This package provides a convenient R wrapper to the Comet API, which is a cloud platform allowing you to track, compare, explain and optimize machine learning experiments and models. Experiments can be viewed on the Comet online dashboard at <https://www.comet.com>.

This package implements the nonparametric moving sum procedure for detecting changes in the joint characteristic function (NP-MOJO) for multiple change point detection in multivariate time series. See McGonigle, E. T., Cho, H. (2025) <doi:10.1093/biomet/asaf024> for description of the NP-MOJO methodology.

This package implements the semiparametric efficient estimators of continuous-time causal models for time-varying treatments and confounders in the presence of dependent censoring (including structural failure time model and Cox proportional hazards marginal structural model). S. Yang, K. Pieper, and F. Cools (2019) <doi:10.1111/biom.12845>.

This package provides SPSS'- and SAS'-like output for cross tabulations of two categorical variables (CROSSTABS) and for hierarchical loglinear analyses of two or more categorical variables (LOGLINEAR). The methods are described in Agresti (2013, ISBN:978-0-470-46363-5), Ajzen & Walker (2021, ISBN:9780429330308), Field (2018, ISBN:9781526440273), Norusis (2012, ISBN:978-0-321-74843-0), Nussbaum (2015, ISBN:978-1-84872-603-1), Stevens (2009, ISBN:978-0-8058-5903-4), Tabachnik & Fidell (2019, ISBN:9780134790541), and von Eye & Mun (2013, ISBN:978-1-118-14640-8).

Produce forest plots to visualize covariate effects using either the command line or an interactive Shiny application.

This package provides the official administrative boundaries of the Azores (Região Autónoma dos Açores (RAA)) as defined in the 2024 edition of the Carta Administrativa Oficial de Portugal (CAOP), published by the Direção-Geral do Território (DGT). The package includes convenience functions to import these boundaries as sf objects for spatial analysis in R. Source: <https://geo2.dgterritorio.gov.pt/caop/CAOP_RAA_2024-gpkg.zip>.

Access chemical, hazard, bioactivity, and exposure data from the Computational Toxicology and Exposure ('CTX') APIs <https://api-ccte.epa.gov/docs/>. ccdR was developed to streamline the process of accessing the information available through the CTX APIs without requiring prior knowledge of how to use APIs. Most data is also available on the CompTox Chemical Dashboard ('CCD') <https://comptox.epa.gov/dashboard/> and other resources found at the EPA Computational Toxicology and Exposure Online Resources <https://www.epa.gov/comptox-tools>.

Detects a variety of coordinated actions on social media and outputs the network of coordinated users along with related information.

This package provides a collection of common test and item analyses from a classical test theory (CTT) framework. Analyses can be applied to both dichotomous and polytomous data. Functions provide reliability analyses (alpha), item statistics, disctractor analyses, disattenuated correlations, scoring routines, and empirical ICCs.

This package provides a general toolkit for drug target identification. We include functionality to reduce large graphs to subgraphs and prioritize nodes. In addition to being optimized for use with generic graphs, we also provides support to analyze protein-protein interactions networks from online repositories. For more details on core method, refer to Weaver et al. (2021) <https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008755>.

Read tables chunk by chunk using a C++ backend and a simple R interface.

Convex Partition is a black-box optimisation algorithm for single objective real-parameters functions. The basic principle is to progressively estimate and exploit a regression tree similar to a CART (Classification and Regression Tree) of the objective function. For more details see de Paz (2024) <doi:10.1007/978-3-031-62836-8_3> and Loh (2011) <doi:10.1002/widm.8> .

It is devoted to Cramer-von Mises goodness-of-fit tests. It implements three statistical methods based on Cramer-von Mises statistics to estimate and test a regression model.

General functions for convolutions of data. Moving average, running median, and other filters are available. Bibliography regarding the functions can be found in the following text. Richard G. Brereton (2003) <ISBN:9780471489771>.

Random sampling from distributions with user-specified population covariance matrix. Marginal information may be fully specified, for which the package implements the VITA (VIne-To-Anything) algorithm Grønneberg and Foldnes (2017) <doi:10.1007/s11336-017-9569-6>. See also Grønneberg, Foldnes and Marcoulides (2022) <doi:10.18637/jss.v102.i03>. Alternatively, marginal skewness and kurtosis may be specified, for which the package implements the IG (independent generator) and PLSIM (piecewise linear) algorithms, see Foldnes and Olsson (2016) <doi:10.1080/00273171.2015.1133274> and Foldnes and Grønneberg (2021) <doi:10.1080/10705511.2021.1949323>, respectively.

Calculates the probabilities of k successes given n trials of a binomial random variable with non-negative correlation across trials. The function takes as inputs the scalar values the level of correlation or association between trials, the success probability, the number of trials, an optional input specifying the number of bits of precision used in the calculation, and an optional input specifying whether the calculation approach to be used is from Witt (2014) <doi:10.1080/03610926.2012.725148> or from Kuk (2004) <doi:10.1046/j.1467-9876.2003.05369.x>. The output is a (trials+1)-dimensional vector containing the likelihoods of 0, 1, ..., trials successes.

This package performs simulation-based inference as an alternative to the delta method for obtaining valid confidence intervals and p-values for regression post-estimation quantities, such as average marginal effects and predictions at representative values. This framework for simulation-based inference is especially useful when the resulting quantity is not normally distributed and the delta method approximation fails. The methodology is described in Greifer, et al. (2025) <doi:10.32614/RJ-2024-015>. clarify is meant to replace some of the functionality of the archived package Zelig'; see the vignette "Translating Zelig to clarify" for replicating this functionality.

Implementation of Clarke's distribution-free test of non-nested models. Currently supported model functions are: lm(), glm() ('binomial', poisson', negative binomial links), polr() ('MASS'), clm() ('ordinal'), and multinom() ('nnet'). For more information on the test, see Clarke (2007) <doi:10.1093/pan/mpm004>.

An interface for creating new condition generators objects. Generators are special functions that can be saved in registries and linked to other functions. Utilities for documenting your generators, and new conditions is provided for package development.

This package provides a method for determining groups in multiple curves with an automatic selection of their number based on k-means or k-medians algorithms. The selection of the optimal number is provided by bootstrap methods. The methodology can be applied both in regression and survival framework. Implemented methods are: Grouping multiple survival curves described by Villanueva et al. (2018) <doi:10.1002/sim.8016>.

Colorful Data Frames in the terminal. The new class does change the behaviour of any of the objects, but adds a style definition and a print method. Using ANSI escape codes, it colors the terminal output of data frames. Some column types (such as p-values and identifiers) are automatically recognized.

Interface with and extract data from the United Nations Comtrade API <https://comtradeplus.un.org/>. Comtrade provides country level shipping data for a variety of commodities, these functions allow for easy API query and data returned as a tidy data frame.

This package provides an R interface to the CVD Prevent application programming interface (API), allowing users to retrieve and analyse cardiovascular disease prevention data from primary care records across England. The Cardiovascular Disease Prevention Audit (CVDPREVENT) automatically extracts routinely held GP health data to support national reporting and improvement initiatives. See the API documentation for details: <https://bmchealthdocs.atlassian.net/wiki/spaces/CP/pages/317882369/CVDPREVENT+API+Documentation>.

Retail shopping transactions for 2,469 households over one year. Originates from the 84.51° Complete Journey 2.0 source files <https://www.8451.com/area51> which also includes useful metadata on products, coupons, campaigns, and promotions.