Full Bayesian estimation of Multidimensional Generalized Graded Unfolding Model (MGGUM) using rstan (See Stan Development Team (2020) <https://mc-stan.org/>). Functions are provided for estimation, result extraction, model fit statistics, and plottings.

This package implements a bootstrap aggregated (bagged) version of the k-nearest neighbors survival probability prediction method (Lowsky et al. 2013). In addition to the bootstrapping of training samples, the features can be subsampled in each baselearner to break the correlation between them. The Rcpp package is used to speed up the computation.

This package provides a recently proposed Bayesian BIN model disentangles the underlying processes that enable forecasters and forecasting methods to improve, decomposing forecasting accuracy into three components: bias, partial information, and noise. By describing the differences between two groups of forecasters, the model allows the user to carry out useful inference, such as calculating the posterior probabilities of the treatment reducing bias, diminishing noise, or increasing information. It also provides insight into how much tamping down bias and noise in judgment or enhancing the efficient extraction of valid information from the environment improves forecasting accuracy. This package provides easy access to the BIN model. For further information refer to the paper Ville A. Satopää, Marat Salikhov, Philip E. Tetlock, and Barbara Mellers (2021) "Bias, Information, Noise: The BIN Model of Forecasting" <doi:10.1287/mnsc.2020.3882>.

From a given data frame, this package learns its Bayesian network structure based on a selected score.

This package provides tools to design best-worst scaling designs (i.e., balanced incomplete block designs) and to analyze data from these designs, using aggregate and individual methods such as: difference scores, Louviere, Lings, Islam, Gudergan, & Flynn (2013) <doi:10.1016/j.ijresmar.2012.10.002>; analytical estimation, Lipovetsky & Conklin (2014) <doi:10.1016/j.jocm.2014.02.001>; empirical Bayes, Lipovetsky & Conklin (2015) <doi:10.1142/S1793536915500028>; Elo, Hollis (2018) <doi:10.3758/s13428-017-0898-2>; and network-based measures.

Fitting Bayesian multiple and mixed-effect regression models for circular data based on the projected normal distribution. Both continuous and categorical predictors can be included. Sampling from the posterior is performed via an MCMC algorithm. Posterior descriptives of all parameters, model fit statistics and Bayes factors for hypothesis tests for inequality constrained hypotheses are provided. See Cremers, Mulder & Klugkist (2018) <doi:10.1111/bmsp.12108> and Nuñez-Antonio & Guttiérez-Peña (2014) <doi:10.1016/j.csda.2012.07.025>.

Simulate multivariate data with arbitrary marginal distributions. bigsimr is a package for simulating high-dimensional multivariate data with a target correlation and arbitrary marginal distributions via Gaussian copula. It utilizes the Julia package Bigsimr.jl for its core routines.

Interactive box plot using plotly for clinical trial analysis.

The Biodem package provides a number of functions for Biodemographic analysis.

This package provides a computationally-efficient leading-eigenvalue approximation to tail probabilities and quantiles of large quadratic forms, in particular for the Sequence Kernel Association Test (SKAT) used in genomics <doi:10.1002/gepi.22136>. Also provides stochastic singular value decomposition for dense or sparse matrices.

Analyze bioequivalence study data with industrial strength. Sample size could be determined for various crossover designs, such as 2x2 design, 2x4 design, 4x4 design, Balaam design, Two-sequence dual design, and William design. Reference: Chow SC, Liu JP. Design and Analysis of Bioavailability and Bioequivalence Studies. 3rd ed. (2009, ISBN:978-1-58488-668-6).

This package provides tools to create, validate, and export BioCompute Objects described in King et al. (2019) <doi:10.17605/osf.io/h59uh>. Users can encode information in data frames, and compose BioCompute Objects from the domains defined by the standard. A checksum validator and a JSON schema validator are provided. This package also supports exporting BioCompute Objects as JSON, PDF, HTML, or Word documents, and exporting to cloud-based platforms.

The mixed model for repeated measures (MMRM) is a popular model for longitudinal clinical trial data with continuous endpoints, and brms is a powerful and versatile package for fitting Bayesian regression models. The brms.mmrm R package leverages brms to run MMRMs, and it supports a simplified interfaced to reduce difficulty and align with the best practices of the life sciences. References: Bürkner (2017) <doi:10.18637/jss.v080.i01>, Mallinckrodt (2008) <doi:10.1177/009286150804200402>.

This package provides methods for examining posterior MCMC samples from a single chain using trace plots and density plots, and from multiple chains by comparing posterior medians and credible intervals from each chain. These plotting functions have a variety of options, such as figure sizes, legends, parameters to plot, and saving plots to file. Functions interface with the NIMBLE software package, see de Valpine, Turek, Paciorek, Anderson-Bergman, Temple Lang and Bodik (2017) <doi:10.1080/10618600.2016.1172487>.

Fit Bayesian graduation mortality using the Heligman-Pollard model, as seen in Heligman, L., & Pollard, J. H. (1980) <doi:10.1017/S0020268100040257> and Dellaportas, Petros, et al. (2001) <doi:10.1111/1467-985X.00202>, and dynamic linear model (Campagnoli, P., Petris, G., and Petrone, S. (2009) <doi:10.1007/b135794_2>). While Heligman-Pollard has parameters with a straightforward interpretation yielding some rich analysis, the dynamic linear model provides a very flexible adjustment of the mortality curves by controlling the discount factor value. Closing methods for both Heligman-Pollard and dynamic linear model were also implemented according to Dodd, Erengul, et al. (2018) <https://www.jstor.org/stable/48547511>. The Bayesian Lee-Carter model is also implemented to fit historical mortality tables time series to predict the mortality in the following years and to do improvement analysis, as seen in Lee, R. D., & Carter, L. R. (1992) <doi:10.1080/01621459.1992.10475265> and Pedroza, C. (2006) <doi:10.1093/biostatistics/kxj024>. Journal publication available at <doi:10.18637/jss.v113.i09>.

Calculates nonparametric pointwise confidence intervals for the survival distribution for right censored data, and for medians [Fay and Brittain <DOI:10.1002/sim.6905>]. Has two-sample tests for dissimilarity (e.g., difference, ratio or odds ratio) in survival at a fixed time, and differences in medians [Fay, Proschan, and Brittain <DOI:10.1111/biom.12231>]. Basically, the package gives exact inference methods for one- and two-sample exact inferences for Kaplan-Meier curves (e.g., generalizing Fisher's exact test to allow for right censoring), which are especially important for latter parts of the survival curve, small sample sizes or heavily censored data. Includes mid-p options.

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.

Perform the Benford's Analysis to a data set in order to evaluate if it contains human fabricated data. For more details on the method see Moreau, 2021, Model Assist. Statist. Appl., 16 (2021) 73â 79. <doi:10.3233/MAS-210517>.

This package provides a cross-platform representation of models as sets of equations that facilitates modularity in model building and allows users to harness modern techniques for numerical integration and data visualization. Documentation is provided by several vignettes included in this package; also see Lochocki et al. (2022) <doi:10.1093/insilicoplants/diac003>.

Israeli baby names provided by Israel's Central Bureau of Statistics. The package contains only names used for at least 5 children in at least one gender and sector ("Jewish", "Muslim", "Christian", "Druze" and "Other"). Data was downloaded from: <https://www.cbs.gov.il/he/publications/LochutTlushim/2020/%D7%A9%D7%9E%D7%95%D7%AA-%D7%A4%D7%A8%D7%98%D7%99%D7%99%D7%9D.xlsx>.

This package creates an area-proportional Venn diagram of 2 or 3 circles. BioVenn is the only R package that can automatically generate an accurate area-proportional Venn diagram by having only lists of (biological) identifiers as input. Also offers the option to map Entrez and/or Affymetrix IDs to Ensembl IDs. In SVG mode, text and numbers can be dragged and dropped. Based on the BioVenn web interface available at <https://www.biovenn.nl>. Hulsen (2021) <doi:10.3233/DS-210032>.

This package provides bias-corrected estimates for the regression coefficients of a marginal model estimated with generalized estimating equations. Details about the bias formula used are in Lunardon, N., Scharfstein, D. (2017) <doi:10.1002/sim.7366>.

Computation of the minimum sample size using the Average Coverage Criterion or the Average Length Criterion for estimating binomial proportions using beta prior distributions. For more details see Costa (2025) <DOI:10.1007/978-3-031-72215-8_14>.

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>.