Permutation (randomisation) test for single-case phase design data with two phases (e.g., pre- and post-treatment). Correction for dependency of observations is done through stepwise resampling the time series while varying the distance between observations. The required distance 0,1,2,3.. is determined based on repeated dependency testing while stepwise increasing the distance. In preparation: Vroegindeweij et al. "A Permutation distancing test for single-case observational AB phase design data: A Monte Carlo simulation study".

Statistical power analysis for designs including t-tests, correlations, multiple regression, ANOVA, mediation, and logistic regression. Functions accompany Aberson (2019) <doi:10.4324/9781315171500>.

When working with big data sets, RAM conservation is critically important. However, it is not always enough to just monitor the size of the objects created. So-called "copy-on-modify" behavior, characteristic of R, means that some expressions or functions may require an unexpectedly large amount of RAM overhead. For example, replacing a single value in a matrix duplicates that matrix in the back-end, making this task require twice as much RAM as that used by the matrix itself. This package makes it easy to monitor the total and peak RAM used so that developers can quickly identify and eliminate RAM hungry code.

This package contains functions to calculate power and sample size for various study designs used in bioequivalence studies. Use known.designs() to see the designs supported. Power and sample size can be obtained based on different methods, amongst them prominently the TOST procedure (two one-sided t-tests). See README and NEWS for further information.

Collection of tutorials for working with Positron and for learning how to apply generative AI when coding with R. Covers scripts, Quarto documents, Git', GitHub', and Quarto websites. Makes extensive use of the tools in the tutorial.helpers package.

Spearman's rank correlation test with precomputed exact null distribution for n <= 22.

These are useful tools and data sets for the study of quantitative peace science. The goal for this package is to include tools and data sets for doing original research that mimics well what a user would have to previously get from a software package that may not be well-sourced or well-supported. Those software bundles were useful the extent to which they encourage replications of long-standing analyses by starting the data-generating process from scratch. However, a lot of the functionality can be done relatively quickly and more transparently in the R programming language.

This package provides functions for working with primary event censored distributions and Stan implementations for use in Bayesian modeling. Primary event censored distributions are useful for modeling delayed reporting scenarios in epidemiology and other fields (Charniga et al. (2024) <doi:10.48550/arXiv.2405.08841>). It also provides support for arbitrary delay distributions, a range of common primary distributions, and allows for truncation and secondary event censoring to be accounted for (Park et al. (2024) <doi:10.1101/2024.01.12.24301247>). A subset of common distributions also have analytical solutions implemented, allowing for faster computation. In addition, it provides multiple methods for fitting primary event censored distributions to data via optional dependencies.

Efficient implementations of multiple exact and approximate methods as described in Hong (2013) <doi:10.1016/j.csda.2012.10.006>, Biscarri, Zhao & Brunner (2018) <doi:10.1016/j.csda.2018.01.007> and Zhang, Hong & Balakrishnan (2018) <doi:10.1080/00949655.2018.1440294> for computing the probability mass, cumulative distribution and quantile functions, as well as generating random numbers for both the ordinary and generalised Poisson binomial distribution.

Design, backtest, and analyze portfolio strategies using simple, English-like function chains. Includes technical indicators, flexible stock selection, portfolio construction methods (equal weighting, signal weighting, inverse volatility, hierarchical risk parity), and a compact backtesting engine for portfolio returns, drawdowns, and summary metrics.

Generic code for estimating treatment effects with panel data. The idea is to break into separate steps organizing the data, looping over groups and time periods, computing group-time average treatment effects, and aggregating group-time average treatment effects. Often, one is able to implement a new identification/estimation procedure by simply replacing the step on estimating group-time average treatment effects. See several different examples of this approach in the package documentation.

Search CRAN metadata about packages by keyword, popularity, recent activity, package name and more. Uses the R-hub search server, see <https://r-pkg.org> and the CRAN metadata database, that contains information about CRAN packages. Note that this is _not_ a CRAN project.

This package provides methods for spatial predictive modeling, especially for spatial distribution models. This includes algorithms for model fitting and prediction, as well as methods for model evaluation.

Bundles the datasets and functions used in the textbook by Philip Pollock and Barry Edwards, an R Companion to Essentials of Political Analysis, Second Edition.

An R implementation of methods employed in the field of pedometrics, soil science discipline dedicated to studying the spatial, temporal, and spatio-temporal variation of soil using statistical and computational methods. The methods found here include the calibration of linear regression models using covariate selection strategies, computation of summary validation statistics for predictions, generation of summary plots, evaluation of the local quality of a geostatistical model of uncertainty, and so on. Other functions simply extend the functionalities of or facilitate the usage of functions from other packages that are commonly used for the analysis of soil data. Formerly available versions of suggested packages no longer available from CRAN can be obtained from the CRAN archive <https://cran.r-project.org/src/contrib/Archive/>.

Patient Rule Induction Method (PRIM) for bump hunting in high-dimensional data.

This package provides a pipe-friendly R client for PX-Web statistical APIs. Provides a search-then-fetch workflow for discovering and downloading data from national statistics agencies (SCB, SSB, Statistics Finland, etc.) using a consistent tibble-based interface.

For a multivariate dataset with independent Poisson measurement error, calculates principal components of transformed latent Poisson means. T. Kenney, T. Huang, H. Gu (2019) <arXiv:1904.11745>.

Convert English letters to numbers or numbers to English letters as on a telephone keypad. When converting letters to numbers, a character vector is returned with "A," "B," or "C" becoming 2, "D," "E", or "F" becoming 3, etc. When converting numbers to letters, a character vector is returned with multiple elements (i.e., "2" becomes a vector of "A," "B," and "C").

This package provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Household questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) 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 (<http://www.mics.unicef.org/surveys>).

The perturbR() function incrementally perturbs network edges (using the rewireR function)and compares the resulting community detection solutions from the rewired networks with the solution found for the original network. These comparisons aid in understanding the stability of the original solution. The package requires symmetric, weighted (specifically, count) matrices/networks.

An easy-to-use tool for implementing Neural Ordinary Differential Equations (NODEs) in pharmacometric software such as Monolix', NONMEM', and nlmixr2', see Bräm et al. (2024) <doi:10.1007/s10928-023-09886-4> and Bräm et al. (2025) <doi:10.1002/psp4.13265>. The main functionality is to automatically generate structural model code describing computations within a neural network. Additionally, parameters and software settings can be initialized automatically. For using these additional functionalities with Monolix', pmxNODE interfaces with MonolixSuite via the lixoftConnectors package. The lixoftConnectors package is distributed with MonolixSuite (<https://monolixsuite.slp-software.com/r-functions/2024R1/package-lixoftconnectors>) and is not available from public repositories.

This package provides a suite of Propensity Score Predictive Inference (PSPI) methods to generalize treatment effects in trials to target populations. The package includes an existing model Bayesian Causal Forest (BCF) and four PSPI models (BCF-PS, FullBART, SplineBART, DSplineBART). These methods leverage Bayesian Additive Regression Trees (BART) to adjust for high-dimensional covariates and nonlinear associations, while SplineBART and DSplineBART further use propensity score based splines to address covariate shift between trial data and target population.

Computes Owen's T function or the bivariate normal integral using one of the following methods: the modified Euler's arctangent series, tetrachoric series, or Vasicek's series. For the methodology, see Komelj, J. (2023) <doi:10.4236/ajcm.2023.134026>, or the revised postprint version with corrected contents and improved typography at <doi:10.48550/arXiv.2312.00011>. Also see the alternative approach by Vasicek, O. A. (1998) <doi:10.21314/JCF.1998.015>.