Computes optimal changepoint models using the Poisson likelihood for non-negative count data, subject to the PeakSeg constraint: the first change must be up, second change down, third change up, etc. For more info about the models and algorithms, read "Constrained Dynamic Programming and Supervised Penalty Learning Algorithms for Peak Detection" <https://jmlr.org/papers/v21/18-843.html> by TD Hocking et al.

This package provides a RStudio addin allowing to paste the content of the clipboard as a comment block or as roxygen lines. This is very useful to insert an example in the roxygen block.

Implementation of assumption-lean and data-adaptive post-prediction inference (POPInf), for valid and efficient statistical inference based on data predicted by machine learning. See Miao, Miao, Wu, Zhao, and Lu (2023) <arXiv:2311.14220>.

Supplementary utils for CRAN maintainers and R packages developers. Validating the library, packages and lock files. Exploring a complexity of a specific package like evaluating its size in bytes with all dependencies. The shiny app complexity could be explored too. Assessing the life duration of a specific package version. Checking a CRAN package check page status for any errors and warnings. Retrieving a DESCRIPTION or NAMESPACE file for any package version. Comparing DESCRIPTION or NAMESPACE files between different package versions. Getting a list of all releases for a specific package. The Bioconductor is partly supported.

This package provides a tidy interface to Pakistan's official administrative boundary data from the United Nations Office for the Coordination of Humanitarian Affairs (OCHA). Spatial data at country, province, district, and tehsil levels are embedded in the package as sf objects compatible with the tidyverse and geospatial ecosystem. Includes utilities for geographic dictionary lookup, coordinate reference system selection, spatial measurement, and neighbour structure construction for use with spdep', ggplot2', leaflet', and related packages.

We extend two general methods of moment estimators to panel vector autoregression models (PVAR) with p lags of endogenous variables, predetermined and strictly exogenous variables. This general PVAR model contains the first difference GMM estimator by Holtz-Eakin et al. (1988) <doi:10.2307/1913103>, Arellano and Bond (1991) <doi:10.2307/2297968> and the system GMM estimator by Blundell and Bond (1998) <doi:10.1016/S0304-4076(98)00009-8>. We also provide specification tests (Hansen overidentification test, lag selection criterion and stability test of the PVAR polynomial) and classical structural analysis for PVAR models such as orthogonal and generalized impulse response functions, bootstrapped confidence intervals for impulse response analysis and forecast error variance decompositions.

This package provides a nonparametric, multicore-capable plausible naive Bayes classifier based on Pareto density estimation (PDE). It addresses low-evidence cases through a plausibility correction. To enhance the interpretability of the flexible naive Bayes classifier by revealing its posterior structure and feature-wise, class-specific evidence, posterior probabilities can be visualized as class-wise line plots for one-dimensional data or color-coded Voronoi diagrams for pairwise feature projections, and class-conditional PDE likelihoods as overlaid, mirrored density profiles resembling violin plots. Methodological details are provided by Stier, Q., Hoffmann, J. and Thrun, M. C. (2026) "Classifying with the Fine Structure of Distributions: Leveraging Distributional Information for Robust and Plausible Naive Bayes" <DOI:10.3390/make8010013>. For multicore computations, the implementation applies the general memory-sharing approach described by Thrun, M. C. and Märte, J. (2026) "memshare: Memory Sharing for Multicore Computation in R with an Application to Feature Selection by Mutual Information using PDE" <DOI:10.32614/RJ-2025-043>.

This package provides an interface to the GenderAPI.io Phone Number Validation & Formatter API (<https://www.genderapi.io>) for validating international phone numbers, detecting number type (mobile, landline, Voice over Internet Protocol (VoIP)), retrieving region and country metadata, and formatting numbers to E.164 or national format. Designed to simplify integration into R workflows for data validation, Customer Relationship Management (CRM) data cleaning, and analytics tasks. Full documentation is available at <https://www.genderapi.io/docs-phone-validation-formatter-api>.

Partial Least Squares Path Modeling (PLS-PM), Tenenhaus, Esposito Vinzi, Chatelin, Lauro (2005) <doi:10.1016/j.csda.2004.03.005>, analysis for both metric and non-metric data, as well as REBUS analysis, Esposito Vinzi, Trinchera, Squillacciotti, and Tenenhaus (2008) <doi:10.1002/asmb.728>.

Search and browse the Political Science Replication Index (<https://jsakowuah.github.io/polisci-replication/>), a searchable, tagged index of replication packages crawled monthly from flagship political science journals Harvard Dataverse collections, without leaving R. Provides functions to download and cache the index locally and to search and filter it by journal, method, data type, and year.

Create, transform, and summarize custom random variables with distribution functions (analogues of p*()', d*()', q*()', and r*() functions from base R). Two types of distributions are supported: "discrete" (random variable has finite number of output values) and "continuous" (infinite number of values in the form of continuous random variable). Functions for distribution transformations and summaries are available. Implemented approaches often emphasize approximate and numerical solutions: all distributions assume finite support and finite values of density function; some methods implemented with simulation techniques.

Disk-based implementation of Functional Pruning Optimal Partitioning with up-down constraints <doi:10.18637/jss.v101.i10> for single-sample peak calling (independently for each sample and genomic problem), can handle huge data sets (10^7 or more).

This package provides tools for poultry production and economic analysis. The package calculates feed conversion ratio (FCR), hen-day egg production (HDEP), hen-housed egg production (HHEP), cost per egg, profit per bird, mortality percentage, and an overall efficiency score based on predefined performance benchmarks. The results can optionally be exported to an Excel workbook and visualized using a bar chart.

This package provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of SDG monitoring, as the survey produces information on 32 global SDG indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using Probability Proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household.

This package provides additional functions for evaluating predictive models, including plotting calibration curves and model-based Receiver Operating Characteristic (mROC) based on Sadatsafavi et al (2021) <arXiv:2003.00316>.

Identifies the entries with patterned responses for psychometric scales. The patterns included in the package are identical (a, a, a), ascending (a, b, c), descending (c, b, a), alternative (a, b, a, b / a, b, c, a, b, c).

Allow to run pylint on Python files with a R command or a RStudio addin. The report appears in the RStudio viewer pane as a formatted HTML file.

This version of the permutational algorithm generates a dataset in which event and censoring times are conditional on an user-specified list of covariates, some or all of which are time-dependent.

This package implements tools for the analysis of partially ordered data, with a particular focus on the evaluation of multidimensional systems of indicators and on the analysis of poverty. References, Fattore M. (2016) <doi:10.1007/s11205-015-1059-6> Fattore M., Arcagni A. (2016) <doi:10.1007/s11205-016-1501-4> Arcagni A. (2017) <doi:10.1007/978-3-319-45421-4_19>.

Data and statistics of Pakistan Social and Living Standards Measurement (PSLM) survey 2014-15 from Pakistan Bureau of Statistics (<http://www.pbs.gov.pk/>).

Implementation of PsychroLib <https://github.com/psychrometrics/psychrolib> library which contains functions to enable the calculation properties of moist and dry air in both metric (SI) and imperial (IP) systems of units. References: Meyer, D. and Thevenard, D (2019) <doi:10.21105/joss.01137>.

Lightweight, GPU-accelerated bioinformatics visualization widgets (volcano plots, expression and clustered heatmaps, dot plots, stacked violins, embeddings, spatial tissue maps, oncoprints, protein domain lollipops, Kaplan-Meier curves, mutational signature profiles, UpSet plots, treemaps, networks and Hi-C contact matrices) backed by a shared JavaScript core and exposed to R through htmlwidgets'. Designed for large datasets that render smoothly in the browser, the RStudio Viewer, R Markdown, Quarto and Shiny.

Calculates multivariate analysis of variance based on permutations and some associated pictorial representations. The pictorial representation is based on the principal coordinates of the group means. There are some original results that will be published soon.

The Prize-Collecting Steiner Tree problem asks to find a subgraph connecting a given set of vertices with the most expensive nodes and least expensive edges. Since it is proven to be NP-hard, exact and efficient algorithm does not exist. This package provides convenient functionality for obtaining an approximate solution to this problem using loopy belief propagation algorithm.