Treats the proximity matrices produced by tree ensembles as first-class statistical objects rather than as model by-products. Provides a unified extractor across ensemble engines, in-bag and out-of-bag definitions, transformations to dissimilarities with metric diagnostics, corrections that make an indefinite proximity usable as a kernel, and permutation inference for comparing two proximity matrices or partitioning one across the terms of a design. Also provides a Nystrom approximation, a thresholded sparse representation and a streaming form that never allocates the matrix at all, for samples too large to hold it, measures how far the proximity moves between replicates of the ensemble, and draws each object through ggplot2': the seriated matrix, the configuration it implies, and the thresholded graph with its communities.

This package provides functions to calculate power and sample size for testing main effect or interaction effect in the survival analysis of epidemiological studies (non-randomized studies), taking into account the correlation between the covariate of the interest and other covariates. Some calculations also take into account the competing risks and stratified analysis. This package also includes a set of functions to calculate power and sample size for testing main effect in the survival analysis of randomized clinical trials and conditional logistic regression for nested case-control study.

This R package provides power calculations via internal simulation methods. The package also provides a frontend to the now abandoned PBAT program (developed by Christoph Lange), and reads in the corresponding output and displays results and figures when appropriate. The license of this R package itself is GPL. However, to have the program interact with the PBAT program for some functionality of the R package, users must additionally obtain the PBAT program from Christoph Lange, and accept his license. Both the data analysis and power calculations have command line and graphical interfaces using tcltk.

Deploy, maintain, and invoke predictive models using the Alteryx Promote REST API. Alteryx Promote is available at the URL: <https://www.alteryx.com/products/alteryx-promote>.

This package provides a power analysis tool for jointly testing the cause-1 cause-specific hazard and the any-cause hazard with competing risks data.

This package provides a package for selecting the most relevant features (genes) in the high-dimensional binary classification problems. The discriminative features are identified using analyzing the overlap between the expression values across both classes. The package includes functions for measuring the proportional overlapping score for each gene avoiding the outliers effect. The used measure for the overlap is the one defined in the "Proportional Overlapping Score (POS)" technique for feature selection. A gene mask which represents a gene's classification power can also be produced for each gene (feature). The set size of the selected genes might be set by the user. The minimum set of genes that correctly classify the maximum number of the given tissue samples (observations) can be also produced.

The aim of postpack is to provide the infrastructure for a standardized workflow for mcmc.list objects. These objects can be used to store output from models fitted with Bayesian inference using JAGS', WinBUGS', OpenBUGS', NIMBLE', Stan', or even custom MCMC algorithms. Although the coda R package provides some methods for these objects, it is somewhat limited in easily performing post-processing tasks for specific nodes. Models are ever increasing in their complexity and the number of tracked nodes, and oftentimes a user may wish to summarize/diagnose sampling behavior for only a small subset of nodes at a time for a particular question or figure. Thus, many postpack functions support performing tasks on a subset of nodes, where the subset is specified with regular expressions. The functions in postpack streamline the extraction, summarization, and diagnostics of specific monitored nodes after model fitting. Further, because there is rarely only ever one model under consideration, postpack scales efficiently to perform the same tasks on output from multiple models simultaneously, facilitating rapid assessment of model sensitivity to changes in assumptions.

In this record linkage package, data preprocessing has been meticulously executed to cover a wide range of datasets, ensuring that variable names are standardized using synonyms. This approach facilitates seamless data integration and analysis across various datasets. While users have the flexibility to modify variable names, the system intelligently ensures that changes are only permitted when they do not compromise data consistency or essential variable essence.

Retrieves a pluscode by inputting latitude and longitude. Includes additional functions to retrieve neighbouring pluscodes'.

Data from All the World's Primates relational SQL database and other tabular datasets are made available via drivers and connection functions. Additionally we provide several functions and examples to facilitate the merging and aggregation of these tabular inputs.

Sensitivity and power analysis, for calculating statistics describing pedigrees from wild populations, and for visualizing pedigrees. This is a reboot of the methods developed by Morrissey and Wilson (2010) <doi: 10.1111/j.1755-0998.2009.02817.x>.

Use Pokemon(R) inspired palettes with additional ggplot2 scales. Palettes are the colours in each Pokemon's sprite, ordered by how common they are in the image. The first 386 Pokemon are currently provided.

This package provides tools for the processing, segmentation, and analysis of terrestrial laser scanning (TLS and MLS) forest point-cloud data. The package provides fast voxel-based processing, classification of point clouds into forest floor, understory, canopy, and woody components, and algorithms for single-tree analysis and structural characterization. Methods are designed to handle large and dense point-cloud datasets efficiently, supporting applications in forest structure assessment, connectivity analysis, and fire-risk evaluation. Input data are provided as .xyz', .txt', .las', or .laz point-cloud files. The circle-fitting routines used for diameter estimation are adapted, in base R, from the conicfit package (GPL-3) by Jose Gama, based on the original algorithms and code by Nikolai Chernov. For methodological details, see Ferrara and Arrizza (2025) <https://hdl.handle.net/20.500.14243/533471> and Ferrara et al. (2018) <doi:10.1016/j.agrformet.2018.04.008>.

This package provides Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models in high dimensional settings <doi:10.1093/bioinformatics/btu660>, Bastien, P., Bertrand, F., Meyer N., Maumy-Bertrand, M. (2015), Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data, Bioinformatics, 31(3):397-404. Cross validation criteria were studied in <doi:10.48550/arXiv.1810.02962>, Bertrand, F., Bastien, Ph. and Maumy-Bertrand, M. (2018), Cross validating extensions of kernel, sparse or regular partial least squares regression models to censored data.

Several functions introduced in Aster et al.'s book on inverse theory. The functions are often translations of MATLAB code developed by the authors to illustrate concepts of inverse theory as applied to geophysics. Generalized inversion, tomographic inversion algorithms (conjugate gradients, ART and SIRT'), non-linear least squares, first and second order Tikhonov regularization, roughness constraints, and procedures for estimating smoothing parameters are included.

This package contains functions to simulate the most commonly used SAS® procedures. Specifically, the package aims to simulate the functionality of proc freq', proc means', proc ttest', proc reg', proc transpose', proc sort', and proc print'. The simulation will include recreating all statistics with the highest fidelity possible.

Computes the All-Resolution Inference method in the permutation framework, i.e., simultaneous lower confidence bounds for the number of true discoveries. <doi:10.1002/sim.9725>.

Sample size calculations for practical equivalence trial design with a time to event endpoint.

Includes functions and data used in the book "Presenting Statistical Results Effectively", Andersen and Armstrong (2022, ISBN: 978-1446269800). Several functions aid in data visualization - creating compact letter displays for simple slopes, kernel density estimates with normal density overlay. Other functions aid in post-model evaluation heatmap fit statistics for binary predictors, several variable importance measures, compact letter displays and simple-slope calculation. Finally, the package makes available the example datasets used in the book.

It provides utility functions for investigating changes within R packages. The pkgInfo() function extracts package information such as exported and non-exported functions as well as their arguments. The pkgDiff() function compares this information for two versions of a package and creates a diff file viewable in a browser.

This package implements (1) panel cointegration rank tests, (2) estimators for panel vector autoregressive (VAR) models, and (3) identification methods for panel structural vector autoregressive (SVAR) models as described in the accompanying vignette. The implemented functions allow to account for cross-sectional dependence and for structural breaks in the deterministic terms of the VAR processes. Among the large set of functions, particularly noteworthy are those that implement (1) the correlation-augmented inverse normal test on the cointegration rank by Arsova and Oersal (2021, <doi:10.1016/j.ecosta.2020.05.002>), (2) the two-step estimator for pooled cointegrating vectors by Breitung (2005, <doi:10.1081/ETC-200067895>), and (3) the pooled identification based on independent component analysis by Herwartz and Wang (2024, <doi:10.1002/jae.3044>).

Fast, flexible framework for implementing Bayesian optimization of model hyperparameters according to the methods described in Snoek et al. (2012) <doi:10.48550/arXiv.1206.2944>. The package allows the user to run scoring function in parallel, save intermediary results, and tweak other aspects of the process to fully utilize the computing resources available to the user.

Uses provenance collected by rdtLite package or comparable tool to display information about input files, output files, and exchanged files for a single R script or a series of R scripts.

This package provides a unified method, called M statistic, is provided for detecting phylogenetic signals in continuous traits, discrete traits, and multi-trait combinations. Blomberg and Garland (2002) <doi:10.1046/j.1420-9101.2002.00472.x> provided a widely accepted statistical definition of the phylogenetic signal, which is the "tendency for related species to resemble each other more than they resemble species drawn at random from the tree". The M statistic strictly adheres to the definition of phylogenetic signal, formulating an index and developing a method of testing in strict accordance with the definition, instead of relying on correlation analysis or evolutionary models. The novel method equivalently expressed the textual definition of the phylogenetic signal as an inequality equation of the phylogenetic and trait distances and constructed the M statistic. The M statistic implemented in this package is based on the methodology described in Yao and Yuan (2025) <doi:10.1002/ece3.71106>. If you use this method in your research, please cite the paper.