This package provides a toolkit for constructing, validating, and justifying Bayesian priors in clinical trial settings. Implements expert elicitation via quantile matching, the roulette method, and moment matching across six distribution families, linear and logarithmic expert pooling, meta-analytic-predictive (MAP) priors derived from historical trials, prior-data conflict diagnostics including the Box p-value (computed from the exact prior predictive distribution where one has a closed form), an exploratory surprisal (S-value) companion to it, surprise index, information divergence, and Mahalanobis distance, sensitivity analyses with tornado and influence heatmap plots, sceptical, robust, and power priors, and automated prior justification reports. Includes a fully modular Shiny application for interactive use. Methods based on O'Hagan et al. (2006, ISBN:9780470029886), Box (1980) <doi:10.2307/2982063>, Greenland (2023) <doi:10.1111/sjos.12625>, Oakley and O'Hagan (2010) <https://tonyohagan.co.uk/shelf/>, Schmidli et al. (2014) <doi:10.1111/biom.12242>, Neuenschwander et al. (2010) <doi:10.1177/1740774509356002>, Roever et al. (2021) <doi:10.1002/jrsm.1475>, Ibrahim and Chen (2000) <doi:10.1214/ss/1009212673>, Spiegelhalter et al. (1994) <doi:10.2307/2983527>.

This package provides a set of models to estimate nonlinear longitudinal data using Bayesian estimation methods. These models include the: 1) Bayesian Piecewise Random Effects Model (Bayes_PREM()) which estimates a piecewise random effects (mixture) model for a given number of latent classes and a latent number of possible changepoints in each class, and can incorporate class and outcome predictive covariates (see Lamm (2022) <https://hdl.handle.net/11299/252533> and Lock et al., (2018) <doi:10.1007/s11336-017-9594-5>), 2) Bayesian Crossed Random Effects Model (Bayes_CREM()) which estimates a linear, quadratic, exponential, or piecewise crossed random effects models where individuals are changing groups over time (e.g., students and schools; see Rohloff et al., (2024) <doi:10.1111/bmsp.12334>), and 3) Bayesian Bivariate Piecewise Random Effects Model (Bayes_BPREM()) which estimates a bivariate piecewise random effects model to jointly model two related outcomes (e.g., reading and math achievement; see Peralta et al., (2022) <doi:10.1037/met0000358>).

Fits a Bayesian zero-inflated Bernoulli regression model handling (potentially) different covariates for the zero-inflated and non zero-inflated parts. See Moriña D, Puig P, Navarro A. (2021) <doi:10.1186/s12874-021-01427-2>.

This package provides tools for constructing board/grid based games, as well as readily available game(s) for your entertainment.

This package provides functions to plot and help understand positive and negative predictive values (PPV and NPV), and their relationship with sensitivity, specificity, and prevalence. See Akobeng, A.K. (2007) <doi:10.1111/j.1651-2227.2006.00180.x> for a theoretical overview of the technical concepts and Navarrete et al. (2015) for a practical explanation about the importance of their understanding <doi:10.3389/fpsyg.2015.01327>.

Easily talk to Google's BigQuery Storage API from R (<https://docs.cloud.google.com/bigquery/docs/reference/storage/rpc>).

Allows users to easily visualize data from the BLS (United States of America Bureau of Labor Statistics) <https://www.bls.gov>. Currently unemployment data series U1-U6 are available. Not affiliated with the Bureau of Labor Statistics or United States Government.

This package performs change point detection on univariate and multivariate time series (Martà nez & Mena, 2014, <doi:10.1214/14-BA878> ; Corradin, Danese & Ongaro, 2022, <doi:10.1016/j.ijar.2021.12.019>) and clusters time-dependent data with common change points (Corradin, Danese, KhudaBukhsh & Ongaro, 2026, <doi:10.1007/s11222-025-10756-x>).

This package provides a maximum likelihood estimation of Bivariate Zero-Inflated Negative Binomial (BZINB) model or the nested model parameters. Also estimates the underlying correlation of the a pair of count data. See Cho, H., Liu, C., Preisser, J., and Wu, D. (In preparation) for details.

Highly efficient functions for estimating various rank (centrality) measures of nodes in bipartite graphs (two-mode networks). Includes methods for estimating HITS, CoHITS, BGRM, and BiRank with implementation primarily inspired by He et al. (2016) <doi:10.1109/TKDE.2016.2611584>. Also provides easy-to-use tools for efficiently estimating PageRank in one-mode graphs, incorporating or removing edge-weights during rank estimation, projecting two-mode graphs to one-mode, and for converting edgelists and matrices to sparseMatrix format. Best of all, the package's rank estimators can work directly with common formats of network data including edgelists (class data.frame, data.table, or tbl_df) and adjacency matrices (class matrix or dgCMatrix).

Spike and slab regression with a variety of residual error distributions corresponding to Gaussian, Student T, probit, logit, SVM, and a few others. Spike and slab regression is Bayesian regression with prior distributions containing a point mass at zero. The posterior updates the amount of mass on this point, leading to a posterior distribution that is actually sparse, in the sense that if you sample from it many coefficients are actually zeros. Sampling from this posterior distribution is an elegant way to handle Bayesian variable selection and model averaging. See <DOI:10.1504/IJMMNO.2014.059942> for an explanation of the Gaussian case.

Build decision trees and random forests for classification and regression. The implementation strikes a balance between minimizing computing efforts and maximizing the expected predictive accuracy, thus scales well to large data sets. Multi-threading is available through OpenMP <https://gcc.gnu.org/wiki/openmp>.

This package provides a method for the Bayesian functional linear regression model (scalar-on-function), including two estimators of the coefficient function and an estimator of its support. A representation of the posterior distribution is also available. Grollemund P-M., Abraham C., Baragatti M., Pudlo P. (2019) <doi:10.1214/18-BA1095>.

This package provides a collection of functions that allow servers (specifically BPKP) to summarize and verify shift earnings, identify composition of tips and hourly wage, and track performance through several metrics.

Running and comparing meta-analyses of data with hierarchical Bayesian models in Stan, including convenience functions for formatting data, plotting and pooling measures specific to meta-analysis. This implements many models from Meager (2019) <doi:10.1257/app.20170299>.

Estimates Boltzmannâ Lotkaâ Volterra (BLV) interaction model efficiently. Enables programmatic and graphical exploration of the solution space of BLV models when parameters are varied. See Wilson, A. (2008) <dx.doi.org/10.1098/rsif.2007.1288>.

This package provides a collection of S4 classes, methods and functions to create and visualize business plans. Different types of cash flows can be defined, which can then be used and tabulated to create profit and loss statements, cash flow plans, investment and depreciation schedules, loan amortization schedules, etc. The methods are designed to produce handsome tables in both PDF and HTML using RMarkdown or Shiny'.

Laplace approximations and penalized B-splines are combined for fast Bayesian inference in latent Gaussian models. The routines can be used to fit survival models, especially proportional hazards and promotion time cure models (Gressani, O. and Lambert, P. (2018) <doi:10.1016/j.csda.2018.02.007>). The Laplace-P-spline methodology can also be implemented for inference in (generalized) additive models (Gressani, O. and Lambert, P. (2021) <doi:10.1016/j.csda.2020.107088>). See the associated website for more information and examples.

This package provides functions provide risk projections of invasive breast cancer based on Gail model according to National Cancer Institute's Breast Cancer Risk Assessment Tool algorithm for specified race/ethnic groups and age intervals. Gail MH, Brinton LA, et al (1989) <doi:10.1093/jnci/81.24.1879>. Marthew PB, Gail MH, et al (2016) <doi:10.1093/jnci/djw215>.

Approximate Bayesian inference and Monte Carlo validation for small-N repeated-measures designs with two time points and two raters. The package is intended for applications in which sample size is limited and the observed outcome may be affected by rater-specific bias. User-supplied data are standardised into a common long-format structure. Pre-post effects are analysed using difference scores in a linear model with a rater indicator as covariate. Posterior summaries for the regression coefficients are obtained from a large-sample normal approximation centred at the least-squares estimate with plug-in covariance under a flat improper prior. Evidence for a non-zero pre-post effect, adjusted for rater differences, is summarised using a BIC-based approximation to the Bayes factor for comparison between models with and without the pre-post effect. Monte Carlo validation uses design quantities estimated from the observed data, including sample size, mean pre-post change, and second-rater additive discrepancy, and summarises inferential performance in terms of bias, root mean squared error, credible interval coverage, posterior tail probabilities, and mean Bayes factor values. For background on the BIC approximation and Bayes factors, see Schwarz (1978) <doi:10.1214/aos/1176344136> and Kass and Raftery (1995) <doi:10.1080/01621459.1995.10476572>.

This package provides a modular framework for standardized analysis of thermal imaging data in animal experimentation. The package integrates thermographic data import (FLIR, raw, CSV), automated region of interest (ROI) segmentation based on EBImage (Pau et al., 2010 <doi:10.1093/bioinformatics/btq046>), interactive ROI refinement, and high-throughput batch processing.

Interactive box plot using plotly for clinical trial analysis.

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 tools to fit joint models of multivariate longitudinal data and time-to-event data for dynamic prediction. It allows the joint prediction of both future time-to-event outcomes and future longitudinal outcomes conditional on survival. The models accommodate irregularly measured longitudinal data and competing risks outcomes. The use of the backward joint model enables fast and efficient computation, especially for applications with large sample sizes and many longitudinal variables.