Applying the global sensitivity analysis workflow to investigate the parameter uncertainty and sensitivity in physiologically based kinetic (PK) models, especially the physiologically based pharmacokinetic/toxicokinetic model with multivariate outputs. The package also provides some functions to check the convergence and sensitivity of model parameters. The workflow was first mentioned in Hsieh et al., (2018) <doi:10.3389/fphar.2018.00588>, then further refined (Hsieh et al., 2020 <doi:10.1016/j.softx.2020.100609>).

Fits Bayesian nonparametric models based on Pólya tree processes, including adaptive Pólya trees, Markov adaptive Pólya trees, optional Pólya trees, and their conditional-density counterparts. Methods are described in Ma (2017) <doi:10.1214/16-BA1021>, Ma (2017) <doi:10.1214/17-EJS1254>, and Wong and Ma (2010) <doi:10.1214/09-AOS755>.

Kernel density estimation with global bandwidth selection via "plug-in".

Find R packages from CRAN, rOpenSci', or Bioconductor corpora. Packages can be matched to general text descriptions, to names of installed packages, or to local paths to entire source repositories. The package is used to list the most similar packages for each new submission to the rOpenSci software peer-review program ('rOpenSci authors, 2026; <doi:10.5281/zenodo.18885936>).

Periodic B Splines Basis.

This package provides functions tailored for scientific and student communities involved in plant science research. Functionalities encompass estimation chlorophyll content according to Arnon (1949) <doi:10.1104/pp.24.1.1>, determination water potential of Polyethylene glycol(PEG)6000 as in Michel and Kaufmann (1973) <doi:10.1104/pp.51.5.914> and functions related to estimation of yield related indices like Abiotic tolerance index as given by Moosavi et al.(2008)<doi:10.22059/JDESERT.2008.27115>, Geometric mean productivity (GMP) by Fernandez (1992) <ISBN:92-9058-081-X>, Golden Mean by Moradi et al.(2012)<doi:10.14207/ejsd.2012.v1n3p543>, HAM by Schneider et al.(1997)<doi:10.2135/cropsci1997.0011183X003700010007x>,MPI and TOL by Hossain etal., (1990)<doi:10.2135/cropsci1990.0011183X003000030030x>, RDI by Fischer et al. (1979)<doi:10.1071/AR9791001>,SSI by Fisher et al.(1978)<doi:10.1071/AR9780897>, STI by Fernandez (1993)<doi:10.22001/wvc.72511>,YSI by Bouslama & Schapaugh (1984)<doi:10.2135/cropsci1984.0011183X002400050026x>, Yield index by Gavuzzi et al.(1997)<doi:10.4141/P96-130>.

This package provides functions to estimate and plot smooth or linear population trends, or population indices, from animal or plant count survey data.

Joint frailty models have been widely used to study the associations between recurrent events and a survival outcome. However, existing joint frailty models only consider one or a few recurrent events and cannot deal with high-dimensional recurrent events. This package can be used to fit our recently developed penalized joint frailty model that can handle high-dimensional recurrent events. Specifically, an adaptive lasso penalty is imposed on the parameters for the effects of the recurrent events on the survival outcome, which allows for variable selection. Also, our algorithm is computationally efficient, which is based on the Gaussian variational approximation method.

This package provides a set of basic tools for generating, analyzing, summarizing and visualizing finite partially ordered sets. In particular, it implements flexible and very efficient algorithms for the extraction of linear extensions and for the computation of mutual ranking probabilities and other user-defined functionals, over them. The package is meant as a computationally efficient "engine", for the implementation of data analysis procedures, on systems of multidimensional ordinal indicators and partially ordered data, in the spirit of Fattore, M. (2016) "Partially ordered sets and the measurement of multidimensional ordinal deprivation", Social Indicators Research <DOI:10.1007/s11205-015-1059-6>, and Fattore M. and Arcagni, A. (2018) "A reduced posetic approach to the measurement of multidimensional ordinal deprivation", Social Indicators Research <DOI:10.1007/s11205-016-1501-4>.

This package provides a PEP, or Portable Encapsulated Project, is a dataset that subscribes to the PEP structure for organizing metadata. It is written using a simple YAML + CSV format, it is your one-stop solution to metadata management across data analysis environments. This package reads this standardized project configuration structure into R. Described in Sheffield et al. (2021) <doi:10.1093/gigascience/giab077>.

Allows for nonparametric regression where one assumes that the signal is given by the sum of a piecewise constant function and a smooth function. More precisely, it implements the estimator PCpluS (piecewise constant plus smooth regression estimator) from Pein and Shah (2025) <doi:10.48550/arXiv.2112.03878>.

Designed for prediction error estimation through resampling techniques, possibly accelerated by parallel execution on a compute cluster. Newly developed model fitting routines can be easily incorporated. Methods used in the package are detailed in Porzelius Ch., Binder H. and Schumacher M. (2009) <doi:10.1093/bioinformatics/btp062> and were used, for instance, in Porzelius Ch., Schumacher M. and Binder H. (2011) <doi:10.1007/s00180-011-0236-6>.

Fits successive Lasso models for several blocks of (omics) data with different priorities and takes the predicted values as an offset for the next block. Also offers options to deal with block-wise missingness in multi-omics data.

Helpers for preparing ecommerce product photo planning tables, prompt sheets, and public ProductShot AI workflow URLs. The package works offline and focuses on data-frame preparation for product image batches.

An implementation of reliability estimation methods described in the paper (Bosnic, Z., & Kononenko, I. (2008) <doi:10.1007/s10489-007-0084-9>), which allows you to test the reliability of a single predicted instance made by your model and prediction function. It also allows you to make a correlation test to estimate which reliability estimate is the most accurate for your model.

Curated color palettes drawn from India's natural beauty - Himalayan snow, Thar dunes, Kerala backwaters, Andaman reefs, Spiti's cold desert, Kashmir's autumn chinar, and more. Provides discrete and continuous palettes with first-class ggplot2 integration through scale_color_prakriti() and scale_fill_prakriti(), plus base graphics helpers for displaying palettes.

This package provides programmatic access to GitHub API with a focus on project management. Key functionality includes setting up issues and milestones from R objects or YAML configurations, querying outstanding or completed tasks, and generating progress updates in tables, charts, and RMarkdown reports. Useful for those using GitHub in personal, professional, or academic settings with an emphasis on streamlining the workflow of data analysis projects.

Data sets and functions used in the polish book "Przewodnik po pakiecie R" (The Hitchhiker's Guide to the R). See more at <http://biecek.pl/R>. Among others you will find here data about housing prices, cancer patients, running times and many others.

This package implements a range of facilities for post-hoc analysis and summarizing linear models, generalized linear models and generalized linear mixed models, including grouping and clustering via pairwise comparisons using graph representations and efficient algorithms for finding maximal cliques of a graph. Includes also non-parametric toos for post-hoc analysis. It has S3 methods for printing summarizing, and producing plots, line and barplots suitable for post-hoc analyses.

Computes classical process capability indices (Cp, Cpk, Cpu, Cpl, Cpm, Cpmk, Pp, Ppk, Ppu, Ppl, Z) and the generalized process capability index Cpy (Maiti, Saha & Nanda, 2010) <doi:10.1080/16843703.2010.11673233> for any continuous or discrete quality characteristic. Users supply the probability density function (PDF) and cumulative distribution function (CDF) of the characteristic, and the package returns point estimates, bootstrap confidence intervals (percentile and BCa), and sensitivity tables/plots across ranges of short-term standard deviation (sigma), long-term standard deviation (s), desired yield (p0), and significance levels. Classical indices are recoverable as special cases under the normal distribution. The package follows the theory and notation of Kane (1986) <doi:10.1080/00224065.1986.11978984>, Chan, Cheng & Spiring (1988) <doi:10.1080/00224065.1988.11979102>, Pearn, Kotz & Johnson (1992) <doi:10.1080/00224065.1992.11979403>, Kotz & Johnson (2002) <doi:10.1080/00224065.2002.11980119>, Montgomery (2020, ISBN:978-1-119-39930-8), Juran (1974, ISBN:978-0-07-033176-1), Harry & Schroeder (2000, ISBN:978-0-385-49437-2), and the AIAG SPC Reference Manual (2005, ISBN:978-1-60534-026-3).

Hexadecimal codes are typically used to represent colors in R. Connecting these codes to their colors requires practice or memorization. palette provides a vctrs class for working with color palettes, including printing and plotting functions. The goal of the class is to place visual representations of color palettes directly on or, at least, next to their corresponding character representations. Palette extensions also are provided for data frames using pillar'.

This package provides functions to easily convert data to binary formats other programs/machines can understand.

An implementation of a non-parametric statistical model using a parallelised Monte Carlo sampling scheme. The method implemented in this package allows non-parametric inference to be regularized for small sample sizes, while also being more accurate than approximations such as variational Bayes. The concentration parameter is an effective sample size parameter, determining the faith we have in the model versus the data. When the concentration is low, the samples are close to the exact Bayesian logistic regression method; when the concentration is high, the samples are close to the simplified variational Bayes logistic regression. The method is described in full in the paper Lyddon, Walker, and Holmes (2018), "Nonparametric learning from Bayesian models with randomized objective functions" <doi:10.48550/arXiv.1806.11544>.

This package implements novel tools for estimating sample sizes needed for phylogenetic studies, including studies focused on estimating the probability of true pathogen transmission between two cases given phylogenetic linkage and studies focused on tracking pathogen variants at a population level. Methods described in Wohl, Giles, and Lessler (2021) and in Wohl, Lee, DiPrete, and Lessler (2023).