Data sets and functions for chi-squared Hardy-Weinberg and case-control association tests of highly polymorphic genetic data [e.g., human leukocyte antigen (HLA) data]. Performs association tests at multiple levels of polymorphism (haplotype, locus and HLA amino-acids) as described in Pappas DJ, Marin W, Hollenbach JA, Mack SJ (2016) <doi:10.1016/j.humimm.2015.12.006>. Combines rare variants to a common class to account for sparse cells in tables as described by Hollenbach JA, Mack SJ, Thomson G, Gourraud PA (2012) <doi:10.1007/978-1-61779-842-9_14>.

Bayesian inference under log-normality assumption must be performed very carefully. In fact, under the common priors for the variance, useful quantities in the original data scale (like mean and quantiles) do not have posterior moments that are finite (Fabrizi et al. 2012 <doi:10.1214/12-BA733>). This package allows to easily carry out a proper Bayesian inferential procedure by fixing a suitable distribution (the generalized inverse Gaussian) as prior for the variance. Functions to estimate several kind of means (unconditional, conditional and conditional under a mixed model) and quantiles (unconditional and conditional) are provided.

This package implements Bayesian spatio-temporal factor analysis models for multivariate data observed across space and time. The package provides tools for model fitting via Markov chain Monte Carlo (MCMC), spatial and temporal interpolation, and visualization of latent factors and loadings to support inference and exploration of underlying spatio-temporal patterns. Designed for use in environmental, ecological, or public health applications, with support for posterior prediction and uncertainty quantification. Includes functions such as BSTFA() for model fitting and plot_factor() to visualize the latent processes. Functions are based on and extended from methods described in Berrett, et al. (2020) <doi:10.1002/env.2609>.

Unified and user-friendly framework for using new distributional representations of biosensors data in different statistical modeling tasks: regression models, hypothesis testing, cluster analysis, visualization, and descriptive analysis. Distributional representations are a functional extension of compositional time-range metrics and we have used them successfully so far in modeling glucose profiles and accelerometer data. However, these functional representations can be used to represent any biosensor data such as ECG or medical imaging such as fMRI. Matabuena M, Petersen A, Vidal JC, Gude F. "Glucodensities: A new representation of glucose profiles using distributional data analysis" (2021) <doi:10.1177/0962280221998064>.

This package provides functions for training extreme gradient boosting model using propensity score A-learning and weight-learning methods. For further details, see Liu et al. (2024) <doi:10.1093/bioinformatics/btae592>.

BAYesian inference for MEDical designs in R. Functions for the computation of Bayes factors for common biomedical research designs. Implemented are functions to test the equivalence (equiv_bf), non-inferiority (infer_bf), and superiority (super_bf) of an experimental group compared to a control group on a continuous outcome measure. Bayes factors for these three tests can be computed based on raw data (x, y) or summary statistics (n_x, n_y, mean_x, mean_y, sd_x, sd_y [or ci_margin and ci_level]).

This package provides a fast integrative genetic association test for rare diseases based on a model for disease status given allele counts at rare variant sites. Probability of association, mode of inheritance and probability of pathogenicity for individual variants are all inferred in a Bayesian framework - A Fast Association Test for Identifying Pathogenic Variants Involved in Rare Diseases', Greene et al 2017 <doi:10.1016/j.ajhg.2017.05.015>.

Estimates Bayesian models of list experiments with informative priors. It includes functionalities to estimate different types of list experiment models with varying prior information. See Lu and Traunmüller (2026) <doi:10.1017/psrm.2025.10084> for examples and details of estimation.

Interface to Local Data Bank ('Bank Danych Lokalnych - bdl') API <https://api.stat.gov.pl/Home/BdlApi?lang=en> with set of useful tools like quick plotting and map generating using data from bank.

This package provides functionality for determining the sample size of replication studies using Bayesian design approaches in the normal-normal hierarchical model (Pawel et al., 2022) <doi:10.48550/arXiv.2211.02552>.

This package performs a spatial Bayesian general linear model (GLM) for task functional magnetic resonance imaging (fMRI) data on the cortical surface. Additional models include group analysis and inference to detect thresholded areas of activation. Includes direct support for the CIFTI neuroimaging file format. For more information see A. F. Mejia, Y. R. Yue, D. Bolin, F. Lindgren, M. A. Lindquist (2020) <doi:10.1080/01621459.2019.1611582> and D. Spencer, Y. R. Yue, D. Bolin, S. Ryan, A. F. Mejia (2022) <doi:10.1016/j.neuroimage.2022.118908>.

Fully Bayesian Classification with a subset of high-dimensional features, such as expression levels of genes. The data are modeled with a hierarchical Bayesian models using heavy-tailed t distributions as priors. When a large number of features are available, one may like to select only a subset of features to use, typically those features strongly correlated with the response in training cases. Such a feature selection procedure is however invalid since the relationship between the response and the features has be exaggerated by feature selection. This package provides a way to avoid this bias and yield better-calibrated predictions for future cases when one uses F-statistic to select features.

The backfill Bayesian optimal interval design using efficacy and toxicity outcomes for dose optimization (BF-BOIN-ET) design is a novel clinical trial design to allow patients to be backfilled at lower doses during a dose-finding trial while prioritizing the dose-escalation cohort to explore a higher dose. The advantages compared to the other designs in terms of the percentage of correct optimal dose (OD) selection, reducing the sample size, and shortening the duration of the trial, in various realistic setting.

Utilities for Bratteli graphs. A tree is an example of a Bratteli graph. The package provides a function which generates a LaTeX file that renders the given Bratteli graph. It also provides functions to compute the dimensions of the vertices, the intrinsic kernels and the intrinsic distances. Intrinsic kernels and distances were introduced by Vershik (2014) <doi:10.1007/s10958-014-1958-0>.

This package provides a collection of box-geometry model (BGM) files for the Atlantis ecosystem model. Atlantis is a deterministic, biogeochemical, whole-of-ecosystem model (see <http://atlantis.cmar.csiro.au/> for more information).

This package provides a framework for building interactive dashboards and document-based reports. Underlying data manipulation and visualization is possible using a web-based point and click user interface.

This package provides tools to facilitate the access and processing of data from the Central Bank of Brazil API. The package allows users to retrieve economic and financial data, transforming them into usable tabular formats for further analysis. The data is obtained from the Central Bank of Brazil API: <https://api.bcb.gov.br/dados/serie/bcdata.sgs.series_code/dados?formato=json&dataInicial=start_date&dataFinal=end_date>.

Script search, corner, genetic optimization, permutation tests, write expect test.

Interactive box plot using plotly for clinical trial analysis.

This package provides tools for conducting Bayesian analyses and Bayesian model averaging (Kass and Raftery, 1995, <doi:10.1080/01621459.1995.10476572>, Hoeting et al., 1999, <doi:10.1214/ss/1009212519>). The package contains functions for creating a wide range of prior distribution objects, mixing posterior samples from JAGS and Stan models, plotting posterior distributions, and etc... The tools for working with prior distribution span from visualization, generating JAGS and bridgesampling syntax to basic functions such as rng, quantile, and distribution functions.

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>.

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.

Instructor-developed tools for Analytics and Quantitative Methods (AQM) courses at Babson College. Included are compact descriptive statistics for data frames and lists, expanded reporting and graphics for linear regressions, and formatted reports for best subsets analyses.

Data on multiple individuals through time are often sampled at times that differ between persons. Irregular observation times can severely complicate the statistical analysis of the data. The broken stick model approximates each subjectâ s trajectory by one or more connected line segments. The times at which segments connect (breakpoints) are identical for all subjects and under control of the user. A well-fitting broken stick model effectively transforms individual measurements made at irregular times into regular trajectories with common observation times. Specification of the model requires three variables: time, measurement and subject. The model is a special case of the linear mixed model, with time as a linear B-spline and subject as the grouping factor. The main assumptions are: subjects are exchangeable, trajectories between consecutive breakpoints are straight, random effects follow a multivariate normal distribution, and unobserved data are missing at random. The package contains functions for fitting the broken stick model to data, for predicting curves in new data and for plotting broken stick estimates. The package supports two optimization methods, and includes options to structure the variance-covariance matrix of the random effects. The analyst may use the software to smooth growth curves by a series of connected straight lines, to align irregularly observed curves to a common time grid, to create synthetic curves at a user-specified set of breakpoints, to estimate the time-to-time correlation matrix and to predict future observations. See <doi:10.18637/jss.v106.i07> for additional documentation on background, methodology and applications.