Includes wrapper functions around existing functions for the analysis of categorical data and introduces functions for calculating risk differences and matched odds ratios. R currently supports a wide variety of tools for the analysis of categorical data. However, many functions are spread across a variety of packages with differing syntax and poor compatibility with each another. prop_test() combines the functions binom.test(), prop.test() and BinomCI() into one output. prop_power() allows for power and sample size calculations for both balanced and unbalanced designs. riskdiff() is used for calculating risk differences and matched_or() is used for calculating matched odds ratios. For further information on methods used that are not documented in other packages see Nathan Mantel and William Haenszel (1959) <doi:10.1093/jnci/22.4.719> and Alan Agresti (2002) <ISBN:0-471-36093-7>.

Predicts categorical or continuous outcomes while concentrating on a number of key points. These are Cross-validation, Accuracy, Regression and Rule of Ten or "one in ten rule" (CARRoT), and, in addition to it R-squared statistics, prior knowledge on the dataset etc. It performs the cross-validation specified number of times by partitioning the input into training and test set and fitting linear/multinomial/binary regression models to the training set. All regression models satisfying chosen constraints are fitted and the ones with the best predictive power are given as an output. Best predictive power is understood as highest accuracy in case of binary/multinomial outcomes, smallest absolute and relative errors in case of continuous outcomes. For binary case there is also an option of finding a regression model which gives the highest AUROC (Area Under Receiver Operating Curve) value. The option of parallel toolbox is also available. Methods are described in Peduzzi et al. (1996) <doi:10.1016/S0895-4356(96)00236-3> , Rhemtulla et al. (2012) <doi:10.1037/a0029315>, Riley et al. (2018) <doi:10.1002/sim.7993>, Riley et al. (2019) <doi:10.1002/sim.7992>.

Causal network analysis methods for regulator prediction and network reconstruction from genome scale data.

This package implements the board game CamelUp for use in introductory statistics classes using a Shiny app.

This package provides datasets to accompany J. Fox and S. Weisberg, An R Companion to Applied Regression, Third Edition, Sage.

This package provides tools for the calibration of penalized criteria for model selection. The calibration methods available are based on the slope heuristics.

Simulate plasma caffeine concentrations using population pharmacokinetic model described in Lee, Kim, Perera, McLachlan and Bae (2015) <doi:10.1007/s00431-015-2581-x>.

This tool performs pairwise correlation analysis and estimate causality. Particularly, it is useful for detecting the metabolites that would be altered by the gut bacteria.

Git hook scripts are useful for identifying simple issues before submission to code review. captain (hook) is an R package to manage and run git pre-commit hooks.

Computer algebra via the SymPy library (<https://www.sympy.org/>). This makes it possible to solve equations symbolically, find symbolic integrals, symbolic sums and other important quantities.

This R package contains examples from the book Regression for Categorical Data, Tutz 2012, Cambridge University Press. The names of the examples refer to the chapter and the data set that is used.

This package provides a modeling tool allowing gene selection, reverse engineering, and prediction in cascade networks. Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014) <doi:10.1093/bioinformatics/btt705>.

This package provides methods and utilities for testing, identifying, selecting and mutating objects as categorical or continous types. These functions work on both atomic vectors as well as recursive objects: data.frames, data.tables, tibbles, lists, etc..

This package contains the function calendR() for creating fully customizable monthly and yearly calendars (colors, fonts, formats, ...) and even heatmap calendars. In addition, it allows saving the calendars in ready to print A4 format PDF files.

This package contains several basic utility functions including: moving (rolling, running) window statistic functions, read/write for GIF and ENVI binary files, fast calculation of AUC, LogitBoost classifier, base64 encoder/decoder, round-off-error-free sum and cumsum, etc.

Explore and normalize American campaign finance data. Created by the Investigative Reporting Workshop to facilitate work on The Accountability Project, an effort to collect public data into a central, standard database that is more easily searched: <https://publicaccountability.org/>.

Accelerate Bayesian analytics workflows in R through interactive modelling, visualization, and inference. Define probabilistic graphical models using directed acyclic graphs (DAGs) as a unifying language for business stakeholders, statisticians, and programmers. This package relies on interfacing with the numpyro python package.

This package contains functions to estimate the Correlation-Adjusted Regression Survival (CARS) Scores. The method is described in Welchowski, T. and Zuber, V. and Schmid, M., (2018), Correlation-Adjusted Regression Survival Scores for High-Dimensional Variable Selection, <arXiv:1802.08178>.

The caRamel optimizer has been developed to meet the requirement for an automatic calibration procedure that delivers a family of parameter sets that are optimal with regard to a multi-objective target (Monteil et al. <doi:10.5194/hess-24-3189-2020>).

Evaluation of the Carlson elliptic integrals and the incomplete elliptic integrals with complex arguments. The implementations use Carlson's algorithms <doi:10.1007/BF02198293>. Applications of elliptic integrals include probability distributions, geometry, physics, mechanics, electrodynamics, statistical mechanics, astronomy, geodesy, geodesics on conics, and magnetic field calculations.

This package provides methods for caching or memoization of objects and results. With this package, any R object can be cached in a key-value storage where the key can be an arbitrary set of R objects. The cache memory is persistent (on the file system).

This package provides a toolkit to perform cross-species analysis based on scRNA-seq data. This package contains 5 main features. (1) identify Markers in each cluster. (2) Cell type annotation (3) identify conserved markers. (4) identify conserved cell types. (5) identify conserved modules of regulatory networks.

Parameters of a user-specified probability distribution are modelled by a multi-layer perceptron artificial neural network. This framework can be used to implement probabilistic nonlinear models including mixture density networks, heteroscedastic regression models, zero-inflated models, etc. following Cannon (2012) <doi:10.1016/j.cageo.2011.08.023>.

This package provides access to the Calcite Design System javascript components via integration with the htmltools and shiny packages. Pre-built and interactive components can be used to generate either static html or interactive web applications. Learn more about the Calcite Design System at <https://developers.arcgis.com/calcite-design-system/>.