Estimation and interpretation of Bayesian distributed lag interaction models (BDLIMs). A BDLIM regresses a scalar outcome on repeated measures of exposure and allows for modification by a categorical variable under four specific patterns of modification. The main function is bdlim(). There are also summary and plotting files. Details on methodology are described in Wilson et al. (2017) <doi:10.1093/biostatistics/kxx002>.

This package provides a set of functions for doing analysis of A/B split test data and web metrics in general.

This package provides a Bayesian smoothing method for post-processing of remote sensing image classification which refines the labelling in a classified image in order to enhance its classification accuracy. Combines pixel-based classification methods with a spatial post-processing method to remove outliers and misclassified pixels.

Enables off-the-shelf functionality for fully Bayesian, nonstationary Gaussian process modeling. The approach to nonstationary modeling involves a closed-form, convolution-based covariance function with spatially-varying parameters; these parameter processes can be specified either deterministically (using covariates or basis functions) or stochastically (using approximate Gaussian processes). Stationary Gaussian processes are a special case of our methodology, and we furthermore implement approximate Gaussian process inference to account for very large spatial data sets (Finley, et al (2017) <doi:10.48550/arXiv.1702.00434>). Bayesian inference is carried out using Markov chain Monte Carlo methods via the "nimble" package, and posterior prediction for the Gaussian process at unobserved locations is provided as a post-processing step.

This package provides functions to construct efficient block designs for 3-level factorial experiments in block size 3. The designs ensure the estimation of all main effects and two-factor interactions in minimum number of replications. For more details, see Dey and Mukerjee (2012) <doi:10.1016/j.spl.2012.06.014> and Dash, S., Parsad, R. and Gupta, V.K. (2013) <doi:10.1007/s40003-013-0059-5>.

Computations for Bessel function for complex, real and partly mpfr (arbitrary precision) numbers; notably interfacing TOMS 644; approximations for large arguments, experiments, etc.

Bayes factors and posterior probabilities in Linear models, aimed at provide a formal Bayesian answer to testing and variable selection problems.

This package provides a two-step Bayesian approach for mode inference (BaŠtürk et al., 2026) <doi:10.18637/jss.v116.i03>. First, a mixture distribution is fitted on the data using a sparse finite mixture (SFM) Markov chain Monte Carlo (MCMC) algorithm. The number of mixture components does not have to be known; the size of the mixture is estimated endogenously through the SFM approach. Second, the modes of the estimated mixture at each MCMC draw are retrieved using algorithms specifically tailored for mode detection. These estimates are then used to construct posterior probabilities for the number of modes, their locations and uncertainties, providing a powerful tool for mode inference.

This package implements bidirectional two-stage least squares (Bi-TSLS) estimation for identifying bidirectional causal effects between two variables in the presence of unmeasured confounding. The method uses proxy variables (negative control exposure and outcome) along with at least one covariate to handle confounding.

Bayesian estimations of a covariance matrix for multivariate normal data. Assumes that the covariance matrix is sparse or band matrix and positive-definite. Methods implemented include the beta-mixture shrinkage prior (Lee et al. (2022) <doi:10.1016/j.jmva.2022.105067>), screened beta-mixture prior (Lee et al. (2024) <doi:10.1214/24-BA1495>), and post-processed posteriors for banded and sparse covariances (Lee et al. (2023) <doi:10.1214/22-BA1333>; Lee and Lee (2023) <doi:10.1016/j.jeconom.2023.105475>). This software has been developed using funding supported by Basic Science Research Program through the National Research Foundation of Korea ('NRF') funded by the Ministry of Education ('RS-2023-00211979', NRF-2022R1A5A7033499', NRF-2020R1A4A1018207 and NRF-2020R1C1C1A01013338').

Nonparametric detection of nonuniformity and dependence with Binary Expansion Testing (BET). See Kai Zhang (2019) BET on Independence, Journal of the American Statistical Association, 114:528, 1620-1637, <DOI:10.1080/01621459.2018.1537921>, Kai Zhang, Wan Zhang, Zhigen Zhao, Wen Zhou. (2023). BEAUTY Powered BEAST, <doi:10.48550/arXiv.2103.00674> and Wan Zhang, Zhigen Zhao, Michael Baiocchi, Yao Li, Kai Zhang. (2023) SorBET: A Fast and Powerful Algorithm to Test Dependence of Variables, Techinical report.

This package provides an interface to Bank of Japan <https://www.boj.or.jp> statistics.

This package provides functions for performing the Bayesian bootstrap as introduced by Rubin (1981) <doi:10.1214/aos/1176345338> and for summarizing the result. The implementation can handle both summary statistics that works on a weighted version of the data and summary statistics that works on a resampled data set.

Fit (using Bayesian methods) and simulate mixtures of univariate and bivariate angular distributions. Chakraborty and Wong (2021) <doi:10.18637/jss.v099.i11>.

This package provides a toolbox for analyzing and simulating large networks based on hierarchical exponential-family random graph models (HERGMs).'bigergm implements the estimation for large networks efficiently building on the lighthergm and hergm packages. Moreover, the package contains tools for simulating networks with local dependence to assess the goodness-of-fit.

Fit and simulate bivariate correlated frailty models with proportional hazard structure. Frailty distributions, such as gamma and lognormal models are supported semiparametric procedures. Frailty variances of the two subjects can be varied or equal. Details on the models are available in book of Wienke (2011,ISBN:978-1-4200-7388-1). Bivariate gamma fit is obtained using the approach given in Kifle et al (2023) <DOI: 10.4310/22-SII738> with modifications. Lognormal fit is based on the approach by Ripatti and Palmgren (2000) <doi:10.1111/j.0006-341X.2000.01016.x>. Frailty distributions, such as gamma, inverse gaussian and power variance frailty models are supported for parametric approach.

Fast Bayesian estimation and forecasting of age-specific rates, probabilities, and means, based on Template Model Builder'.

This package provides several methods for generating density functions based on binned data. Methods include step function, recursive subdivision, and optimized spline. Data are assumed to be nonnegative, the top bin is assumed to have no upper bound, but the bin widths need be equal. All PDF smoothing methods maintain the areas specified by the binned data. (Equivalently, all CDF smoothing methods interpolate the points specified by the binned data.) In practice, an estimate for the mean of the distribution should be supplied as an optional argument. Doing so greatly improves the reliability of statistics computed from the smoothed density functions. Includes methods for estimating the Gini coefficient, the Theil index, percentiles, and random deviates from a smoothed distribution. Among the three methods, the optimized spline (splinebins) is recommended for most purposes. The percentile and random-draw methods should be regarded as experimental, and these methods only support splinebins.

This package provides fast and efficient procedures for Bayesian analysis of Structural Vector Autoregressions. This package estimates a wide range of models, including homo-, heteroskedastic, and non-normal specifications. Structural models can be identified by adjustable exclusion restrictions, time-varying volatility, or non-normality. They all include a flexible three-level equation-specific local-global hierarchical prior distribution for the estimated level of shrinkage for autoregressive and structural parameters. Additionally, the package facilitates predictive and structural analyses such as impulse responses, forecast error variance and historical decompositions, forecasting, verification of heteroskedasticity, non-normality, and hypotheses on autoregressive parameters, as well as analyses of structural shocks, volatilities, and fitted values. Beautiful plots, informative summary functions, and extensive documentation including the vignette by Woźniak (2024) <doi:10.48550/arXiv.2410.15090> complement all this. The implemented techniques align closely with those presented in Lütkepohl, Shang, Uzeda, & Woźniak (2024) <doi:10.48550/arXiv.2404.11057>, Lütkepohl & Woźniak (2020) <doi:10.1016/j.jedc.2020.103862>, and Song & Woźniak (2021) <doi:10.1093/acrefore/9780190625979.013.174>. The bsvars package is aligned regarding objects, workflows, and code structure with the R package bsvarSIGNs by Wang & Woźniak (2024) <doi:10.32614/CRAN.package.bsvarSIGNs>, and they constitute an integrated toolset.

Fits double generalized Gamma regression models from a Bayesian perspective, where both the mean and shape parameters are modeled simultaneously using flexible link functions. The methodology is based on Cepeda-Cuervo and Urdinola (2012) <doi:10.1080/03610918.2011.600500> and extended in Cepeda-Cuervo (2026), Double Generalized Linear Models: Likelihood and Bayesian Methods (ISBN: 9781041169970). The package provides parameter estimation, model fitting, and model comparison tools, including Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC).

This package provides Bayesian estimation and forecasting of dynamic panel data using Bayesian Panel Vector Autoregressions with hierarchical prior distributions following the specification by Sanchez-Martinez & Woźniak (2026) <doi:10.48550/arXiv.2606.14143>. The models include country-specific Vector Autoregressions (VARs) that share a global prior distribution that extend the model by JarociÅ ski (2010) <doi:10.1002/jae.1082>. Under this prior expected value, each country's system follows a global VAR with country-invariant parameters. Further flexibility is provided by the hierarchical prior structure that retains the Minnesota prior interpretation for the global VAR and features estimated prior covariance matrices, shrinkage, and persistence levels. Bayesian forecasting is developed for models including exogenous variables, allowing conditional forecasts given the future trajectories of some variables and restricted forecasts assuring that rates are forecasted to stay positive and less than 100. The package implements the model specification, estimation, and forecasting routines, facilitating coherent workflows and reproducibility. It also includes automated pseudo-out-of-sample forecasting and computation of forecasting performance measures. Beautiful plots, informative summary functions, and extensive documentation complement all this. Extraordinary computational speed is achieved thanks to employing frontier econometric and numerical techniques and algorithms written in C++'. The bpvars package is aligned regarding objects, workflows, and code structure with the R packages bsvars by Woźniak (2024) <doi:10.32614/CRAN.package.bsvars>, bsvarSIGNs by Wang & Woźniak (2025) <doi:10.32614/CRAN.package.bsvarSIGNs>, and bvars by Liu, Ramirez Hassan, & Woźniak (2026) <doi:10.32614/CRAN.package.bvars> and they constitute an integrated toolset. Copyright: 2025 International Labour Organization. The International Labour Organization should not be held responsible for any issues arising from the use of the bpvars package or from the results obtained with it.

This package provides functions that allow users to quantify the relative contributions of geographic and ecological distances to empirical patterns of genetic differentiation on a landscape. Specifically, we use a custom Markov chain Monte Carlo (MCMC) algorithm, which is used to estimate the parameters of the inference model, as well as functions for performing MCMC diagnosis and assessing model adequacy.

This package implements methods for building and analyzing models based on panel data as described in the paper by Moral-Benito (2013, <doi:10.1080/07350015.2013.818003>). The package provides functions to estimate dynamic panel data models and analyze the results of the estimation.

Fork-safe, raw access to the Amazon Web Services ('AWS') SDK via the boto3 Python module, and convenient helper functions to query the Simple Storage Service ('S3') and Key Management Service ('KMS'), partial support for IAM', the Systems Manager Parameter Store and Secrets Manager'.