Tests for a comparison of two partially overlapping samples. A comparison of means using the partially overlapping samples t-test: See Derrick, Russ, Toher and White (2017), Test statistics for the comparison of means for two samples which include both paired observations and independent observations, Journal of Modern Applied Statistical Methods, 16(1). A comparison of proportions using the partially overlapping samples z-test: See Derrick, Dobson-Mckittrick, Toher and White (2015), Test statistics for comparing two proportions with partially overlapping samples. Journal of Applied Quantitative Methods, 10(3).

Bayesian regularized quantile regression utilizing two major classes of shrinkage priors (the spike-and-slab priors and the horseshoe family of priors) leads to efficient Bayesian shrinkage estimation, variable selection and valid statistical inference. In this package, we have implemented robust Bayesian variable selection with spike-and-slab priors under high-dimensional linear regression models (Fan et al. (2024) <doi:10.3390/e26090794> and Ren et al. (2023) <doi:10.1111/biom.13670>), and regularized quantile varying coefficient models (Zhou et al.(2023) <doi:10.1016/j.csda.2023.107808>). In particular, valid robust Bayesian inferences under both models in the presence of heavy-tailed errors can be validated on finite samples. Additional models with spike-and-slab priors include robust Bayesian group LASSO and robust binary Bayesian LASSO (Fan and Wu (2025) <doi:10.1002/sta4.70078>). Besides, robust sparse Bayesian regression with the horseshoe family of (horseshoe, horseshoe+ and regularized horseshoe) priors has also been implemented and yielded valid inference results under heavy-tailed model errors(Fan et al.(2025) <doi:10.48550/arXiv.2507.10975>). The Markov chain Monte Carlo (MCMC) algorithms of the proposed and alternative models are implemented in C++.

This package provides methods for plotting potentially large (raster) images interactively on a plain HTML canvas. In contrast to package mapview data are plotted without background map, but data can be projected to any spatial coordinate reference system. Supports plotting of classes RasterLayer', RasterStack', RasterBrick (from package raster') as well as png files located on disk. Interactivity includes zooming, panning, and mouse location information. In case of multi-layer RasterStacks or RasterBricks', RGB image plots are created (similar to raster::plotRGB - but interactive).

Read R package news files, regardless of whether or not the package is installed.

This package provides a tool for inferring kinase activity changes from phosphoproteomics data. pKSEA uses kinase-substrate prediction scores to weight observed changes in phosphopeptide abundance to calculate a phosphopeptide-level contribution score, then sums up these contribution scores by kinase to obtain a phosphoproteome-level kinase activity change score (KAC score). pKSEA then assesses the significance of changes in predicted substrate abundances for each kinase using permutation testing. This results in a permutation score (pKSEA significance score) reflecting the likelihood of a similarly high or low KAC from random chance, which can then be interpreted in an analogous manner to an empirically calculated p-value. pKSEA contains default databases of kinase-substrate predictions from NetworKIN (NetworKINPred_db) <http://networkin.info> Horn, et. al (2014) <doi:10.1038/nmeth.2968> and of known kinase-substrate links from PhosphoSitePlus (KSEAdb) <https://www.phosphosite.org/> Hornbeck PV, et. al (2015) <doi:10.1093/nar/gku1267>.

This package provides a toolbox for deterministic, probabilistic and privacy-preserving record linkage techniques. Combines the functionality of the Merge ToolBox (<https://www.record-linkage.de>) with current privacy-preserving techniques.

In each odd dimension is a convex body - the polar zonoid - whose generating functions are trigonometric polynomials. The polar zonoid is a straightforward generalization of the polar zonohedron in dimension 3, as defined by Chilton and Coxeter (1963) <doi:10.2307/2313051>. The package has some applications of the polar zonoid, including the properties of configuration spaces of arcs on the circle and 3x3 rotation matrices. There is also a root solver for trigonometric polynomials.

This package contains sixteen moisture sorption isotherm models, which evaluate the fitness of adsorption and desorption curves for further understanding of the relationship between moisture content and water activity. Fitness evaluation is conducted through parameter estimation and error analysis. Moreover, graphical representation, hysteresis area estimation, and isotherm classification through the equation of Blahovec & Yanniotis (2009) <doi:10.1016/j.jfoodeng.2008.08.007> which is based on the classification system introduced by Brunauer et. al. (1940) <doi:10.1021/ja01864a025> are also included for the visualization of models and hysteresis.

This package provides functions are available to calibrate designs over a range of posterior and predictive thresholds, to plot the various design options, and to obtain the operating characteristics of optimal accuracy and optimal efficiency designs.

Offers a range of utilities and functions for everyday programming tasks. 1.Data Manipulation. Such as grouping and merging, column splitting, and character expansion. 2.File Handling. Read and convert files in popular formats. 3.Plotting Assistance. Helpful utilities for generating color palettes, validating color formats, and adding transparency. 4.Statistical Analysis. Includes functions for pairwise comparisons and multiple testing corrections, enabling perform statistical analyses with ease. 5.Graph Plotting, Provides efficient tools for creating doughnut plot and multi-layered doughnut plot; Venn diagrams, including traditional Venn diagrams, upset plots, and flower plots; Simplified functions for creating stacked bar plots, or a box plot with alphabets group for multiple comparison group.

Like similar profiling tools, the proffer package automatically detects sources of slowness in R code. The distinguishing feature of proffer is its utilization of pprof', which supplies interactive visualizations that are efficient and easy to interpret. Behind the scenes, the profile package converts native Rprof() data to a protocol buffer that pprof understands. For the documentation of proffer', visit <https://r-prof.github.io/proffer/>. To learn about the implementations and methodologies of pprof', profile', and protocol buffers, visit <https://github.com/google/pprof>. <https://protobuf.dev>, and <https://github.com/r-prof/profile>, respectively.

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.

Generic interface for the PX-Web/PC-Axis API. The PX-Web/PC-Axis API is used by organizations such as Statistics Sweden and Statistics Finland to disseminate data. The R package can interact with all PX-Web/PC-Axis APIs to fetch information about the data hierarchy, extract metadata and extract and parse statistics to R data.frame format. PX-Web is a solution to disseminate PC-Axis data files in dynamic tables on the web. Since 2013 PX-Web contains an API to disseminate PC-Axis files.

Reviews other packages during code review by looking at their dependencies, code style, code complexity, and how internally defined functions interact with one another.

Allows to perform the tests of equal predictive accuracy for panels of forecasts. Main references: Qu et al. (2024) <doi:10.1016/j.ijforecast.2023.08.001> and Akgun et al. (2024) <doi:10.1016/j.ijforecast.2023.02.001>.

Farmer, J., D. Jacobs (2108) <DOI:10.1371/journal.pone.0196937>. A multivariate nonparametric density estimator based on the maximum-entropy method. Accurately predicts a probability density function (PDF) for random data using a novel iterative scoring function to determine the best fit without overfitting to the sample.

Determine the chlorophyll a (Chl a) concentrations of different phytoplankton groups based on their pigment biomarkers. The method uses non-negative matrix factorisation and simulated annealing to minimise error between the observed and estimated values of pigment concentrations (Hayward et al. (2023) <doi:10.1002/lom3.10541>). The approach is similar to the widely used CHEMTAX program (Mackey et al. 1996) <doi:10.3354/meps144265>, but is more straightforward, accurate, and not reliant on initial guesses for the pigment to Chl a ratios for phytoplankton groups.

This package provides data set and function for exploration of Multiple Indicator Cluster Survey 2014 Women (age 15-49 years) questionnaire data for Punjab, Pakistan.

Partitioning clustering divides the objects in a data set into non-overlapping subsets or clusters by using the prototype-based probabilistic and possibilistic clustering algorithms. This package covers a set of the functions for Fuzzy C-Means (Bezdek, 1974) <doi:10.1080/01969727308546047>, Possibilistic C-Means (Krishnapuram & Keller, 1993) <doi:10.1109/91.227387>, Possibilistic Fuzzy C-Means (Pal et al, 2005) <doi:10.1109/TFUZZ.2004.840099>, Possibilistic Clustering Algorithm (Yang et al, 2006) <doi:10.1016/j.patcog.2005.07.005>, Possibilistic C-Means with Repulsion (Wachs et al, 2006) <doi:10.1007/3-540-31662-0_6> and the other variants of hard and soft clustering algorithms. The cluster prototypes and membership matrices required by these partitioning algorithms are initialized with different initialization techniques that are available in the package inaparc'. As the distance metrics, not only the Euclidean distance but also a set of the commonly used distance metrics are available to use with some of the algorithms in the package.

Wrangle and annotate different types of political texts. It also introduces Urgency Analysis, a new method for the analysis of urgency in political texts.

Routines for flexible functional form estimation via basis regression, with model selection via the adaptive LASSO or SCAD to prevent overfitting.

Predicts the most common race of a surname and based on U.S. Census data, and the most common first named based on U.S. Social Security Administration data.

Perform flexible and quick calculations for Demand and Supply Planning, such as projected inventories and coverages, as well as replenishment plan. For any time bucket, daily, weekly or monthly, and any granularity level, product or group of products.

Perform scale linking to establish relationships between instruments that measure similar constructs according to the PROsetta Stone methodology, as in Choi, Schalet, Cook, & Cella (2014) <doi:10.1037/a0035768>.