Build regular expressions piece by piece using human readable code. This package contains number-related functionality, and is primarily intended to be used by package developers.

Load data by campaigns, ads, ad sets and insights, ad account and business manager from Facebook Marketing API into R. For more details see official documents by Facebook Marketing API <https://developers.facebook.com/documentation/ads-commerce/marketing-api>.

Simulation of random orthonormal matrices from linear and quadratic exponential family distributions on the Stiefel manifold. The most general type of distribution covered is the matrix-variate Bingham-von Mises-Fisher distribution. Most of the simulation methods are presented in Hoff(2009) "Simulation of the Matrix Bingham-von Mises-Fisher Distribution, With Applications to Multivariate and Relational Data" <doi:10.1198/jcgs.2009.07177>. The package also includes functions for optimization on the Stiefel manifold based on algorithms described in Wen and Yin (2013) "A feasible method for optimization with orthogonality constraints" <doi:10.1007/s10107-012-0584-1>.

This package provides tools to automate the morphological delineation of riverside urban areas based on a method introduced in Forgaci (2018) <doi:10.7480/abe.2018.31>. Delineation entails the identification of corridor boundaries, segmentation of the corridor, and delineation of the river space using two-dimensional spatial information from street network data and digital elevation data in a projected CRS. The resulting delineation can be used to characterise spatial phenomena that can be related to the river as a central element.

Facilities for running simulations from ordinary differential equation ('ODE') models, such as pharmacometrics and other compartmental models. A compilation manager translates the ODE model into C, compiles it, and dynamically loads the object code into R for improved computational efficiency. An event table object facilitates the specification of complex dosing regimens (optional) and sampling schedules. NB: The use of this package requires both C and Fortran compilers, for details on their use with R please see Section 6.3, Appendix A, and Appendix D in the "R Administration and Installation" manual. Also the code is mostly released under GPL. The VODE and LSODA are in the public domain. The vendored SUNDIALS CVODE sources and headers are released under the BSD-3-Clause license. The information is available in the inst/COPYRIGHTS.

This package provides access to Rangeland Analysis Platform (RAP) products <https://rangelands.app/products> for arbitrary extents via GDAL virtual file system.

This package provides an R6 class and several utility methods to facilitate the implementation of models based on ordinary differential equations. The heart of the package is a code generator that creates compiled Fortran (or R') code which can be passed to a numerical solver. There is direct support for solvers contained in packages deSolve and rootSolve'.

Recursive partitioning methods to build classification trees for ordinal responses within the CART framework. Trees are grown using the Generalized Gini impurity function, where the misclassification costs are given by the absolute or squared differences in scores assigned to the categories of the response. Pruning is based on the total misclassification rate or on the total misclassification cost.

Model relational and durational events in a counting process framework, with functions for estimating and simulating Relational Event Models (REM) and Durational Event Models (DEM). Includes support for time-varying covariates, windowed statistics, and high-dimensional node-level fixed effects. References include Fritz et al. (2026) "Scalable Durational Event Models: Application to Physical and Digital Interactions" <doi:10.48550/arXiv.2504.00049>.

This package provides a programmatic interface to web-services of YouTheria. YouTheria is an online database of mammalian trait data <http://www.utheria.org/>.

Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Candes, E. J., Li, X., Ma, Y., & Wright, J. (2011). Robust principal component analysis?. Journal of the ACM (JACM), 58(3), 11. prove that we can recover each component individually under some suitable assumptions. It is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the L1 norm. This package implements this decomposition algorithm resulting with Robust PCA approach.

TROLL is coded in C++ and it typically simulates hundreds of thousands of individuals over hundreds of years. The rcontroll R package is a wrapper of TROLL'. rcontroll includes functions that generate inputs for simulations and run simulations. Finally, it is possible to analyse the TROLL outputs through tables, figures, and maps taking advantage of other R visualisation packages. rcontroll also offers the possibility to generate a virtual LiDAR point cloud that corresponds to a snapshot of the simulated forest.

Determination of rainfall-runoff erosivity factor.

General purpose R client for ERDDAPâ ¢ servers. Includes functions to search for datasets', get summary information on datasets', and fetch datasets', in either csv or netCDF format. ERDDAPâ ¢ information: <https://upwell.pfeg.noaa.gov/erddap/information.html>.

Data cleaning including 1) generating datasets for time-series and case-crossover analyses based on raw hospital records, 2) linking individuals to an areal map, 3) picking out cases living within a buffer of certain size surrounding a site, etc. For more information, please refer to Zhang W,etc. (2018) <doi:10.1016/j.envpol.2018.08.030>.

This package provides complete functionality to analyse data from repeated measures experiments with hierarchical or crossed experimental designs. Supports testing modeling assumptions, identifying outlier observations and experimental units, estimating statistical power, and performing sample size calculations. Uses linear mixed effects models via lme4 and simulation-based power analysis via simr'. Handles both normal and non-normal error distributions including binomial and Poisson families. For more details see Shin et al. (2022) <doi:10.1101/2022.07.18.500490>, Bates et al. (2015) <doi:10.18637/jss.v067.i01>, Green and MacLeod (2016) <doi:10.1111/2041-210X.12504>, Hartig (2024) <doi:10.32614/CRAN.package.DHARMa>, Nieuwenhuis et al. (2012) <doi:10.32614/RJ-2012-011>, Millard (2013) <doi:10.1007/978-1-4614-8456-1> and Kuznetsova et al. (2017) <doi:10.18637/jss.v082.i13>.

Efficient reading of raw markdown tables into tibbles. Designed to accept content from strings, files, and URLs with the ability to extract and read multiple tables from markdown for analysis.

This package provides robust parameter tuning and model training for predictive models applied across data sources where the data distribution varies slightly from source to source. This package implements three primary tuning methods: cross-validation-based internal tuning, external tuning, and the RobustTuneC method. External tuning includes a conservative option where parameters are tuned internally on the training data and validating on an external dataset, providing a slightly pessimistic estimate. It supports Lasso, Ridge, Random Forest, Boosting, and Support Vector Machine classifiers. Currently, only binary classification is supported. The response variable must be the first column of the dataset and a factor with exactly two levels. The tuning methods are based on the paper by Nicole Ellenbach, Anne-Laure Boulesteix, Bernd Bischl, Kristian Unger, and Roman Hornung (2021) "Improved Outcome Prediction Across Data Sources Through Robust Parameter Tuning" <doi:10.1007/s00357-020-09368-z>.

This package provides a lightweight, table-driven engine for executing insurance rating plans, including coverage-specific rating specifications, entity aggregation, and trace output for auditing. Given policy data, an ordered rating specification, and rating factor tables, it returns rated policies and, optionally, a step-by-step trace of the calculation.

Non-linear transformations of data to better discover latent effects. Applies a sequence of three transformations (1) a Gaussianizing transformation, (2) a Z-score transformation, and (3) an outlier removal transformation. A publication describing the method has the following citation: Gregory J. Hunt, Mark A. Dane, James E. Korkola, Laura M. Heiser & Johann A. Gagnon-Bartsch (2020) "Automatic Transformation and Integration to Improve Visualization and Discovery of Latent Effects in Imaging Data", Journal of Computational and Graphical Statistics, <doi:10.1080/10618600.2020.1741379>.

This package contains basic tools for visualizing, interpreting, and building regression models. It has been designed for use with the book Introduction to Regression and Modeling with R by Adam Petrie, Cognella Publishers, ISBN: 978-1-63189-250-9.

This package provides an R scripting interface to the open-source SAGA-GIS (System for Automated Geoscientific Analyses Geographical Information System) software. Rsagacmd dynamically generates R functions for every SAGA-GIS geoprocessing tool based on the user's currently installed SAGA-GIS version. These functions are contained within an S3 object and are accessed as a named list of libraries and tools. This structure facilitates an easier scripting experience by organizing the large number of SAGA-GIS geoprocessing tools (>700) by their respective library. Interactive scripting can fully take advantage of code autocompletion tools (e.g. in RStudio'), allowing for each tools syntax to be quickly recognized. Furthermore, the most common types of spatial data (via the terra', sp', and sf packages) along with non-spatial data are automatically passed from R to the SAGA-GIS command line tool for geoprocessing operations, and the results are loaded as the appropriate R object. Outputs from individual SAGA-GIS tools can also be chained using pipes from the magrittr and dplyr packages to combine complex geoprocessing operations together in a single statement. SAGA-GIS is available under a GPLv2 / LGPLv2 licence from <https://sourceforge.net/projects/saga-gis/> including Windows x86/x64 and macOS binaries. SAGA-GIS is also included in Debian/Ubuntu default software repositories. Rsagacmd has currently been tested on SAGA-GIS versions from 2.3.1 to 9.5.1 on Windows, Linux and macOS.

An R command interface to the MLwiN multilevel modelling software package.

This package provides a collection of small text corpora of interesting data. It contains all data sets from dariusk/corpora'. Some examples: names of animals: birds, dinosaurs, dogs; foods: beer categories, pizza toppings; geography: English towns, rivers, oceans; humans: authors, US presidents, occupations; science: elements, planets; words: adjectives, verbs, proverbs, US president quotes.