This package implements multiple change searching algorithms for a variety of frequently considered parametric change-point models. In particular, it integrates a criterion proposed by Zou, Wang and Li (2020) <doi:10.1214/19-AOS1814> to select the number of change-points in a data-driven fashion. Moreover, it also provides interfaces for user-customized change-point models with one's own cost function and parameter estimation routine. It is easy to get started with the cpss.* set of functions by accessing their documentation pages (e.g., ?cpss).

This package provides text analysis in R, focusing on the use of a tokenized text format. In this format, the positions of tokens are maintained, and each token can be annotated (e.g., part-of-speech tags, dependency relations). Prominent features include advanced Lucene-like querying for specific tokens or contexts (e.g., documents, sentences), similarity statistics for words and documents, exporting to DTM for compatibility with many text analysis packages, and the possibility to reconstruct original text from tokens to facilitate interpretation.

Interacting with binary files can be difficult because R's types are a subset of what is generally supported by C'. This package provides a suite of functions for reading and writing binary data (with files, connections, and raw vectors) using C type descriptions. These functions convert data between C types and R types while checking for values outside the type limits, NA values, etc.

This package performs adjustments of a user-supplied independence loglikelihood function using a robust sandwich estimator of the parameter covariance matrix, based on the methodology in Chandler and Bate (2007) <doi:10.1093/biomet/asm015>. This can be used for cluster correlated data when interest lies in the parameters of the marginal distributions or for performing inferences that are robust to certain types of model misspecification. Functions for profiling the adjusted loglikelihoods are also provided, as are functions for calculating and plotting confidence intervals, for single model parameters, and confidence regions, for pairs of model parameters. Nested models can be compared using an adjusted likelihood ratio test.

An R interface to Cheetah Grid', a high-performance JavaScript table widget. cheetahR allows users to render millions of rows in just a few milliseconds, making it an excellent alternative to other R table widgets. The package wraps the Cheetah Grid JavaScript functions and makes them readily available for R users. The underlying grid implementation is based on Cheetah Grid <https://github.com/future-architect/cheetah-grid>.

Use frequentist and Bayesian methods to estimate parameters from a binary outcome misclassification model. These methods correct for the problem of "label switching" by assuming that the sum of outcome sensitivity and specificity is at least 1. A description of the analysis methods is available in Hochstedler and Wells (2023) <doi:10.48550/arXiv.2303.10215>.

This package performs simple correspondence analysis on a two-way contingency table, or multiple correspondence analysis (homogeneity analysis) on data with p categorical variables, and produces bootstrap-based elliptical confidence regions around the projected coordinates for the category points. Includes routines to plot the results in a variety of styles. Also reports the standard numerical output for correspondence analysis.

Manages comparison of MCMC performance metrics from multiple MCMC algorithms. These may come from different MCMC configurations using the nimble package or from other packages. Plug-ins for JAGS via rjags and Stan via rstan are provided. It is possible to write plug-ins for other packages. Performance metrics are held in an MCMCresult class along with samples and timing data. It is easy to apply new performance metrics. Reports are generated as html pages with figures comparing sets of runs. It is possible to configure the html pages, including providing new figure components.

Generate multivariate color palettes to represent two-dimensional or three-dimensional data in graphics (in contrast to standard color palettes that represent just one variable). You tell colors3d how to map color space onto your data, and it gives you a color for each data point. You can then use these colors to make plots in base R', ggplot2', or other graphics frameworks.

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

Model-free selection of covariates under unconfoundedness for situations where the parameter of interest is an average causal effect. This package is based on model-free backward elimination algorithms proposed in de Luna, Waernbaum and Richardson (2011). Marginal co-ordinate hypothesis testing is used in situations where all covariates are continuous while kernel-based smoothing appropriate for mixed data is used otherwise.

Compare C-statistics (concordance statistics) between two survival models, using either bootstrap resampling (Harrell's C) or Uno's C with perturbation-resampling (from the survC1 package). Returns confidence intervals and a p-value for the difference in C-statistics. Useful for evaluating and comparing predictive performance of survival models. Methods implemented for Uno's C are described in Uno et al. (2011) <doi:10.1002/sim.4154>.

Non-parametric tests (Wilcoxon rank sum test and Wilcoxon signed rank test) for clustered data documented in Jiang et. al (2020) <doi:10.18637/jss.v096.i06>.

This is an open-source implementation of the Congruent Matching Profile Segments (CMPS) method (Chen et al. 2019)<doi:10.1016/j.forsciint.2019.109964>. In general, it can be used for objective comparison of striated tool marks, and in our examples, we specifically use it for bullet signatures comparisons. The CMPS score is expected to be large if two signatures are similar. So it can also be considered as a feature that measures the similarity of two bullet signatures.

CODATA internationally recommended values of the fundamental physical constants, provided as symbols for direct use within the R language. Optionally, the values with uncertainties and/or units are also provided if the errors', units and/or quantities packages are installed. The Committee on Data for Science and Technology (CODATA) is an interdisciplinary committee of the International Council for Science which periodically provides the internationally accepted set of values of the fundamental physical constants. This package contains the "2022 CODATA" version, published on May 2024: Eite Tiesinga, Peter J. Mohr, David B. Newell, and Barry N. Taylor (2024) <https://physics.nist.gov/cuu/Constants/>.

Using polygenic scores (PGS, or PRS/GRS for binary outcomes), this package allows to investigate shared predisposition between different conditions, and do fast association analysis, export plots and views of the PGS distribution using ggplot2 object.

This package implements Monte Carlo conditional inference for the parameters of a linear nonnormal regression model.

This package provides functions to test and compare causal models using Confirmatory Path Analysis.

Use the high-precision arithmetic provided by the R package Rmpfr to compute a custom-made Gauss quadrature nodes and weights, with up to 33 nodes, using a moment-based method via moment determinants. Paul Kabaila (2022) <arXiv:2211.04729>.

Integrates two numerical omics data sets from the same samples using partial correlations. The output can be represented as a network, bipartite graph or a hypergraph structure. The method used in the package refers to Klaus et al (2021) <doi:10.1016/j.molmet.2021.101295>.

This package provides a basic implementation of the change in mean detection method outlined in: Taylor, Wayne A. (2000) <https://variation.com/wp-content/uploads/change-point-analyzer/change-point-analysis-a-powerful-new-tool-for-detecting-changes.pdf>. The package recursively uses the mean-squared error change point calculation to identify candidate change points. The candidate change points are then re-estimated and Taylor's backwards elimination process is then employed to come up with a final set of change points. Many of the underlying functions are written in C++ for improved performance.

Supporting the use of the Canadian Community Health Survey (CCHS) by transforming variables from each cycle into harmonized, consistent versions that span survey cycles (currently, 2001 to 2018). CCHS data used in this library is accessed and adapted in accordance to the Statistics Canada Open Licence Agreement. This package uses rec_with_table(), which was developed from sjmisc rec(). Lüdecke D (2018). "sjmisc: Data and Variable Transformation Functions". Journal of Open Source Software, 3(26), 754. <doi:10.21105/joss.00754>.

Resampling is a standard step in particle filtering and in sequential Monte Carlo. This package implements the chopthin resampler, which keeps a bound on the ratio between the largest and the smallest weights after resampling.

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>.