This package provides a set of tools for performing graph theory analysis of brain MRI data. It works with data from a Freesurfer analysis (cortical thickness, volumes, local gyrification index, surface area), diffusion tensor tractography data (e.g., from FSL) and resting-state fMRI data (e.g., from DPABI). It contains a graphical user interface for graph visualization and data exploration, along with several functions for generating useful figures.

These are bartMachine's Java dependency libraries. Note: this package has no functionality of its own and should not be installed as a standalone package without bartMachine.

Real-time quantitative polymerase chain reaction (qPCR) data sets by Boggy et al. (2008) <doi:10.1371/journal.pone.0012355>. This package provides a dilution series for one PCR target: a random sequence that minimizes secondary structure and off-target primer binding. The data set is a six-point, ten-fold dilution series. For each concentration there are two replicates. Each amplification curve is 40 cycles long. Original raw data file: <https://journals.plos.org/plosone/article/file?type=supplementary&id=10.1371/journal.pone.0012355.s004>.

Boosting Regression Quantiles is a component-wise boosting algorithm, that embeds all boosting steps in the well-established framework of quantile regression. It is initialized with the corresponding quantile, uses a quantile-specific learning rate, and uses quantile regression as its base learner. The package implements this algorithm and allows cross-validation and stability selection.

The Philippines frequently experiences tropical cyclones (called bagyo in the Filipino language) because of its geographical position. These cyclones typically bring heavy rainfall, leading to widespread flooding, as well as strong winds that cause significant damage to human life, crops, and property. Data on cyclones are collected and curated by the Philippine Atmospheric, Geophysical, and Astronomical Services Administration or PAGASA and made available through its website <https://bagong.pagasa.dost.gov.ph/tropical-cyclone/publications/annual-report>. This package contains Philippine tropical cyclones data in a machine-readable format. It is hoped that this data package provides an interesting and unique dataset for data exploration and visualisation.

Decision tree algorithm with a major feature added. Allows for users to define an ordering on the partitioning process. Resulting in Branch-Exclusive Splits Trees (BEST). Cedric Beaulac and Jeffrey S. Rosentahl (2019) <arXiv:1804.10168>.

An implementation of the model in Steorts (2015) <DOI:10.1214/15-BA965SI>, which performs Bayesian entity resolution for categorical and text data, for any distance function defined by the user. In addition, the precision and recall are in the package to allow one to compare to any other comparable method such as logistic regression, Bayesian additive regression trees (BART), or random forests. The experiments are reproducible and illustrated using a simple vignette. LICENSE: GPL-3 + file license.

Fits and simulates multi-optima Ornstein-Uhlenbeck models to phylogenetic comparative data using Bayesian reversible-jump methods. See Uyeda and Harmon (2014) <DOI:10.1093/sysbio/syu057>.

This package provides a collection of functions to analyse, visualize and interpret wind data and to calculate the potential energy production of wind turbines.

Estimation of large Vector AutoRegressive (VAR), Vector AutoRegressive with Exogenous Variables X (VARX) and Vector AutoRegressive Moving Average (VARMA) Models with Structured Lasso Penalties, see Nicholson, Wilms, Bien and Matteson (2020) <https://jmlr.org/papers/v21/19-777.html> and Wilms, Basu, Bien and Matteson (2021) <doi:10.1080/01621459.2021.1942013>.

Binomial Haar-Fisz transforms for Gaussianization as in Nunes and Nason (2009).

This package provides tools for building Rescorla-Wagner Models for Two-Alternative Forced Choice tasks, commonly employed in psychological research. Most concepts and ideas within this R package are referenced from Sutton and Barto (2018) <ISBN:9780262039246>. The package allows for the intuitive definition of RL models using simple if-else statements and three basic models built into this R package are referenced from Niv et al. (2012) <doi:10.1523/JNEUROSCI.5498-10.2012>. Our approach to constructing and evaluating these computational models is informed by the guidelines proposed in Wilson & Collins (2019) <doi:10.7554/eLife.49547>. Example datasets included with the package are sourced from the work of Mason et al. (2024) <doi:10.3758/s13423-023-02415-x>.

Facilitates some of the analyses performed in studies of behavioral economic discounting. The package supports scoring of the 27-Item Monetary Choice Questionnaire (see Kaplan et al., 2016; <doi:10.1007/s40614-016-0070-9>), calculating k values (Mazur's simple hyperbolic and exponential) using nonlinear regression, calculating various Area Under the Curve (AUC) measures, plotting regression curves for both fit-to-group and two-stage approaches, checking for unsystematic discounting (Johnson & Bickel, 2008; <doi:10.1037/1064-1297.16.3.264>) and scoring of the minute discounting task (see Koffarnus & Bickel, 2014; <doi:10.1037/a0035973>) using the Qualtrics 5-trial discounting template (see the Qualtrics Minute Discounting User Guide; <doi:10.13140/RG.2.2.26495.79527>), which is also available as a .qsf file in this package.

An implementation of the Bayesian version of the Mallows rank model (Vitelli et al., Journal of Machine Learning Research, 2018 <https://jmlr.org/papers/v18/15-481.html>; Crispino et al., Annals of Applied Statistics, 2019 <doi:10.1214/18-AOAS1203>; Sorensen et al., R Journal, 2020 <doi:10.32614/RJ-2020-026>; Stein, PhD Thesis, 2023 <https://eprints.lancs.ac.uk/id/eprint/195759>). Both Metropolis-Hastings and sequential Monte Carlo algorithms for estimating the models are available. Cayley, footrule, Hamming, Kendall, Spearman, and Ulam distances are supported in the models. The rank data to be analyzed can be in the form of complete rankings, top-k rankings, partially missing rankings, as well as consistent and inconsistent pairwise preferences. Several functions for plotting and studying the posterior distributions of parameters are provided. The package also provides functions for estimating the partition function (normalizing constant) of the Mallows rank model, both with the importance sampling algorithm of Vitelli et al. and asymptotic approximation with the IPFP algorithm (Mukherjee, Annals of Statistics, 2016 <doi:10.1214/15-AOS1389>).

This package provides spatial data for mapping Brunei, including boundaries for districts, mukims, and kampongs, as well as locations of key infrastructure such as masjids, hospitals, clinics, and schools. The package supports researchers, analysts, and developers working with Bruneiâ s geographic and demographic data, offering a quick and accessible foundation for creating maps and conducting spatial studies.

Computational tools for outlier detection and influence diagnostics in meta-analysis (Noma et al. (2025) <doi:10.1101/2025.09.18.25336125>). Bootstrap distributions of influence statistics are computed, and explicit thresholds for identifying outliers are provided. These methods can also be applied to the analysis of influential centers or regions in multicenter or multiregional clinical trials (Aoki, Noma and Gosho (2021) <doi:10.1080/24709360.2021.1921944>, Nakamura and Noma (2021) <doi:10.5691/jjb.41.117>).

The BACCO bundle of packages is replaced by the BACCO package, which provides a vignette that illustrates the constituent packages (emulator, approximator, calibrator) in use.

This package provides a computational tool to describe patterns in black and white images from natural structures. bwimage implemented functions for exceptionally broad subject. For instance, bwimage provide examples that range from calculation of canopy openness, description of patterns in vertical vegetation structure, to patterns in bird nest structure.

Facilitates scalable spatiotemporally varying coefficient modelling with Bayesian kernelized tensor regression. The important features of this package are: (a) Enabling local temporal and spatial modeling of the relationship between the response variable and covariates. (b) Implementing the model described by Lei et al. (2023) <doi:10.48550/arXiv.2109.00046>. (c) Using a Bayesian Markov Chain Monte Carlo (MCMC) algorithm to sample from the posterior distribution of the model parameters. (d) Employing a tensor decomposition to reduce the number of estimated parameters. (e) Accelerating tensor operations and enabling graphics processing unit (GPU) acceleration with the torch package.

This package provides a Gibbs sampler algorithm was developed to estimate change points in constant-wise data sequences while performing clustering simultaneously. The algorithm is described in da Cruz, A. C. and de Souza, C. P. E "A Bayesian Approach for Clustering Constant-wise Change-point Data" <doi:10.48550/arXiv.2305.17631>.

Distributes Gaussian process calculations across nodes in a distributed memory setting, using Rmpi. The bigGP class provides high-level methods for maximum likelihood with normal data, prediction, calculation of uncertainty (i.e., posterior covariance calculations), and simulation of realizations. In addition, bigGP provides an API for basic matrix calculations with distributed covariance matrices, including Cholesky decomposition, back/forwardsolve, crossproduct, and matrix multiplication.

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.

This package provides a method to filter correlation and covariance matrices by averaging bootstrapped filtered hierarchical clustering and boosting. See Ch. Bongiorno and D. Challet, Covariance matrix filtering with bootstrapped hierarchies (2020) <arXiv:2003.05807> and Ch. Bongiorno and D. Challet, Reactive Global Minimum Variance Portfolios with k-BAHC covariance cleaning (2020) <arXiv:2005.08703>.

Fit and simulate bivariate correlated frailty models with proportional hazard structure. Frailty distributions, such as gamma and lognormal models are supported semiparametric procedures. Frailty variances of the two subjects can be varied or equal. Details on the models are available in book of Wienke (2011,ISBN:978-1-4200-7388-1). Bivariate gamma fit is obtained using the approach given in Kifle et al (2023) <DOI: 10.4310/22-SII738> with modifications. Lognormal fit is based on the approach by Ripatti and Palmgren (2000) <doi:10.1111/j.0006-341X.2000.01016.x>. Frailty distributions, such as gamma, inverse gaussian and power variance frailty models are supported for parametric approach.