This package provides a Seurat'-compatible toolkit for marker gene identification, expression summarization, and visualization of annotated single-cell transcriptomic data. CellWindX identifies top cell-type-enriched markers, calculates marker expression percentages and average expression values across cell groups, and generates publication-oriented dimensional reduction plots, marker heatmaps, and gene-level radar plots. The package includes built-in aesthetic palettes and supports both exploratory analysis and downstream figure preparation for single-cell atlas studies. The workflow is designed to complement single-cell analysis frameworks such as Seurat described by Satija et al. (2015) <doi:10.1038/nbt.3192> and Hao et al. (2021) <doi:10.1016/j.cell.2021.04.048>, as well as heatmap visualization methods implemented in ComplexHeatmap described by Gu et al. (2016) <doi:10.1093/bioinformatics/btw313>.

An R implementation of the Critical Path Method (CPM). CPM is a method used to estimate the minimum project duration and determine the amount of scheduling flexibility on the logical network paths within the schedule model. The flexibility is in terms of early start, early finish, late start, late finish, total float and free float. Beside, it permits to quantify the complexity of network diagram through the analysis of topological indicators. Finally, it permits to change the activities duration to perform what-if scenario analysis. The package was built based on following references: To make topological sorting and other graph operation, we use Csardi, G. & Nepusz, T. (2005) <https://www.researchgate.net/publication/221995787_The_Igraph_Software_Package_for_Complex_Network_Research>; For schedule concept, the reference was Project Management Institute (2017) <https://www.pmi.org/pmbok-guide-standards/foundational/pmbok>; For standards terms, we use Project Management Institute (2017) <https://www.pmi.org/pmbok-guide-standards/lexicon>; For algorithms on Critical Path Method development, we use Vanhoucke, M. (2013) <doi:10.1007/978-3-642-40438-2> and Vanhoucke, M. (2014) <doi:10.1007/978-3-319-04331-9>; And, finally, for topological definitions, we use Vanhoucke, M. (2009) <doi:10.1007/978-1-4419-1014-1>.

This package provides data science tools for conservation science, including methods for environmental data analysis, humidity calculations, sustainability metrics, engineering calculations, and data visualisation. Supports conservators, scientists, and engineers working with cultural heritage preventive conservation data. The package is motivated by the framework outlined in Cosaert and Beltran et al. (2022) "Tools for the Analysis of Collection Environments" <https://www.getty.edu/conservation/publications_resources/pdf_publications/tools_for_the_analysis_of_collection_environments.html>.

Allows to generate colors from palettes defined in the colormap module of Node.js'. (see <https://github.com/bpostlethwaite/colormap> for more information). In total it provides 44 distinct palettes made from sequential and/or diverging colors. In addition to the pre defined palettes you can also specify your own set of colors. There are also scale functions that can be used with ggplot2'.

An R implementation of the algorithms described in Reingold and Dershowitz (4th ed., Cambridge University Press, 2018) <doi:10.1017/9781107415058>, allowing conversion between many different calendar systems. Cultural and religious holidays from several calendars can be calculated.

Generate random numbers from the Cryptographically Secure Pseudorandom Number Generator (CSPRNG) provided by the underlying operating system. System CSPRNGs are seeded internally by the OS with entropy it gathers from the system hardware. The following system functions are used: arc4random_buf() on macOS and BSD; BCryptgenRandom() on Windows; Sys_getrandom() on Linux.

This package implements a deficiency-theoretic framework for causal inference, grounded in the classical theory of statistical experiment comparison, as described in Akdemir (2026) <doi:10.5281/zenodo.21877511>. Provides theorem-backed bounds together with computable proxy diagnostics for information loss from confounding, selection bias, and distributional shift. Supports continuous, binary, count, survival, and competing risks outcomes. Key features include propensity-score total-variation deficiency proxies, negative control diagnostics, policy regret bounds, and sensitivity analysis via confounding frontiers.

Estimate coefficients of Cox proportional hazards model using stochastic gradient descent algorithm for batch data.

This package provides access to consolidated information from the Brazilian Federal Government Payment Card. Includes functions to retrieve, clean, and organize data directly from the Transparency Portal <https://portaldatransparencia.gov.br/download-de-dados/cpgf/> and a curated dataset hosted on the Open Science Framework <https://osf.io/z2mxc/>. Useful for public spending analysis, transparency research, and reproducible workflows in auditing or investigative journalism.

Standard metrics converter and comparator for consumer electronics. Provides utility functions for converting battery capacity (mAh to Wh), comparing wall charger output times, and validating product specifications using standard formulas. Includes a sample dataset of electronic accessories compiled from CairoVolt's catalog.

Create and manipulate study cohorts in data mapped to the Observational Medical Outcomes Partnership Common Data Model.

Classification using Richard A. Harshman's Parallel Factor Analysis-1 (Parafac) model or Parallel Factor Analysis-2 (Parafac2) model fit to a three-way or four-way data array. See Harshman and Lundy (1994): <doi:10.1016/0167-9473(94)90132-5>. Classification using principal component analysis (PCA) fit to a two-way data matrix is also supported. Uses component weights from one mode of a Parafac, Parafac2, or PCA model as features to tune parameters for one or more classification methods via a k-fold cross-validation procedure. Allows for constraints on different tensor modes. Allows for inclusion of additional features alongside features generated by the component model. Supports penalized logistic regression, support vector machine, random forest, feed-forward neural network, regularized discriminant analysis, and gradient boosting machine. Supports binary and multiclass classification. Predicts class labels or class probabilities and calculates multiple classification performance measures. Uses the clue package to align Parafac or Parafac2 models across data splits in the cross-validation procedure. Calculates classification importance of individual features using permutation feature importance. Implements parallel computing via the foreach', doParallel', and doRNG packages.

The biases introduced in association measures, particularly mutual information, are influenced by factors such as tumor purity, mutation burden, and hypermethylation. This package provides the estimation of conditional mutual information (CMI) and its statistical significance with a focus on its application to multi-omics data. Utilizing B-spline functions (inspired by Daub et al. (2004) <doi:10.1186/1471-2105-5-118>), the package offers tools to estimate the association between heterogeneous multi- omics data, while removing the effects of confounding factors. This helps to unravel complex biological interactions. In addition, it includes methods to evaluate the statistical significance of these associations, providing a robust framework for multi-omics data integration and analysis. This package is ideal for researchers in computational biology, bioinformatics, and systems biology seeking a comprehensive tool for understanding interdependencies in omics data.

Fungi are ubiquitous in Earth's wonderfully diverse ecosystems. The ClassifyITS package aids in the taxonomic classification of internal transcribed spacer (ITS) fungal sequences. Unlike previous methods, it employs taxon-specific e-value and percent identity cutoffs at each taxonomic rank from kingdom to species. The package takes a conservative approach and outputs both graphics and user-friendly files to help users manually inspect fungal operational taxonomic units (OTUs) that fail classification at relevant levels (e.g., Phylum). ClassifyITS is based on taxonomic cutoff criteria from "The Global Soil Mycobiome consortium dataset for boosting fungal diversity research" (Fungal Diversity, Tedersoo, 2021, <doi:10.1007/s13225-021-00493-7>) and "Best practices in metabarcoding of fungi: From experimental design to results" (Molecular Ecology, Tedersoo, 2022, <doi:10.1111/mec.16460>).

Comprehensive suite of Granger causality tests for time series and panel data. For time series: Toda-Yamamoto (1995) <doi:10.1016/0304-4076(94)01616-8>, Fourier-based tests with single frequency (Enders and Jones, 2016) <doi:10.1515/snde-2014-0101> and cumulative frequencies (Nazlioglu et al., 2019) <doi:10.1080/1540496X.2018.1434072>, quantile causality tests (Cai et al., 2023) <doi:10.1016/j.frl.2023.104327>, and Bootstrap Fourier Granger Causality in Quantiles (Cheng et al., 2021) <doi:10.1007/s12076-020-00263-0>. For panel data: Panel Fourier Toda-Yamamoto (Yilanci and Gorus, 2020) <doi:10.1007/s11356-020-10092-9> and Panel Quantile Causality tests (Wang and Nguyen, 2022) <doi:10.1080/1331677X.2021.1952089>, as well as Group-Mean and Pooled Fully Modified OLS estimators for panel cointegrating polynomial regressions (Wagner and Reichold, 2023) <doi:10.1080/07474938.2023.2178141>. All tests include bootstrap inference for robust p-values.

This package provides tools for visualization of, and inference on, the calibration of prediction models on the cumulative domain. This provides a method for evaluating calibration of risk prediction models without having to group the data or use tuning parameters (e.g., loess bandwidth). This package implements the methodology described in Sadatsafavi and Petkau (2024) <doi:10.1002/sim.10138>. The core of the package is cumulcalib(), which takes in vectors of binary responses and predicted risks. The package also implements non-parametric assessment of the calibration of individualized treatment effect (ITE) models using data from a randomized trial, via cumulcalibITE(), as described in Sadatsafavi et al. (2026) <doi:10.1002/sim.70724>. The plot() and summary() methods are implemented for the results returned by cumulcalib() and cumulcalibITE().

Estimates tree crown scorch from terrestrial lidar scans collected with a RIEGL vz400i. The methods follow those described in Cannon et al. (2025, Fire Ecology 21:71, <doi:10.1186/s42408-025-00420-0>).

This package provides the facility to perform the chi-square and G-square test of independence, calculates the retrospective power of the traditional chi-square test, compute permutation and Monte Carlo p-value, and provides measures of association for tables of any size such as Phi, Phi corrected, odds ratio with 95 percent CI and p-value, Yule Q and Y, adjusted contingency coefficient, Cramer's V, V corrected, V standardised, bias-corrected V, W, Cohen's w, Goodman-Kruskal's lambda, and tau. It also calculates standardised, moment-corrected standardised, and adjusted standardised residuals, and their significance, as well as the Quetelet Index, IJ association factor, and adjusted standardised counts. It also computes the chi-square-maximising version of the input table. Different outputs are returned in nicely formatted tables.

The Copernicus Data Space Ecosystem, is an open ecosystem that provides free instant access to a wide range of data and services from the Copernicus Sentinel missions and more on our planetâ s land, oceans and atmosphere. This package provides entry points to several APIs allowing users to access the data directly in R.

This package provides a generic, easy-to-use and expandable implementation of a pharmacokinetic (PK) / pharmacodynamic (PD) model based on the S4 class system. This package allows the user to read and write pharmacometric models from and to files, including a JSON-based interface to import Campsis models defined using a formal JSON schema distributed with the package. Models can be adapted further on the fly in the R environment using an intuitive API to add, modify or delete equations, ordinary differential equations (ODEs), model parameters or compartment properties (such as infusion duration or rate, bioavailability and initial values). The package also provides export facilities for use with the simulation packages rxode2 and mrgsolve'. The package itself is licensed under the GPL (>= 3); the JSON schema file shipped in inst/extdata is licensed separately under the Creative Commons Attribution 4.0 International (CC BY 4.0). This package is designed and intended to be used with the package campsis', a PK/PD simulation platform built on top of rxode2 and mrgsolve'.

Cross-validate one or multiple regression and classification models and get relevant evaluation metrics in a tidy format. Validate the best model on a test set and compare it to a baseline evaluation. Alternatively, evaluate predictions from an external model. Currently supports regression and classification (binary and multiclass). Described in chp. 5 of Jeyaraman, B. P., Olsen, L. R., & Wambugu M. (2019, ISBN: 9781838550134).

This package provides functions for implementing the novel algorithm CASCORE, which is designed to detect latent community structure in graphs with node covariates. This algorithm can handle models such as the covariate-assisted degree corrected stochastic block model (CADCSBM). CASCORE specifically addresses the disagreement between the community structure inferred from the adjacency information and the community structure inferred from the covariate information. For more detailed information, please refer to the reference paper: Yaofang Hu and Wanjie Wang (2022) <arXiv:2306.15616>. In addition to CASCORE, this package includes several classical community detection algorithms that are compared to CASCORE in our paper. These algorithms are: Spectral Clustering On Ratios-of Eigenvectors (SCORE), normalized PCA, ordinary PCA, network-based clustering, covariates-based clustering and covariate-assisted spectral clustering (CASC). By providing these additional algorithms, the package enables users to compare their performance with CASCORE in community detection tasks.

This package provides a collection of tools for estimating a network from a random sample of cognitive social structure (CSS) slices. Also contains functions for evaluating a CSS in terms of various error types observed in each slice.

Modelling structural zeros in compositional data assuming a latent Gaussian model, where MLE is performed via the EM algorithm. The relevant paper is Tsagris M. (2026). Modelling structural zeros in compositional data via a zero-censored multivariate normal model. <doi:10.48550/arXiv.2208.13073>.