This package implements an MCMC algorithm to estimate a hierarchical multinomial logit model with a normal heterogeneity distribution. The algorithm uses a hybrid Gibbs Sampler with a random walk metropolis step for the MNL coefficients for each unit. Dependent variable may be discrete or continuous. Independent variables may be discrete or continuous with optional order constraints. Means of the distribution of heterogeneity can optionally be modeled as a linear function of unit characteristics variables.

When causal quantities are not identifiable from the observed data, it still may be possible to bound these quantities using the observed data. We outline a class of problems for which the derivation of tight bounds is always a linear programming problem and can therefore, at least theoretically, be solved using a symbolic linear optimizer. We extend and generalize the approach of Balke and Pearl (1994) <doi:10.1016/B978-1-55860-332-5.50011-0> and we provide a user friendly graphical interface for setting up such problems via directed acyclic graphs (DAG), which only allow for problems within this class to be depicted. The user can then define linear constraints to further refine their assumptions to meet their specific problem, and then specify a causal query using a text interface. The program converts this user defined DAG, query, and constraints, and returns tight bounds. The bounds can be converted to R functions to evaluate them for specific datasets, and to latex code for publication. The methods and proofs of tightness and validity of the bounds are described in a paper by Sachs, Jonzon, Gabriel, and Sjölander (2022) <doi:10.1080/10618600.2022.2071905>.

Computes and visualize the results of the 0-1 test for chaos proposed by Gottwald and Melbourne (2004) <DOI:10.1137/080718851>. The algorithm is available in parallel for the independent values of parameter c. Additionally, fast RQA is added to distinguish chaos from noise.

This package provides functions to check whether a vector of p-values respects the assumptions of FDR (false discovery rate) control procedures and to compute adjusted p-values.

Color palettes for EPL, MLB, NBA, NHL, and NFL teams.

Facilitate Pharmacokinetic (PK) and Pharmacodynamic (PD) modeling and simulation with powerful tools for Nonlinear Mixed-Effects (NLME) modeling. The package provides access to the same advanced Maximum Likelihood algorithms used by the NLME-Engine in the Phoenix platform. These tools support a range of analyses, from parametric methods to individual and pooled data, and support integrated use within the Pirana pharmacometric workbench <doi:10.1002/psp4.70067>. Execution is supported both locally or on remote machines.

The primary motivation of this package is to take the things that are great about the R packages flextable <https://davidgohel.github.io/flextable/> and officer <https://davidgohel.github.io/officer/>, take the standard and complex pieces of formatting clinical tables for regulatory use, and simplify the tedious pieces.

This package provides a system for creating R Markdown reports with a sequential syntax.

Extends the functionality of base R lists and provides specialized data structures deque', set', dict', and dict.table', the latter to extend the data.table package.

General functions for performing extreme value analysis on a circular domain as part of the statistical methodology in the paper by Konzen, E., Neves, C., and Jonathan, P. (2021). Modeling nonstationary extremes of storm severity: Comparing parametric and semiparametric inference. Environmetrics, 32(4), e2667.

This package provides easy access to historical climate data in Canada from R. Search for weather stations and download raw hourly, daily or monthly weather data across Canada from 1840 to present. Implements public API access as detailed at <https://climate.weather.gc.ca>.

Concordance probability estimate (CPE) is a commonly used performance measure in survival analysis that evaluates the predictive accuracy of a survival model. It measures how well a model can distinguish between pairs of individuals with different survival times. Specifically, it calculate the proportion of all pairs of individuals whose predicted survival times are correctly ordered.

This package implements the uniform scaled beta distribution and the continuous convolution kernel density estimator.

Supporting functionality to run caret with spatial or spatial-temporal data. caret is a frequently used package for model training and prediction using machine learning. CAST includes functions to improve spatial or spatial-temporal modelling tasks using caret'. It includes the newly suggested Nearest neighbor distance matching cross-validation to estimate the performance of spatial prediction models and allows for spatial variable selection to selects suitable predictor variables in view to their contribution to the spatial model performance. CAST further includes functionality to estimate the (spatial) area of applicability of prediction models. Methods are described in Meyer et al. (2018) <doi:10.1016/j.envsoft.2017.12.001>; Meyer et al. (2019) <doi:10.1016/j.ecolmodel.2019.108815>; Meyer and Pebesma (2021) <doi:10.1111/2041-210X.13650>; Milà et al. (2022) <doi:10.1111/2041-210X.13851>; Meyer and Pebesma (2022) <doi:10.1038/s41467-022-29838-9>; Linnenbrink et al. (2024) <doi:10.5194/gmd-17-5897-2024>; Schumacher et al. (2025) <doi:10.5194/gmd-18-10185-2025>. The package is described in detail in Meyer et al. (2026) <doi:10.1007/978-3-031-99665-8_11>.

This package provides a companion package to cmstatr <https://cran.r-project.org/package=cmstatr>. cmstatr contains statistical methods that are published in the Composite Materials Handbook, Volume 1 (2012, ISBN: 978-0-7680-7811-4), while cmstatrExt contains statistical methods that are not included in that handbook.

Quickly and easily create codebooks (i.e. data dictionaries) directly from a data frame.

Connect to WFP's Moda platform to R, download data, and obtain the list of individuals with access to the project along with their access level.

Computation of Multiscale Codependence Analysis and spatial eigenvector maps.

OpenAI's ChatGPT <https://chat.openai.com/> coding assistant for RStudio'. A set of functions and RStudio addins that aim to help the R developer in tedious coding tasks.

This package provides a simple set of classes and methods for mapping between scalar intensity values and colors. There is also support for layering maps on top of one another using alpha composition.

Supplies higher-order coordinatized data specification and fluid transform operators that include pivot and anti-pivot as special cases. The methodology is describe in Zumel', 2018, "Fluid data reshaping with cdata'", <https://winvector.github.io/FluidData/FluidDataReshapingWithCdata.html> , <DOI:10.5281/zenodo.1173299> . This package introduces the idea of explicit control table specification of data transforms. Works on in-memory data or on remote data using rquery and SQL database interfaces.

This package provides efficient implementation of the Cross-Covariance Isolate Detect (CCID) methodology for the estimation of the number and location of multiple change-points in the second-order (cross-covariance or network) structure of multivariate, possibly high-dimensional time series. The method is motivated by the detection of change points in functional connectivity networks for functional magnetic resonance imaging (fMRI), electroencephalography (EEG), magentoencephalography (MEG) and electrocorticography (ECoG) data. The main routines in the package have been extensively tested on fMRI data. For details on the CCID methodology, please see Anastasiou et al (2022), Cross-covariance isolate detect: A new change-point method for estimating dynamic functional connectivity. Medical Image Analysis, Volume 75.

Features tools for exploring congruent phylogenetic birth-death models. It can construct the pulled speciation- and net-diversification rates from a reference model. Given alternative speciation- or extinction rates, it can construct new models that are congruent with the reference model. Functionality is included to sample new rate functions, and to visualize the distribution of one congruence class. See also Louca & Pennell (2020) <doi:10.1038/s41586-020-2176-1>.

This package provides functions for the estimation of conditional copulas models, various estimators of conditional Kendall's tau (proposed in Derumigny and Fermanian (2019a, 2019b, 2020) <doi:10.1515/demo-2019-0016>, <doi:10.1016/j.csda.2019.01.013>, <doi:10.1016/j.jmva.2020.104610>), test procedures for the simplifying assumption (proposed in Derumigny and Fermanian (2017) <doi:10.1515/demo-2017-0011> and Derumigny, Fermanian and Min (2022) <doi:10.1002/cjs.11742>), and measures of non-simplifyingness (proposed in Derumigny (2025) <doi:10.48550/arXiv.2504.07704>).