Offers a flexible formula-based interface for building and training Bayesian Neural Networks powered by Stan'. The package supports modeling complex relationships while providing rigorous uncertainty quantification via posterior distributions. With features like user chosen priors, clear predictions, and support for regression, binary, and multi-class classification, it is well-suited for applications in clinical trials, finance, and other fields requiring robust Bayesian inference and decision-making. References: Neal(1996) <doi:10.1007/978-1-4612-0745-0>.

This package implements functions for multi-class discriminant analysis using binary predictors, for corresponding variable selection, and for dichotomizing continuous data.

The method models RNA-seq reads using a mixture of 3 beta-binomial distributions to generate posterior probabilities for genotyping bi-allelic single nucleotide polymorphisms. Elena Vigorito, Anne Barton, Costantino Pitzalis, Myles J. Lewis and Chris Wallace (2023) <doi:10.1093/bioinformatics/btad393> "BBmix: a Bayesian beta-binomial mixture model for accurate genotyping from RNA-sequencing.".

BRIC-seq is a genome-wide approach for determining RNA stability in mammalian cells. This package provides a series of functions for performing quality check of your BRIC-seq data, calculation of RNA half-life for each transcript and comparison of RNA half-lives between two conditions.

This package provides tools for downloading historical financial data from the www.belex.rs.

The blocked weighted bootstrap (BBW) is an estimation technique for use with data from two-stage cluster sampled surveys in which either prior weighting (e.g. population-proportional sampling or PPS as used in Standardized Monitoring and Assessment of Relief and Transitions or SMART surveys) or posterior weighting (e.g. as used in rapid assessment method or RAM and simple spatial sampling method or S3M surveys) is implemented. See Cameron et al (2008) <doi:10.1162/rest.90.3.414> for application of bootstrap to cluster samples. See Aaron et al (2016) <doi:10.1371/journal.pone.0163176> and Aaron et al (2016) <doi:10.1371/journal.pone.0162462> for application of the blocked weighted bootstrap to estimate indicators from two-stage cluster sampled surveys.

Bounded continuous data are encountered in many areas of test application. Examples include visual analogue scales used in the measurement of personality, mood, depression, and quality of life; item response times from tests with item deadlines; confidence ratings; and pain intensity ratings. Using this package, item response theory (IRT) models suitable for bounded continuous item scores can be fitted to data within a Bayesian framework. The package draws on posterior sampling facilities provided by R-package rstan (Stan Development Team, 2025)<https://mc-stan.org/>. Available models include the Beta IRT model by Noel and Dauvier (2007)<doi:10.1177/0146621605287691>, the continuous response model by Samejima (1973)<doi:10.1007/BF03372160>, the unbounded normal model by Mellenbergh (1994)<doi:10.1207/s15327906mbr2903_2>, and the Simplex IRT model by Flores et al. (2020)<doi:10.1007/978-3-030-43469-4_8>. All models can be fitted with or without zero-one inflation (Molenaar et al., 2022)<doi:10.3102/10769986221108455>. Model fit comparisons can be conducted using the Watanabe-Akaike information criterion (WAIC), leave-one-out cross-validation information citerion (LOOIC) and the fully marginalized likelihood (i.e., Bayes factors).

This package implements Bayesian response-adaptive randomization methods based on Bayesian hypothesis testing for multi-arm settings (Pawel and Held, 2025, <doi:10.48550/arXiv.2510.01734>).

This package provides pathwise estimation of regularized logistic propensity score models using covariate balancing loss functions rather than maximum likelihood. Regularization paths are fit via the adelie elastic-net solver with a glmnet'-like interface, yielding balancing weights that target covariate balance for the ATE and ATT. Under lasso penalization, lambda bounds the maximum covariate imbalance, so the regularization path traces a sequence of decreasing imbalance tolerances. For details, see Sverdrup & Hastie (2026) <doi:10.48550/arXiv.2602.18577>.

Permutational method to incorporate taxonomic uncertainty and some functions to assess its effects on parameters of some widely used multivariate methods in ecology, as explained in Cayuela et al. (2011) <doi:10.1111/j.1600-0587.2009.05899.x>.

This package provides a class of Bayesian beta regression models for the analysis of continuous data with support restricted to an unknown finite support. The response variable is modeled using a four-parameter beta distribution with the mean or mode parameter depending linearly on covariates through a link function. When the response support is known to be (0,1), the above class of models reduce to traditional (0,1) supported beta regression models. Model choice is carried out via the logarithm of the pseudo marginal likelihood (LPML), the deviance information criterion (DIC), and the Watanabe-Akaike information criterion (WAIC). See Zhou and Huang (2022) <doi:10.1016/j.csda.2021.107345>.

Designed to simplify the process of retrieving datasets from the Big Data PE platform using secure token-based authentication. It provides functions for securely storing, retrieving, and managing tokens associated with specific datasets, as well as fetching and processing data using the httr2 package.

Extends blockr.core with interactive blocks for reading and writing data files. Supports CSV, Excel, Parquet, RDS, and other formats through a graphical interface without writing code directly. Includes file browser integration and configurable import/export options.

This package provides a fully Bayesian approach in order to estimate a general family of cure rate models under the presence of covariates, see Papastamoulis and Milienos (2024) <doi:10.1007/s11749-024-00942-w> and Papastamoulis and Milienos (2024b) <doi:10.48550/arXiv.2409.10221>. The promotion time can be modelled (a) parametrically using typical distributional assumptions for time to event data (including the Weibull, Exponential, Gompertz, log-Logistic distributions), or (b) semiparametrically using finite mixtures of distributions. In both cases, user-defined families of distributions are allowed under some specific requirements. Posterior inference is carried out by constructing a Metropolis-coupled Markov chain Monte Carlo (MCMC) sampler, which combines Gibbs sampling for the latent cure indicators and Metropolis-Hastings steps with Langevin diffusion dynamics for parameter updates. The main MCMC algorithm is embedded within a parallel tempering scheme by considering heated versions of the target posterior distribution.

This package provides functions to query, retrieve, and tidy economic and financial data from Brazilian Central Bank web services for use in R analyses and workflows. Active institutions information, balance sheets and normative acts.

This package provides tools to create binary dosage files from either VCF or GEN files, merge binary dosage files, and read binary dosage files. Binary dosage files tend to have quicker read times than VCF and GEN formats. There is a small increase in size compared to compressed VCF and GEN files.

Computes approximated adjusted fractional Bayes factors for equality, inequality, and about equality constrained hypotheses. For a tutorial on this method, see Hoijtink, Mulder, van Lissa, & Gu, (2019) <doi:10.1037/met0000201>. For applications in structural equation modeling, see: Van Lissa, Gu, Mulder, Rosseel, Van Zundert, & Hoijtink, (2021) <doi:10.1080/10705511.2020.1745644>. For the statistical underpinnings, see Gu, Mulder, and Hoijtink (2018) <doi:10.1111/bmsp.12110>; Hoijtink, Gu, & Mulder, J. (2019) <doi:10.1111/bmsp.12145>; Hoijtink, Gu, Mulder, & Rosseel, (2019) <doi:10.31234/osf.io/q6h5w>.

This package provides a statistical tool to inference the multi-level partial correlations based on multi-subject time series data, especially for brain functional connectivity. It combines both individual and population level inference by using the methods of Qiu and Zhou. (2021)<DOI: 10.1080/01621459.2021.1917417> and Genovese and Wasserman. (2006)<DOI: 10.1198/016214506000000339>. It realizes two reliable estimation methods of partial correlation coefficients, using scaled lasso and lasso. It can be used to estimate individual- or population-level partial correlations, identify nonzero ones, and find out unequal partial correlation coefficients between two populations.

Facilitates the importation of the Boston Blue Bike trip data since 2015. Functions include the computation of trip distances of given trip data. It can also map the location of stations within a given radius and calculate the distance to nearby stations. Data is from <https://www.bluebikes.com/system-data>.

Bayesian quantile regression using the asymmetric Laplace distribution, both continuous as well as binary dependent variables are supported. The package consists of implementations of the methods of Yu & Moyeed (2001) <doi:10.1016/S0167-7152(01)00124-9>, Benoit & Van den Poel (2012) <doi:10.1002/jae.1216> and Al-Hamzawi, Yu & Benoit (2012) <doi:10.1177/1471082X1101200304>. To speed up the calculations, the Markov Chain Monte Carlo core of all algorithms is programmed in Fortran and called from R.

This package performs general Bayesian estimation method of linearâ bilinear models for genotype à environment interaction. The method is explained in Perez-Elizalde, S., Jarquin, D., and Crossa, J. (2011) (<doi:10.1007/s13253-011-0063-9>).

This package provides functions for the Bayesian analysis of some simple commonly-used models, without using Markov Chain Monte Carlo (MCMC) methods such as Gibbs sampling. The rust package <https://cran.r-project.org/package=rust> is used to simulate a random sample from the required posterior distribution, using the generalized ratio-of-uniforms method. See Wakefield, Gelfand and Smith (1991) <DOI:10.1007/BF01889987> for details. At the moment three conjugate hierarchical models are available: beta-binomial, gamma-Poisson and a 1-way analysis of variance (ANOVA).

This package provides a collection of integrated tools designed to seamlessly interact with each other for the analysis of biogenic silica bSi in inland and marine sediments. These tools share common data representations and follow a consistent API design. The primary goal of the bSi package is to simplify the installation process, facilitate data loading, and enable the analysis of multiple samples for biogenic silica fluxes. This package is designed to enhance the efficiency and coherence of the entire bSi analytic workflow, from data loading to model construction and visualization tailored towards reconstructing productivity in aquatic ecosystems.

This package provides a selection of distances measures for bioinformatics data. Other important distance measures for bioinformatics data are selected from the R package parallelDist'. A special distance measure for the Gene Ontology is available.