Tree- and rule-based models can be bagged (<doi:10.1007/BF00058655>) using this package and their predictions equations are stored in an efficient format to reduce the model objects size and speed.

We provide a tidy data structure and visualisations for multiple or grouped variable correlations, general association measures scagnostics and other pairwise scores suitable for numerical, ordinal and nominal variables. Supported measures include distance correlation, maximal information, ace correlation, Kendall's tau, and polychoric correlation.

Investigating and visualising Bayesian Additive Regression Tree (BART) (Chipman, H. A., George, E. I., & McCulloch, R. E. 2010) <doi:10.1214/09-AOAS285> model fits. We construct conventional plots to analyze a modelâ s performance and stability as well as create new tree-based plots to analyze variable importance, interaction, and tree structure. We employ Value Suppressing Uncertainty Palettes (VSUP) to construct heatmaps that display variable importance and interactions jointly using colour scale to represent posterior uncertainty. Our visualisations are designed to work with the most popular BART R packages available, namely BART Rodney Sparapani and Charles Spanbauer and Robert McCulloch 2021 <doi:10.18637/jss.v097.i01>, dbarts (Vincent Dorie 2023) <https://CRAN.R-project.org/package=dbarts>, and bartMachine (Adam Kapelner and Justin Bleich 2016) <doi:10.18637/jss.v070.i04>.

Frequentist inference on adaptively generated data. The methods implemented are based on Zhan et al. (2021) <doi:10.48550/arXiv.2106.02029> and Hadad et al. (2021) <doi:10.48550/arXiv.1911.02768>. For illustration, several functions for simulating non-contextual and contextual adaptive experiments using Thompson sampling are also supplied.

This package provides a set of R functions and data sets for the book Introduction to Bayesian Statistics, Bolstad, W.M. (2017), John Wiley & Sons ISBN 978-1-118-09156-2.

Generates interactive bipartite graphs using the D3 library. Designed for use with the bipartite analysis package. Includes open source viz-js library Adapted from examples at <https://bl.ocks.org/NPashaP> (released under GPL-3).

This package contains several plotting functions such as barplots, scatterplots, heatmaps, as well as functions to combine plots and assist in the creation of these plots. These functions will give users great ease of use and customization options in broad use for biomedical applications, as well as general purpose plotting. Each of the functions also provides valid default settings to make plotting data more efficient and producing high quality plots with standard colour schemes simpler. All functions within this package are capable of producing plots that are of the quality to be presented in scientific publications and journals. P'ng et al.; BPG: Seamless, automated and interactive visualization of scientific data; BMC Bioinformatics 2019 <doi:10.1186/s12859-019-2610-2>.

Twelve confidence intervals for one binomial proportion or a vector of binomial proportions are computed. The confidence intervals are: Jeffreys, Wald, Wald corrected, Wald, Blyth and Still, Agresti and Coull, Wilson, Score, Score corrected, Wald logit, Wald logit corrected, Arcsine and Exact binomial. References include, among others: Vollset, S. E. (1993). "Confidence intervals for a binomial proportion". Statistics in Medicine, 12(9): 809-824. <doi:10.1002/sim.4780120902>.

Providing equivalent functions for the dummy classifier and regressor used in Python scikit-learn library. Our goal is to allow R users to easily identify baseline performance for their classification and regression problems. Our baseline models use no predictors, and are useful in cases of class imbalance, multiclass classification, and when users want to quickly identify how much improvement their statistical and machine learning models are over several baseline models. We use a "better" default (proportional guessing) for the dummy classifier than the Python implementation ("prior", which is the most frequent class in the training set). The functions in the package can be used on their own, or introduce methods named dummy_regressor or dummy_classifier that can be used within the caret package pipeline.

Fast Bayesian inference of marginal and conditional independence structures from high-dimensional data. Leday and Richardson (2019), Biometrics, <doi:10.1111/biom.13064>.

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>.

Adjusting the bias due to residual confounding (often called treatment selection bias) in estimating the treatment effect in a proportional hazard model, as described in Williamson et al. (2022) <doi:10.1158/1078-0432.ccr-21-2468>.

This package implements Bayesian model averaging for dynamic panels with weakly exogenous regressors as described in the paper by Moral-Benito (2013, <doi:10.1080/07350015.2013.818003>). The package provides functions to estimate dynamic panel data models and analyze the results of the estimation.

Whole-genome regression methods on Bayesian framework fitted via EM or Gibbs sampling, single step (<doi:10.1534/g3.119.400728>), univariate and multivariate (<doi:10.1186/s12711-022-00730-w>, <doi:10.1093/genetics/iyae179>), with optional kernel term and sampling techniques (<doi:10.1186/s12859-017-1582-3>).

Calculates the prices of European options based on the universal solution provided by Bakshi, Cao and Chen (1997) <doi:10.1111/j.1540-6261.1997.tb02749.x>. This solution considers stochastic volatility, stochastic interest and random jumps. Please cite their work if this package is used.

This package provides a unified set of methods to detect scientific emergence and technological trajectories in academic papers and patents. The package combines citation network analysis with community detection and attribute extraction, also applying natural language processing (NLP) and structural topic modeling (STM) to uncover the contents of research communities. It implements metrics and visualizations of community trajectories, including novelty indicators, citation cycle time, and main path analysis, allowing researchers to map and interpret the dynamics of emerging knowledge fields. Applications of the method include: Souza et al. (2022) <doi:10.1002/bbb.2441>, Souza et al. (2022) <doi:10.14211/ibjesb.e1742>, Matos et al. (2023) <doi:10.1007/s43938-023-00036-3>, Maria et al. (2023) <doi:10.3390/su15020967>, Biazatti et al. (2024) <doi:10.1016/j.envdev.2024.101074>, Felizardo et al. (2025) <doi:10.1007/s12649-025-03136-z>, and Miranda et al. (2025) <doi:10.1016/j.ijhydene.2025.01.089>.

This package provides a chemical speciation and toxicity prediction model for the toxicity of metals to aquatic organisms. The Biotic Ligand Model (BLM) engine was originally programmed in PowerBasic by Robert Santore and others. The main way the BLM can be used is to predict the toxicity of a metal to an organism with a known sensitivity (i.e., it is known how much of that metal must accumulate on that organism's biotic ligand to cause a physiological effect in a certain percentage of the population, such as a 20% loss in reproduction or a 50% mortality rate). The second way the BLM can be used is to estimate the chemical speciation of the metal and other constituents in water, including estimating the amount of metal accumulated to an organism's biotic ligand during a toxicity test. In the first application of the BLM, the amount of metal associated with a toxicity endpoint, or regulatory limit will be predicted, while in the second application, the amount of metal is known and the portions of that metal that exist in various forms will be determined. This version of the engine has been re-structured to perform the calculations in a different way that will make it more efficient in R, while also making it more flexible and easier to maintain in the future. Because of this, it does not currently match the desktop model exactly, but we hope to improve this comparability in the future.

Density, distribution function, quantile function random generation and estimation of bimodal GEV distribution given in Otiniano et al. (2023) <doi:10.1007/s10651-023-00566-7>. This new generalization of the well-known GEV (Generalized Extreme Value) distribution is useful for modeling heterogeneous bimodal data from different areas.

Reads and plots phylogenetic placements.

Record algorithmic and analytic meta data along a workflow to store that in a bitfield, which can be published alongside any (modelled) data products.

This package implements two algorithms of detecting Bull and Bear markets in stock prices: the algorithm of Pagan and Sossounov (2002, <doi:10.1002/jae.664>) and the algorithm of Lunde and Timmermann (2004, <doi:10.1198/073500104000000136>). The package also contains functions for printing out the dating of the Bull and Bear states of the market, the descriptive statistics of the states, and functions for plotting the results. For the sake of convenience, the package includes the monthly and daily data on the prices (not adjusted for dividends) of the S&P 500 stock market index.

Function bipmod() that partitions a bipartite network into non-overlapping biclusters by maximizing bipartite modularity defined in Barber (2007) <doi:10.1103/PhysRevE.76.066102> using the bipartite version of the algorithm described in Treviño (2015) <doi:10.1088/1742-5468/2015/02/P02003>.

This package provides a blind spike program provides samples to a laboratory in order to perform quality control (QC) checks. The samples provided are of a known quantity to the tester. The laboratory is typically uninformed of that the sample provided is a QC sample.

This package implements Bayesian brain mapping with population-derived priors, including the original model described in Mejia et al. (2020) <doi:10.1080/01621459.2019.1679638>, the model with spatial priors described in Mejia et al. (2022) <doi:10.1080/10618600.2022.2104289>, and the model with population-derived priors on functional connectivity described in Mejia et al. (2025) <doi:10.1093/biostatistics/kxaf022>. Population-derived priors are based on templates representing established brain network maps, for example derived from independent component analysis (ICA), parcellations, or other methods. Model estimation is based on expectation-maximization or variational Bayes algorithms. Includes direct support for CIFTI', GIFTI', and NIFTI neuroimaging file formats.