Calculates the Boltzmann entropy of a landscape gradient. This package uses the analytical method created by Gao, P., Zhang, H. and Li, Z., 2018 (<doi:10.1111/tgis.12315>) and by Gao, P. and Li, Z., 2019 (<doi:10.1007/s10980-019-00854-3>). It also extend the original ideas by allowing calculations on data with missing values.

An integrated set of tools to analyze and simulate networks based on exponential-family random graph models (ERGMs). ergm is a part of the Statnet suite of packages for network analysis. See Hunter, Handcock, Butts, Goodreau, and Morris (2008) <doi:10.18637/jss.v024.i03> and Krivitsky, Hunter, Morris, and Klumb (2023) <doi:10.18637/jss.v105.i06>.

Lognormal models have broad applications in various research areas such as economics, actuarial science, biology, environmental science and psychology. The estimation problem in lognormal models has been extensively studied. This R package fuel implements thirty-nine existing and newly proposed estimators. See Zhang, F., and Gou, J. (2020), A unified framework for estimation in lognormal models, Technical report.

This package implements the generalized Gauss Markov regression, this is useful when both predictor and response have uncertainty attached to them and also when covariance within the predictor, within the response and between the predictor and the response is present. Base on the results published in guide ISO/TS 28037 (2010) <https://www.iso.org/standard/44473.html>.

The matrix factor model has drawn growing attention for its advantage in achieving two-directional dimension reduction simultaneously for matrix-structured observations. In contrast to the Principal Component Analysis (PCA)-based methods, we propose a simple Iterative Alternating Least Squares (IALS) algorithm for matrix factor model, see the details in He et al. (2023) <arXiv:2301.00360>.

In view of the analysis of the structural characteristics of the tripartite network has been complete, however, there is still a lack of a unified operation that can quickly obtain the corresponding characteristics of the tripartite network. To solve this insufficiency, ILSM was designed for supporting calculating such metrics of tripartite networks by functions of this R package.

An implementation of the Log Cumulative Probability Model (LCPM) and Proportional Probability Model (PPM) for which the Maximum Likelihood Estimates are determined using constrained optimization. This implementation accounts for the implicit constraints on the parameter space. Other features such as standard errors, z tests and p-values use standard methods adapted from the results based on constrained optimization.

This package contains the data sets for the first and second editions of the textbook "Mathematical Modeling and Applied Calculus" by Joel Kilty and Alex M. McAllister. The first edition of the book was published by Oxford University Press in 2018 with ISBN-13: 978-019882472. The second edition is expected to be published in January 2027.

An implementation of the Rapid Assessment Method for Older People or RAM-OP <https://www.helpage.org/resource/rapid-assessment-method-for-older-people-ramop-manual/>. It provides various functions that allow the user to design and plan the assessment and analyse the collected data. RAM-OP provides accurate and reliable estimates of the needs of older people.

This package provides a collection of functions to do model-based phylogenetic analysis. It includes functions to calculate community phylogenetic diversity, to estimate correlations among functional traits while accounting for phylogenetic relationships, and to fit phylogenetic generalized linear mixed models. The Bayesian phylogenetic generalized linear mixed models are fitted with the INLA package (<https://www.r-inla.org>).

Carries out model-based clustering or classification using parsimonious Gaussian mixture models. McNicholas and Murphy (2008) <doi:10.1007/s11222-008-9056-0>, McNicholas (2010) <doi:10.1016/j.jspi.2009.11.006>, McNicholas and Murphy (2010) <doi:10.1093/bioinformatics/btq498>, McNicholas et al. (2010) <doi:10.1016/j.csda.2009.02.011>.

Load the Just Another Gibbs Sampling (JAGS) module pexm'. The module provides the tools to work with the Piecewise Exponential (PE) distribution in a Bayesian model with the corresponding Markov Chain Monte Carlo algorithm (Gibbs Sampling) implemented via JAGS. Details about the module implementation can be found in Mayrink et al. (2021) <doi:10.18637/jss.v100.i08>.

This package provides a collection of functions that enable easy access and updating of a database of data over time. More specifically, the package facilitates type-2 history for data-warehouses and provides a number of Quality of life improvements for working on SQL databases with R. For reference see Ralph Kimball and Margy Ross (2013, ISBN 9781118530801).

This package implements stagewise regression for variable selection in joint models of recurrent events and terminal events (semi-competing risks). Supports two model frameworks: the joint frailty model (Cox-type) and the joint scale-change model (AFT-type). Provides cooperative lasso, lasso, and group lasso penalties with cross-validation for tuning parameter selection via cross-fitted estimating equations.

This package provides functions to produce a fully fledged geo-spatial object extent as a SpatialPolygonsDataFrame'. Also included are functions to generate polygons from raster data using quadmesh techniques, a round number buffered extent, and general spatial-extent and raster-like extent helpers missing from the originating packages. Some latitude-based tools for polar maps are included.

This package provides simple and powerful interfaces that facilitate interaction with ODBC data sources. Each data source gets its own unique and dedicated interface, wrapped around RODBC'. Communication settings are remembered between queries, and are managed silently in the background. The interfaces support multi-statement SQL scripts, which can be parameterised via metaprogramming structures and embedded R expressions.

Link R with Transformers from Hugging Face to transform text variables to word embeddings; where the word embeddings are used to statistically test the mean difference between set of texts, compute semantic similarity scores between texts, predict numerical variables, and visual statistically significant words according to various dimensions etc. For more information see <https://www.r-text.org>.

This package provides functions for performing time domain signal coding as used in Chesmore (2001) <doi:10.1016/S0003-682X(01)00009-3>, and related tasks. This package creates the standard S-matrix and A-matrix (with variable lag), has tools to convert coding matrices into distributed matrices, provides published codebooks and allows for extraction of code sequences.

Binary ties limit the richness of network analyses as relations are unique. The two-mode structure contains a number of features lost when projection it to a one-mode network. Longitudinal datasets allow for an understanding of the causal relationship among ties, which is not the case in cross-sectional datasets as ties are dependent upon each other.

This package provides a tool to define the rare biosphere. ulrb solves the problem of the definition of rarity by replacing arbitrary thresholds with an unsupervised machine learning algorithm (partitioning around medoids, or k-medoids). This algorithm works for any type of microbiome data, provided there is an abundance table. This method also works for non-microbiome data.

Semiparametric modeling of lifetime data with crossing survival curves via Yang and Prentice model with piecewise exponential baseline distribution. Details about the model can be found in Demarqui and Mayrink (2019) <arXiv:1910.02406>. Model fitting carried out via likelihood-based and Bayesian approaches. The package also provides point and interval estimation for the crossing survival times.

Fit and simulate any kind of physiologically-based kinetic ('PBK') models whatever the number of compartments. Moreover, it allows to account for any link between pairs of compartments, as well as any link of each of the compartments with the external medium. Such generic PBK models have today applications in pharmacology (PBPK models) to describe drug effects, in toxicology and ecotoxicology (PBTK models) to describe chemical substance effects. In case of exposure to a parent compound (drug or chemical) the rPBK package allows to consider metabolites, whatever their number and their phase (I, II, ...). Last but not least, package rPBK can also be used for dynamic flux balance analysis (dFBA) to deal with metabolic networks. See also Charles et al. (2022) <doi:10.1101/2022.04.29.490045>.

This package provides pointwise and uniform estimation and inference methods for boundary discontinuity (BD) designs, a causal inference design that generalizes univariate regression discontinuity (RD) designs to settings with bivariate scores. Implements local polynomial methods for location-based and distance-based analyses, including sharp and fuzzy designs, data-driven bandwidth selection, pointwise confidence intervals, and uniform confidence bands. Methodology is developed in Cattaneo, Titiunik, and Yu (2026) <doi:10.48550/arXiv.2505.05670> for location-based methods and Cattaneo, Titiunik, and Yu (2026) <doi:10.48550/arXiv.2510.26051> for distance-based methods. For an overview and empirical guidance, see Cattaneo, Titiunik, and Yu (2026) <doi:10.48550/arXiv.2511.06474>. The companion software article is Cattaneo, Titiunik, and Yu (2025) <doi:10.48550/arXiv.2505.07989>.

This package contains utilities for the analysis of Michaelian kinetic data. Beside the classical linearization methods (Lineweaver-Burk, Eadie-Hofstee, Hanes-Woolf and Eisenthal-Cornish-Bowden), features include the ability to carry out weighted regression analysis that, in most cases, substantially improves the estimation of kinetic parameters (Aledo (2021) <doi:10.1002/bmb.21522>). To avoid data transformation and the potential biases introduced by them, the package also offers functions to directly fitting data to the Michaelis-Menten equation, either using ([S], v) or (time, [S]) data. Utilities to simulate substrate progress-curves (making use of the Lambert W function) are also provided. The package is accompanied of vignettes that aim to orientate the user in the choice of the most suitable method to estimate the kinetic parameter of an Michaelian enzyme.