",
            "description": "Bayesian multilevel mediation models in R"
        },
        {
            "name": "R-bmp",
            "description": "Read Windows Bitmap (BMP) images"
        },
        {
            "name": "R-BMRMM",
            "description": "Implementation of the Bayesian Markov (Renewal) Mixed Models"
        },
        {
            "name": "R-BMS",
            "description": "Bayesian model averaging library"
        },
        {
            "name": "R-BMT",
            "description": "Bezier–Montenegro–Torres distribution"
        },
        {
            "name": "R-BMTAR",
            "description": "Bayesian approach for MTAR models with missing data"
        },
        {
            "name": "R-bnclassify",
            "description": "Learning discrete Bayesian network classifiers from data"
        },
        {
            "name": "R-bnlearn",
            "description": "Bayesian network structure learning, parameter learning and inference"
        },
        {
            "name": "R-bnma",
            "description": "Bayesian Network Meta-Analysis using JAGS"
        },
        {
            "name": "R-bnmonitor",
            "description": "Implementation of sensitivity analysis in Bayesian networks"
        },
        {
            "name": "R-bnmr",
            "description": "Bayesian Non-parametric Monotone Regression"
        },
        {
            "name": "R-bnmr2",
            "description": "R package to conduct causal estimation between exposure and outcome on GWAS data"
        },
        {
            "name": "R-bnpmr",
            "description": "Bayesian Monotonic Non-parametric Regression"
        },
        {
            "name": "R-BNSL",
            "description": "Bayesian network structure learning"
        },
        {
            "name": "R-BNSP",
            "description": "Bayesian non- and semi-parametric model fitting"
        },
        {
            "name": "R-bnstruct",
            "description": "Bayesian network structure learning from data with missing values"
        },
        {
            "name": "R-boinet",
            "description": "Conduct simulation study of Bayesian optimal interval design with BOIN-ET family"
        },
        {
            "name": "R-bolasso",
            "description": "Model-consistent lasso estimation via bootstrap"
        },
        {
            "name": "R-Bolstad",
            "description": "Functions for elementary Bayesian inference"
        },
        {
            "name": "R-Bolstad2",
            "description": "Bolstad Functions"
        },
        {
            "name": "R-bonsai",
            "description": "Model wrappers for tree-based models"
        },
        {
            "name": "R-boodist",
            "description": "Make some distributions from the Boost C++ library available in R"
        },
        {
            "name": "R-bookdown",
            "description": "Authoring books and technical documents with R markdown"
        },
        {
            "name": "R-BoolNet",
            "description": "Construction, simulation and analysis of Boolean networks"
        },
        {
            "name": "R-Boom",
            "description": "Bayesian Object-Oriented Modelling"
        },
        {
            "name": "R-BoomSpikeSlab",
            "description": "MCMC for Spike and Slab regression"
        },
        {
            "name": "R-boostrq",
            "description": "Boosting Regression Quantiles"
        },
        {
            "name": "R-boot",
            "description": "Functions and datasets for bootstrapping"
        },
        {
            "name": "R-boot.pval",
            "description": "Bootstrap p-values"
        },
        {
            "name": "R-bootCT",
            "description": "Bootstrap ARDL tests for cointegration"
        },
        {
            "name": "R-bootES",
            "description": "Bootstrap confidence intervals on effect sizes"
        },
        {
            "name": "R-BootPR",
            "description": "Bootstrap prediction intervals and bias-corrected forecasting"
        },
        {
            "name": "R-bootStepAIC",
            "description": "Model selection by bootstrapping the stepAIC() procedure"
        },
        {
            "name": "R-bootstrap",
            "description": "Functions for An Introduction to the Bootstrap"
        }
    ]
}