Best subset glm using information criteria or cross-validation, carried by using leaps algorithm (Furnival and Wilson, 1974) <doi:10.2307/1267601> or complete enumeration (Morgan and Tatar, 1972) <doi:10.1080/00401706.1972.10488918>. Implements PCR and PLS using AIC/BIC. Implements one-standard deviation rule for use with the caret package.

Package provides functions for estimation and inference in Bayesian quantile regression with ordinal outcomes. An ordinal model with 3 or more outcomes (labeled OR1 model) is estimated by a combination of Gibbs sampling and Metropolis-Hastings (MH) algorithm. Whereas an ordinal model with exactly 3 outcomes (labeled OR2 model) is estimated using a Gibbs sampling algorithm. The summary output presents the posterior mean, posterior standard deviation, 95% credible intervals, and the inefficiency factors along with the two model comparison measures â logarithm of marginal likelihood and the deviance information criterion (DIC). The package also provides functions for computing the covariate effects and other functions that aids either the estimation or inference in quantile ordinal models. Rahman, M. A. (2016).â Bayesian Quantile Regression for Ordinal Models.â Bayesian Analysis, 11(1): 1-24 <doi: 10.1214/15-BA939>. Yu, K., and Moyeed, R. A. (2001). â Bayesian Quantile Regression.â Statistics and Probability Letters, 54(4): 437â 447 <doi: 10.1016/S0167-7152(01)00124-9>. Koenker, R., and Bassett, G. (1978).â Regression Quantiles.â Econometrica, 46(1): 33-50 <doi: 10.2307/1913643>. Chib, S. (1995). â Marginal likelihood from the Gibbs output.â Journal of the American Statistical Association, 90(432):1313â 1321, 1995. <doi: 10.1080/01621459.1995.10476635>. Chib, S., and Jeliazkov, I. (2001). â Marginal likelihood from the Metropolis-Hastings output.â Journal of the American Statistical Association, 96(453):270â 281, 2001. <doi: 10.1198/016214501750332848>.

This package provides a collection of functions to analyse, visualize and interpret wind data and to calculate the potential energy production of wind turbines.

Calculates the Boltzmann entropy of a landscape gradient. This package uses the analytical method created by Gao, P., Zhang, H. and Li, Z., 2018 (<doi:10.1111/tgis.12315>) and by Gao, P. and Li, Z., 2019 (<doi:10.1007/s10980-019-00854-3>). It also extend the original ideas by allowing calculations on data with missing values.

This package provides a computational toolbox designed for handling missing values in large datasets with the Multiple Imputation by Chained Equations (MICE) by using Apache Spark'. The methodology is described in Morvan et al. (2026) <doi:10.48550/arXiv.2601.21613>.

An implementation of the bridge distribution with logit-link in R. In Wang and Louis (2003) <DOI:10.1093/biomet/90.4.765>, such a univariate bridge distribution was derived as the distribution of the random intercept that bridged a marginal logistic regression and a conditional logistic regression. The conditional and marginal regression coefficients are a scalar multiple of each other. Such is not the case if the random intercept distribution was Gaussian.

This package provides a Gibbs sampler algorithm was developed to estimate change points in constant-wise data sequences while performing clustering simultaneously. The algorithm is described in da Cruz, A. C. and de Souza, C. P. E "A Bayesian Approach for Clustering Constant-wise Change-point Data" <doi:10.48550/arXiv.2305.17631>.

This package contains data and code to accompany the book P. Zuccolotto and M. Manisera (2020) Basketball Data Science. Applications with R. CRC Press. ISBN 9781138600799.

This package creates plots showing scored HR experiments and plots of distribution of means of ranks of HR score from bootstrapping.

This package provides a modern view on the principal component analysis biplot with calibrated axes. Create principal component analysis biplots rendered in HTML with significant reactivity embedded within the plot. Furthermore, the traditional biplot view is enhanced by translated axes with inter-class kernel densities superimposed. For more information on biplots, see Gower, J.C., Lubbe, S. and le Roux, N.J. (2011, ISBN: 978-0-470-01255-0).

Bell regression models for count data with overdispersion. The implemented models account for ordinary and zero-inflated regression models under both frequentist and Bayesian approaches. Theoretical details regarding the models implemented in the package can be found in Castellares et al. (2018) <doi:10.1016/j.apm.2017.12.014> and Lemonte et al. (2020) <doi:10.1080/02664763.2019.1636940>.

This package implements fast, exact bootstrap Principal Component Analysis and Singular Value Decompositions for high dimensional data, as described in <doi:10.1080/01621459.2015.1062383> (see also <doi:10.48550/arXiv.1405.0922>). For data matrices that are too large to operate on in memory, users can input objects with class ff (see the ff package), where the actual data is stored on disk. In response, this package will implement a block matrix algebra procedure for calculating the principal components (PCs) and bootstrap PCs. Depending on options set by the user, the parallel package can be used to parallelize the calculation of the bootstrap PCs.

Download typicality rating datasets, generate new stereotype-based typicality ratings using large language models via the Inference Providers API (<https://huggingface.co/docs/inference-providers>), and evaluate them against human-annotated validation data. Also includes functions to extract stereotype strength and base-rate items from typicality matrices. For more details see Beucler et al. (2025) <doi:10.31234/osf.io/eqrfu_v1>.

Computes Bayesian posterior distributions of predictions, marginal effects, and differences of marginal effects for various generalized linear models. Importantly, the posteriors are on the mean (response) scale, allowing for more natural interpretation than summaries on the link scale. Also, predictions and marginal effects of the count probabilities for Poisson and negative binomial models can be computed.

Fetches zonal statistics from weather indicators that were calculated for each municipality in Brazil using data from the BR-DWGD and TerraClimate projects. Zonal statistics such as mean, maximum, minimum, standard deviation, and sum were computed by taking into account the data cells that intersect the boundaries of each municipality and stored in Parquet files. This procedure was carried out for all Brazilian municipalities, and for all available dates, for every indicator available in the weather products (BR-DWGD and TerraClimate projects). This package queries on-line the already calculated statistics on the Parquet files and returns easy-to-use data.frames.

Estimates survival and mortality with covariates from census or capture-recapture/recovery data in a Bayesian framework when many individuals are of unknown age. It includes tools for data checking, model diagnostics and outputs such as life-tables and plots, as described in Colchero, Jones, and Rebke (2012) <doi:10.1111/j.2041-210X.2012.00186.x> and Colchero et al. (2021) <doi:10.1038/s41467-021-23894-3>.

This package provides functions to download and tidy statistical data published by the Bank of England <https://www.bankofengland.co.uk>. Covers Bank Rate, SONIA', gilt yields, exchange rates, mortgage rates, mortgage approvals, consumer credit, and money supply. Series are fetched from the Bank of England Interactive Statistical Database using its CSV endpoint. Data is cached locally between sessions.

Bland-Altman Plots using either base graphics or ggplot2, augmented with confidence intervals, with detailed return values and a sunflowerplot option for data with ties.

Create randomizations for block random clinical trials. Can also produce a pdf file of randomization cards.

Data files and functions accompanying the book Korner-Nievergelt, Roth, von Felten, Guelat, Almasi, Korner-Nievergelt (2015) "Bayesian Data Analysis in Ecology using R, BUGS and Stan", Elsevier, New York.

Bindings to badgen <https://www.npmjs.com/package/badgen> to generate beautiful svg badges in R without internet access. Images can be converted to png using the rsvg package as shown in examples.

Run other estimation and simulation software via the nlmixr2 (Fidler et al (2019) <doi:10.1002/psp4.12445>) interface including PKNCA', NONMEM and Monolix'. While not required, you can get/install the lixoftConnectors package in the Monolix installation, as described at the following url <https://monolixsuite.slp-software.com/r-functions/2024R1/installation-and-initialization>. When lixoftConnectors is available, Monolix can be run directly instead of setting up command line usage.

This package provides a lightweight package to access spatial basemaps from open sources such as OpenStreetMap', Carto', Mapbox and others in R.

This package provides methods for choosing the rank of an SVD (singular value decomposition) approximation via cross validation. The package provides both Gabriel-style "block" holdouts and Wold-style "speckled" holdouts. It also includes an implementation of the SVDImpute algorithm. For more information about Bi-cross-validation, see Owen & Perry's 2009 AoAS article (at <arXiv:0908.2062>) and Perry's 2009 PhD thesis (at <arXiv:0909.3052>).