Enrichment analysis enables researchers to uncover mechanisms underlying a phenotype. However, conventional methods for enrichment analysis do not take into account protein-protein interaction information, resulting in incomplete conclusions. pathfindR is a tool for enrichment analysis utilizing active subnetworks. The main function identifies active subnetworks in a protein-protein interaction network using a user-provided list of genes and associated p values. It then performs enrichment analyses on the identified subnetworks, identifying enriched terms (i.e. pathways or, more broadly, gene sets) that possibly underlie the phenotype of interest. pathfindR also offers functionalities to cluster the enriched terms and identify representative terms in each cluster, to score the enriched terms per sample and to visualize analysis results. The enrichment, clustering and other methods implemented in pathfindR are described in detail in Ulgen E, Ozisik O, Sezerman OU. 2019. pathfindR': An R Package for Comprehensive Identification of Enriched Pathways in Omics Data Through Active Subnetworks. Front. Genet. <doi:10.3389/fgene.2019.00858>.

Reads the provenance collected by the rdtLite or rdt packages, or other tools providing compatible PROV JSON output, created by the execution of a script or a console session, and provides a human-readable summary identifying the input and output files, the scripts used (if any), errors and warnings produced, and the environment in which it was executed. It can also optionally package all the files into a zip file. The exact format of the PROV JSON file created by rdtLite and rdt is described in <https://github.com/End-to-end-provenance/ExtendedProvJson>. More information about rdtLite and associated tools is available at <https://github.com/End-to-end-provenance/> and Lerner, Boose, and Perez (2018), Using Introspection to Collect Provenance in R, Informatics, <doi: 10.3390/informatics5010012>.

This wrapper houses PathLit API endpoints for R. The usage of these endpoints require the use of an API key which can be obtained at <https://www.pathlit.io/docs/cli/>.

Smoothing splines with penalties on order m derivatives.

This package provides functions for fitting abundance distributions over environmental gradients to the species in ecological communities, and tools for simulating the fossil assemblages from those abundance models for such communities, as well as simulating assemblages across various patterns of sedimentary history and sampling. These tools are for particular use with fossil records with detailed age models and abundance distributions used for calculating environmental gradients from ordinations or other indices based on fossil assemblages.

Power and sample size calculations for a variety of study designs and outcomes. Methods include t tests, ANOVA (including tests for interactions, simple effects and contrasts), proportions, categorical data (chi-square tests and proportional odds), linear, logistic and Poisson regression, alternative and coprimary endpoints, power for confidence intervals, correlation coefficient tests, cluster randomized trials, individually randomized group treatment trials, multisite trials, treatment-by-covariate interaction effects and nonparametric tests of location. Utilities are provided for computing various effect sizes. Companion package to the book "Power and Sample Size in R", Crespi (2025, ISBN:9781138591622). Further resources available at <https://powerandsamplesize.org/>.

Tests periodicity in short time series using response surface regression.

This package provides a fast and flexible framework for agglomerative partitioning. partition uses an approach called Direct-Measure-Reduce to create new variables that maintain the user-specified minimum level of information. Each reduced variable is also interpretable: the original variables map to one and only one variable in the reduced data set. partition is flexible, as well: how variables are selected to reduce, how information loss is measured, and the way data is reduced can all be customized. partition is based on the Partition framework discussed in Millstein et al. (2020) <doi:10.1093/bioinformatics/btz661>.

Quickly and easily generate plots of acoustic data aligned with transcriptions similar to those made in Praat using either derived signals generated directly in R with wrassp or imported derived signals from Praat'. Provides easy and fast out-of-the-box solutions but also a high extent of flexibility. Also provides options for embedding audio in figures and animating figures.

This package provides access to word predictability estimates using large language models (LLMs) based on transformer architectures via integration with the Hugging Face ecosystem <https://huggingface.co/>. The package interfaces with pre-trained neural networks and supports both causal/auto-regressive LLMs (e.g., GPT-2') and masked/bidirectional LLMs (e.g., BERT') to compute the probability of words, phrases, or tokens given their linguistic context. For details on GPT-2 and causal models, see Radford et al. (2019) <https://storage.prod.researchhub.com/uploads/papers/2020/06/01/language-models.pdf>, for details on BERT and masked models, see Devlin et al. (2019) <doi:10.48550/arXiv.1810.04805>. By enabling a straightforward estimation of word predictability, the package facilitates research in psycholinguistics, computational linguistics, and natural language processing (NLP).

Recent years have seen an increased interest in novel methods for analyzing quantitative data from experimental psychology. Currently, however, they lack an established and accessible software framework. Many existing implementations provide no guidelines, consisting of small code snippets, or sets of packages. In addition, the use of existing packages often requires advanced programming experience. PredPsych is a user-friendly toolbox based on machine learning predictive algorithms. It comprises of multiple functionalities for multivariate analyses of quantitative behavioral data based on machine learning models.

This package contains functions to fit proportional hazards (PH) model to partly interval-censored (PIC) data (Pan et al. (2020) <doi:10.1177/0962280220921552>), PH model with spatial frailty to spatially dependent PIC data (Pan and Cai (2021) <doi:10.1080/03610918.2020.1839497>), and mixed effects PH model to clustered PIC data. Each random intercept/random effect can follow both a normal prior and a Dirichlet process mixture prior. It also includes the corresponding functions for general interval-censored data.

Directly pipes raw quantitative PCR (qPCR) machine outputs into downstream analyses using the comparative Ct (Delta-Delta Ct) method described by Livak and Schmittgen (2001) <doi:10.1006/meth.2001.1262>. Streamlines the workflow from Excel export to publication-ready plots. Integrates unique visual quality control by reconstructing 96-well plate heatmaps, allowing users to instantly detect pipetting errors, edge effects, and outliers. Key features include automated error propagation, laboratory master mix calculations, and generation of bar charts and volcano plots.

This package implements a unified interface for benchmarking meta-analytic publication bias correction methods through simulation studies (see BartoÅ¡ et al., 2025, <doi:10.48550/arXiv.2510.19489>). It provides 1) predefined data-generating mechanisms from the literature, 2) functions for running meta-analytic methods on simulated data, 3) pre-simulated datasets and pre-computed results for reproducible benchmarks, 4) tools for visualizing and comparing method performance.

Design and implementation of Percentile-based Shewhart Control Charts for continuous data. Faraz (2019) <doi:10.1002/qre.2384>.

Evaluate a function across a grid of parameters. The function may be evaluated once, or many times for simulation. Parallel computing is facilitated. Utilities aim at performing analyses of power and sample size, allowing for easy search of minimum n (or min/max of any other parameter) to achieve a desired minimal level of power (or maximum of any other objective). Plotting functions are included that present the dependency of n and power in relation to further assumptions.

Simulating and conducting four phase 12 clinical trials with correlated binary bivariate outcomes described. Uses the Efftox (efficacy and toxicity tradeoff, <https://biostatistics.mdanderson.org/SoftwareDownload/SingleSoftware/Index/2>) and SPSO (Semi-Parametric Stochastic Ordering) models with Utility and Desirability based objective functions for dose finding.

Support for parallel computation with progress bar, and option to stop or proceed on errors. Also provides logging to console and disk, and the logging persists in the parallel threads. Additional functions support function call automation with delayed execution (e.g. for executing functions in parallel).

Estimate sample size based on precision rather than power. precisely is a study planning tool to calculate sample size based on precision. Power calculations are focused on whether or not an estimate will be statistically significant; calculations of precision are based on the same principles as power calculation but turn the focus to the width of the confidence interval. precisely is based on the work of Rothman and Greenland (2018).

This package provides tools for both single and batch image manipulation and analysis (Olivoto, 2022 <doi:10.1111/2041-210X.13803>) and phytopathometry (Olivoto et al., 2022 <doi:10.1007/S40858-021-00487-5>). The tools can be used for the quantification of leaf area, object counting, extraction of image indexes, shape measurement, object landmark identification, and Elliptical Fourier Analysis of object outlines (Claude (2008) <doi:10.1007/978-0-387-77789-4>). The package also provides a comprehensive pipeline for generating shapefiles with complex layouts and supports high-throughput phenotyping of RGB, multispectral, and hyperspectral orthomosaics. This functionality facilitates field phenotyping using UAV- or satellite-based imagery.

In Shiny apps, it is sometimes useful to store information on a particular item in a tooltip. Prompter allows you to easily create such tooltips, using Hint.css'.

Create a parallel coordinates plot, using `htmlwidgets` package and `d3.js`.

For a given graph containing vertices, edges, and a signal associated with the vertices, the PathwaySpace package performs a convolution operation, which involves a weighted combination of neighboring vertices and their associated signals. The package then uses a decay function to project these signals, creating geodesic paths on a 2D-image space. PathwaySpace could have various applications, such as visualizing network data in a graphical format that highlights the relationships and signal strengths between vertices. It can be particularly useful for understanding the influence of signals through complex networks. By combining graph theory, signal processing, and visualization, the PathwaySpace package provides a novel way of representing graph data.

To Simplify the time consuming and error prone task of assembling complex data sets for non-linear mixed effects modeling. Users are able to select from different absorption processes such as zero and first order, or a combination of both. Furthermore, data sets containing data from several entities, responses, and covariates can be simultaneously assembled.