Generates chronological and ordered p-plots for data vectors or vectors of p-values. The p-plot visualizes the evolution of the p-value of a significance test across the sampled data. It allows for assessing the consistency of the observed effects, for detecting the presence of potential moderator variables, and for estimating the influence of outlier values on the observed results. For non-significant findings, it can diagnose patterns indicative of underpowered study designs. The p-plot can thus either back the binary accept-vs-reject decision of common null-hypothesis significance tests, or it can qualify this decision and stimulate additional empirical work to arrive at more robust and replicable statistical inferences.

Read depth data from genotyping-by-sequencing (GBS) or restriction site-associated DNA sequencing (RAD-seq) are imported and used to make Bayesian probability estimates of genotypes in polyploids or diploids. The genotype probabilities, posterior mean genotypes, or most probable genotypes can then be exported for downstream analysis. polyRAD is described by Clark et al. (2019) <doi:10.1534/g3.118.200913>, and the Hind/He statistic for marker filtering is described by Clark et al. (2022) <doi:10.1186/s12859-022-04635-9>. A variant calling pipeline for highly duplicated genomes is also included and is described by Clark et al. (2020, Version 1) <doi:10.1101/2020.01.11.902890>.

Data sets for the Panel Data Econometrics with R <doi:10.1002/9781119504641> book.

This package implements a general framework for creating dependency graphs using projection as introduced in Fan, Feng and Xia (2019)<arXiv:1501.01617>. Both lasso and sparse additive model projections are implemented. Both Pearson correlation and distance covariance options are available to generate the graph.

When using pooled p-values to adjust for multiple testing, there is an inherent balance that must be struck between rejection based on weak evidence spread among many tests and strong evidence in a few, explored in Salahub and Olford (2023) <arXiv:2310.16600>. This package provides functionality to compute marginal and central rejection levels and the centrality quotient for p-value pooling functions and provides implementations of the chi-squared quantile pooled p-value (described in Salahub and Oldford (2023)) and a proposal from Heard and Rubin-Delanchy (2018) <doi:10.1093/biomet/asx076> to control the quotient's value.

This package provides functions for phenological data preprocessing, modelling and result handling. For more information, please refer to Lange et al. (2016) <doi:10.1007/s00484-016-1161-8>.

This package provides functions for creating color palettes, visualizing palettes, modifying colors, and assigning colors for plotting.

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>.

Procrustes matching of the posterior samples of person and item latent positions from latent space item response models. The methods implemented in this package are based on work by Borg, I., Groenen, P. (1997, ISBN:978-0-387-94845-4), Jeon, M., Jin, I. H., Schweinberger, M., Baugh, S. (2021) <doi:10.1007/s11336-021-09762-5>, and Andrew, D. M., Kevin M. Q., Jong Hee Park. (2011) <doi:10.18637/jss.v042.i09>.

The main function, plot_GMM, is used for plotting output from Gaussian mixture models (GMMs), including both densities and overlaying mixture weight component curves from the fit GMM. The package also include the function, plot_cut_point, which plots the cutpoint (mu) from the GMM over a histogram of the distribution with several color options. Finally, the package includes the function, plot_mix_comps, which is used in the plot_GMM function, and can be used to create a custom plot for overlaying mixture component curves from GMMs. For the plot_mix_comps function, usage most often will be specifying the "fun" argument within "stat_function" in a ggplot2 object.

This package provides tools to show and draw image pixels using HTML widgets and Shiny applications. It can be used to visualize the MNIST dataset for handwritten digit recognition or to create new image recognition datasets.

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>).

Download and generate summaries for the rodent, plant, ant, and weather data from the Portal Project. Portal is a long-term (and ongoing) experimental monitoring site in the Chihuahuan desert. The raw data files can be found at <https://github.com/weecology/portaldata>.

Allows to parse Java properties files in the context of R Service Bus applications.

This package implements multi-armed bandit approaches for pricing experiments with an unknown demand curve, as developed in Weaver, Kumar, and Jain, "Nonparametric Pricing Bandits Leveraging Informational Externalities to Learn the Demand Curve" <doi:10.1287/mksc.2022.0247>. Includes Upper Confidence Bound (UCB) and Thompson Sampling (TS) baselines, Gaussian process variants ('GP-UCB', GP-TS'), monotonic Gaussian process variants that constrain demand to be weakly decreasing in price, and heterogeneous-noise extensions. The willingness-to-pay distribution is fully user-specified via a vector of consumer valuations, so any demand environment can be simulated or replayed.

Analyzing regression data with many and/or highly collinear predictor variables, by simultaneously reducing the predictor variables to a limited number of components and regressing the criterion variables on these components (de Jong S. & Kiers H. A. L. (1992) <doi:10.1016/0169-7439(92)80100-I>). Several rotation and model selection options are provided.

This package implements IV-estimator and Bayesian estimator for linear-in-means Spatial Autoregressive (SAR) model (see LeSage, 1997 <doi:10.1177/016001769702000107>; Lee, 2004 <doi:10.1111/j.1468-0262.2004.00558.x>; Bramoullé et al., 2009 <doi:10.1016/j.jeconom.2008.12.021>), while assuming that only a partial information about the network structure is available. Examples are when the adjacency matrix is not fully observed or when only consistent estimation of the network formation model is available (see Boucher and Houndetoungan, 2025 <doi:10.48550/arXiv.2509.08145>).

Data for the extraterrestrial solar spectral irradiance and ground level solar spectral irradiance and irradiance. In addition data for shade light under vegetation and irradiance time series from different broadband sensors. Part of the r4photobiology suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.

Manipulation and analysis of phylogenetically simulated data sets and phylogenetically based analyses using GLS.

This package provides an automated framework for penalized regression analysis using Ridge Regression, Lasso Regression and Elastic Net Regression. The package performs data standardization, training-testing data partitioning, cross-validation for hyperparameter tuning, model fitting, coefficient estimation, variable importance assessment, prediction, and performance evaluation. It simplifies regularized regression analysis by integrating the complete modeling workflow into a single function suitable for researchers for better understanding of the data.The methods are based on Hoerl and Kennard (1970) <doi:10.1080/00401706.1970.10488634>, Zou and Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>, and Friedman et al. (2010) <doi:10.18637/jss.v033.i01>.

An open-access tool/framework to download, validate, visualize, and analyze multi-source precipitation data. More information and an example of implementation can be found in Vargas Godoy and Markonis (2023, <doi:10.1016/j.envsoft.2023.105711>).

Creation of linkage maps in polyploid species from marker dosage scores of an F1 cross from two heterozygous parents. Currently works for outcrossing diploid, autotriploid, autotetraploid and autohexaploid species, as well as segmental allotetraploids. Methods are described in a manuscript of Bourke et al. (2018) <doi:10.1093/bioinformatics/bty371>. Since version 1.1.0, both discrete and probabilistic genotypes are acceptable input; for more details on the latter see Liao et al. (2021) <doi:10.1007/s00122-021-03834-x>.

This package provides a convenient framework for aggregating and disaggregating continuously varying parameters (for example, case fatality ratio, with age) for proper parametrization of lower-resolution compartmental models (for example, with broad age categories) and subsequent upscaling of model outputs to high resolution (for example, as needed when calculating age-sensitive measures like years-life-lost).

Visualizes the coverage depth of a complete plastid genome as well as the equality of its inverted repeat regions in relation to the circular, quadripartite genome structure and the location of individual genes. For more information, please see Gruenstaeudl and Jenke (2020) <doi:10.1186/s12859-020-3475-0>.