Comprehensive Business Process Analysis toolkit. Creates S3-class for event log objects, and related handler functions. Imports related packages for filtering event data, computation of descriptive statistics, handling of Petri Net objects and visualization of process maps. See also packages edeaR','processmapR', eventdataR and processmonitR'.

MDS is a statistic tool for reduction of dimensionality, using as input a distance matrix of dimensions n à n. When n is large, classical algorithms suffer from computational problems and MDS configuration can not be obtained. With this package, we address these problems by means of six algorithms, being two of them original proposals: - Landmark MDS proposed by De Silva V. and JB. Tenenbaum (2004). - Interpolation MDS proposed by Delicado P. and C. Pachón-Garcà a (2021) <arXiv:2007.11919> (original proposal). - Reduced MDS proposed by Paradis E (2018). - Pivot MDS proposed by Brandes U. and C. Pich (2007) - Divide-and-conquer MDS proposed by Delicado P. and C. Pachón-Garcà a (2021) <arXiv:2007.11919> (original proposal). - Fast MDS, proposed by Yang, T., J. Liu, L. McMillan and W. Wang (2006).

Enable users to evaluate long-term trends using a Generalized Additive Modeling (GAM) approach. The model development includes selecting a GAM structure to describe nonlinear seasonally-varying changes over time, incorporation of hydrologic variability via either a river flow or salinity, the use of an intervention to deal with method or laboratory changes suspected to impact data values, and representation of left- and interval-censored data. The approach has been applied to water quality data in the Chesapeake Bay, a major estuary on the east coast of the United States to provide insights to a range of management- and research-focused questions. Methodology described in Murphy (2019) <doi:10.1016/j.envsoft.2019.03.027>.

Implementation of Graph Signal Processing (GSP) methods including Spectral Graph Wavelet Transform (SGWT) for analyzing spatial patterns in biological data. Based on Hammond, Vandergheynst, and Gribonval (2011) <doi:10.1016/j.acha.2010.04.005>. Provides tools for multi-scale analysis of biology spatial signals, including forward and inverse transforms, energy analysis, and visualization functions tailored for biological applications. Biological application example is on Stephanie, Yao, Yuzhou (2024) <doi:10.1101/2024.12.20.629650>.

An implementation of sensitivity and robustness methods in Bayesian networks in R. It includes methods to perform parameter variations via a variety of co-variation schemes, to compute sensitivity functions and to quantify the dissimilarity of two Bayesian networks via distances and divergences. It further includes diagnostic methods to assess the goodness of fit of a Bayesian networks to data, including global, node and parent-child monitors. Reference: M. Leonelli, R. Ramanathan, R.L. Wilkerson (2022) <doi:10.1016/j.knosys.2023.110882>.

Bayesian Hierarchical beta-binomial models for modeling cell population to predictors/exposures. This package utilizes runjags to run Gibbs sampling, parallelizing the chains. Options for different covariances/relationship structures between parameters of interest.

Using numeric or raster data, this package contains functions to calculate: complete water balance, bioclimatic balance, bioclimatic intensities, reports for individual locations, multi-layered rasters for spatial analysis.

This package provides functions for blind source separation over multivariate spatial data, and useful statistics for evaluating performance of estimation on mixing matrix. BSSoverSpace is based on an eigen analysis of a positive definite matrix defined in terms of multiple normalized spatial local covariance matrices, and thus can handle moderately high-dimensional random fields. This package is an implementation of the method described in Zhang, Hao and Yao (2022)<arXiv:2201.02023>.

Computes exact bounds of Spearman's footrule in the presence of missing data, and performs independence test based on the bounds with controlled Type I error regardless of the values of missing data. Suitable only for distinct, univariate data where no ties is allowed.

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 novel data-augmentation Markov chain Monte Carlo sampling algorithm to fit a progressive compartmental model of disease in a Bayesian framework Morsomme, R.N., Holloway, S.T., Ryser, M.D. and Xu J. (2024) <doi:10.48550/arXiv.2408.14625>.

This package provides a Bayesian latent space model for complex networks, either weighted or unweighted. Given an observed input graph, the estimates for the latent coordinates of the nodes are obtained through a Bayesian MCMC algorithm. The overall likelihood of the graph depends on a fundamental probability equation, which is defined so that ties are more likely to exist between nodes whose latent space coordinates are close. The package is mainly based on the model by Hoff, Raftery and Handcock (2002) <doi:10.1198/016214502388618906> and contains some extra features (e.g., removal of the Procrustean step, weights implemented as coefficients of the latent distances, 3D plots). The original code related to the above model was retrieved from <https://www.stat.washington.edu/people/pdhoff/Code/hoff_raftery_handcock_2002_jasa/>. Users can inspect the MCMC simulation, create and customize insightful graphical representations or apply clustering techniques.

Convert fitted objects from various R mixed-model packages into tidy data frames along the lines of the broom package. The package provides three S3 generics for each model: tidy(), which summarizes a model's statistical findings such as coefficients of a regression; augment(), which adds columns to the original data such as predictions, residuals and cluster assignments; and glance(), which provides a one-row summary of model-level statistics.

To perform species identification using DNA barcodes.

Create a blended curve from two survival curves, which is particularly useful for survival extrapolation in health technology assessment. The main idea is to mix a flexible model that fits the observed data well with a parametric model that encodes assumptions about long-term survival. The two curves are blended into a single survival curve that is identical to the first model over the range of observed times and gradually approaches the parametric model over the extrapolation period based on a given weight function. This approach allows for the inclusion of external information, such as data from registries or expert opinion, to guide long-term extrapolations, especially when dealing with immature trial data. See Che et al. (2022) <doi:10.1177/0272989X221134545>.

Survey systems and other third-party data sources commonly use non-standard representations of logical values when it comes to qualitative data - "Yes", "No" and "N/A", say. batman is a package designed to seamlessly convert these into logicals. It is highly localised, and contains equivalents to boolean values in languages including German, French, Spanish, Italian, Turkish, Chinese and Polish.

Fits Bayesian nonlinear Ornstein-Uhlenbeck models with cubic drift, stochastic volatility, and Student-t innovations. The package implements hierarchical priors for sector-specific parameters and supports parallel MCMC sampling via Stan'. Model comparison is performed using Pareto Smoothed Importance Sampling Leave-One-Out (PSIS-LOO) cross-validation following Vehtari, Gelman, and Gabry (2017) <doi:10.1007/s11222-016-9696-4>. Prior specifications follow recommendations from Gelman (2006) <doi:10.1214/06-BA117A> for scale parameters.

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.

This package provides likelihood-based and hierarchical estimation methods for thresholded (binomial-probit) data. Supports fixed-mean and random-mean models with maximum likelihood estimation (MLE), generalized linear mixed model (GLMM), and Bayesian Markov chain Monte Carlo (MCMC) implementations. For methodological background, see Albert and Chib (1993) <doi:10.1080/01621459.1993.10476321> and McCulloch (1994) <doi:10.2307/2297959>.

The network autocorrelation model (NAM) can be used for studying the degree of social influence regarding an outcome variable based on one or more known networks. The degree of social influence is quantified via the network autocorrelation parameters. In case of a single network, the Bayesian methods of Dittrich, Leenders, and Mulder (2017) <DOI:10.1016/j.socnet.2016.09.002> and Dittrich, Leenders, and Mulder (2019) <DOI:10.1177/0049124117729712> are implemented using a normal, flat, or independence Jeffreys prior for the network autocorrelation. In the case of multiple networks, the Bayesian methods of Dittrich, Leenders, and Mulder (2020) <DOI:10.1177/0081175020913899> are implemented using a multivariate normal prior for the network autocorrelation parameters. Flat priors are implemented for estimating the coefficients. For Bayesian testing of equality and order-constrained hypotheses, the default Bayes factor of Gu, Mulder, and Hoijtink, (2018) <DOI:10.1111/bmsp.12110> is used with the posterior mean and posterior covariance matrix of the NAM parameters based on flat priors as input.

Generalization of the Bayesian classification and regression tree (CART) model that partitions subjects into terminal nodes and tailors regression model to each terminal node.

This package provides a wrapper to allow users to download Bus Open Data Service BODS transport information from the API (<https://www.bus-data.dft.gov.uk/>). This includes timetable and fare metadata (including links for full datasets), timetable data at line level, and real-time location data.

This package provides tools designed to make it easier for beginner and intermediate users to build and validate binary logistic regression models. Includes bivariate analysis, comprehensive regression output, model fit statistics, variable selection procedures, model validation techniques and a shiny app for interactive model building.

Comprehensive toolkit for valid spatial, temporal, and grouped model evaluation. Automatically detects data dependencies (spatial autocorrelation, temporal structure, clustered observations), generates appropriate cross-validation schemes (spatial blocking, checkerboard, hexagonal, KNNDM, environmental blocking, leave-location-out, purged CV), and validates evaluation pipelines for leakage. Includes area of applicability (AOA) assessment following Meyer & Pebesma (2021) <doi:10.1111/2041-210X.13650>, forward feature selection with blocked CV, spatial thinning, block-permutation variable importance, extrapolation detection, and interactive visualizations. Integrates with tidymodels', caret', mlr3', ENMeval', and biomod2'. Based on evaluation principles described in Roberts et al. (2017) <doi:10.1111/ecog.02881>, Kaufman et al. (2012) <doi:10.1145/2382577.2382579>, Kapoor & Narayanan (2023) <doi:10.1016/j.patter.2023.100804>, and Linnenbrink et al. (2024) <doi:10.5194/gmd-17-5897-2024>.