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>.

Plots with high flexibility and easy handling, including informative regression diagnostics for many models.

The spatial interpolation of genetic distances between samples is based on a modified kriging method that accepts a genetic distance matrix and generates a map of probability of lineage presence. This package also offers tools to generate a map of potential contact zones between groups with user-defined thresholds in the tree to account for old and recent divergence. Additionally, it has functions for IDW interpolation using genetic data and midpoints.

Density, distribution function, quantile function, and random generation function based on Kittipong Klinjan,Tipat Sottiwan and Sirinapa Aryuyuen (2024)<DOI:10.28919/cmbn/8833>.

This package provides a central decision in a parametric regression is how to specify the relation between an dependent variable and each explanatory variable. This package provides a semi-parametric tool for comparing different transformations of an explanatory variables in a parametric regression. The functions is relevant in a situation, where you would use a box-cox or Box-Tidwell transformations. In contrast to the classic power-transformations, the methods in this package allows for theoretical driven user input and the possibility to compare with a non-parametric transformation.

Translating messages in R packages is managed using the po top-level directory and the gettext program. This package provides some helper functions for building this support in R packages, e.g. common validation & I/O tasks.

Download press freedom index data from Reporters Without Borders (RSF) with period-aware encoding handling. Data are downloaded from the RSF website (<https://rsf.org/en/index>). Provides infrastructure for data cleaning and ISO 3166 standardization in downstream phases.

Fast functions for dealing with prime numbers, such as testing whether a number is prime and generating a sequence prime numbers. Additional functions include finding prime factors and Ruth-Aaron pairs, finding next and previous prime numbers in the series, finding or estimating the nth prime, estimating the number of primes less than or equal to an arbitrary number, computing primorials, prime k-tuples (e.g., twin primes), finding the greatest common divisor and smallest (least) common multiple, testing whether two numbers are coprime, and computing Euler's totient function. Most functions are vectorized for speed and convenience.

For a data matrix with m rows and n columns (m>=n), the power method is used to compute, simultaneously, the eigendecomposition of a square symmetric matrix. This result is used to obtain the singular value decomposition (SVD) and the principal component analysis (PCA) results. Compared to the classical SVD method, the first r singular values can be computed.

This package provides a new metric named dependency heaviness is proposed that measures the number of additional dependency packages that a parent package brings to its child package and are unique to the dependency packages imported by all other parents. The dependency heaviness analysis is visualized by a customized heatmap. The package is described in <doi:10.1093/bioinformatics/btac449>. We have also performed the dependency heaviness analysis on the CRAN/Bioconductor package ecosystem, described in <doi:10.1016/j.jss.2023.111610>.

Single arm phase 2 oncology trial. For more details see P. Gao (2024) <doi:10.1080/10543406.2024.2341673>.

An alternative data structure and visual rendering for the profiling information generated by Rprof.

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.

Includes functions to wrap most endpoints of the PaleobioDB API and to visualize and process the obtained fossil data. The API documentation for the Paleobiology Database can be found at <https://paleobiodb.org/data1.2/>.

Collection of functions for working with multi-well microtitre plates, mainly 96, 384 and 1536 well plates.

An open-source toolbox for storing, validating, managing, and exploring transportation infrastructure data. Provides a relational data model for binders, aggregates, mixtures, and test results, with a human-readable file format (.pavdata) aligned with FAIR principles.

The introduction of the broom package has made converting model objects into data frames as simple as a single function. While the broom package focuses on providing tidy data frames that can be used in advanced analysis, it deliberately stops short of providing functionality for reporting models in publication-ready tables. pixiedust provides this functionality with a programming interface intended to be similar to ggplot2's system of layers with fine tuned control over each cell of the table. Options for output include printing to the console and to the common markdown formats (markdown, HTML, and LaTeX). With a little pixiedust (and happy thoughts) tables can really fly.

Run Queries against the API of Piwik Pro <https://developers.piwik.pro/en/latest/custom_reports/http_api/http_api.html>. The result is a tibble.

Sparse regression and classification via the Pivotal Information Criterion (PIC), an alternative to the Bayesian Information Criterion (BIC), cross-validation, and Lasso-based tuning. The regularization parameter is selected from a pivotal null-distribution statistic, eliminating the need for cross-validation and yielding sharper support recovery. Provides Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) optimization for the L1, Smoothly Clipped Absolute Deviation (SCAD), and Minimax Concave Penalty (MCP) penalties across six response distributions: Gaussian, binomial, Poisson, exponential, Gumbel, and Cox. Under standard sparsity assumptions, the selector achieves a phase transition for exact support recovery, analogous to results in compressed sensing. See Sardy, van Cutsem and van de Geer (2026) <doi:10.48550/arXiv.2603.04172>.

Finds equivalence classes corresponding to a symmetric relation or undirected graph. Finds total order consistent with partial order or directed graph (so-called topological sort).

This package provides functions for bootstrapping the power of ANOVA designs based on estimated means and standard deviations of the conditions. Please refer to the documentation of the boot.power.anova() function for further details.

Drop-in replacements for standard base graphics functions. The replacements are prettier versions of the originals.

Poisson disk sampling is a method of generating blue noise sample patterns where all samples are at least a specified distance apart. Poisson samples may be generated in two or three dimensions with this package. The algorithm used is an implementation of Bridson's "Fast Poisson disk sampling in arbitrary dimensions" <doi:10.1145%2F1278780.1278807>.

Computes predicted values and lower/upper limits of normal for pulmonary function tests according to American Thoracic Society ('ATS') and European Respiratory Society ('ERS') reference standards. Supports spirometry (Global Lung Function Initiative GLI 2012, Quanjer et al. (2012) <doi:10.1183/09031936.00080312>; and the race-neutral GLI 2022 / GLI Global equations, Bowerman et al. (2023) <doi:10.1164/rccm.202205-0963OC>), static lung volumes ('GLI 2021, Hall et al. (2021) <doi:10.1183/13993003.00289-2020>), and the carbon monoxide transfer factor / diffusion capacity ('GLI 2017, Stanojevic et al. (2017) <doi:10.1183/13993003.00010-2017>, including the 2020 author correction <doi:10.1183/13993003.50010-2017>). Also assigns interpretive pattern labels (Normal, Non-specific, Obstructed, Restricted, Mixed) from spirometry and lung-volume measurements following the ERS'/'ATS 2022 interpretation algorithm, Stanojevic et al. (2022) <doi:10.1183/13993003.01499-2021>.