Several authors have proposed methods for constructing simultaneous confidence intervals for multinomial proportions. The package implements seven classical approachesâ Wilson, Quesenberry and Hurst, Goodman, Wald (with and without continuity correction), Fitzpatrick and Scott, and Sison and Glazâ along with Bayesian methods based on Dirichlet models. Both equal and unequal Dirichlet priors are supported, providing a broad framework for inference, data analysis, and sensitivity evaluation.

This package creates project specific directory and file templates that are written to a .Rprofile file. Upon starting a new R session, these templates can be used to streamline the creation of new directories that are standardized to the user's preferences and can include the initiation of a git repository, an RStudio R project, and project-local dependency management with the renv package.

Adopts the general least squares-based data-driven normalization strategy developed by Heckmann et al. (2011) <doi:10.1186/1471-2105-12-250> to correct for technical variance in gene expression data generated via digital polymerase chain reaction (dPCR). Performs normalization of raw copy numbers and also calculates relative variability metrics that can be used to assess the impact of normalization on variance.

Go beyond standard probability distributions such as the Normal or Exponential by combining, shifting, maximizing, and otherwise transforming distributions with simple, verb-based functions. Provides easy access to a broader space of distributions more representative of real-world systems such as river flows or insurance claims. Part of the probaverse framework of packages to support advanced statistical modeling and simulations with an intuitive workflow.

Simple Principal Components Analysis (PCA) and (Multiple) Correspondence Analysis (CA) based on the Singular Value Decomposition (SVD). This package provides S4 classes and methods to compute, extract, summarize and visualize results of multivariate data analysis. It also includes methods for partial bootstrap validation described in Greenacre (1984, ISBN: 978-0-12-299050-2) and Lebart et al. (2006, ISBN: 978-2-10-049616-7).

This package contains a function called dmur() which accepts four parameters like possible values, probabilities of the values, selling cost and preparation cost. The dmur() function generates various numeric decision parameters like MEMV (Maximum (optimum) expected monitory value), best choice, EPPI (Expected profit with perfect information), EVPI (Expected value of the perfect information), EOL (Expected opportunity loss), which facilitate effective decision-making.

This package implements DB-TARF (Design-Based Targeted Adaptive Residual Forest) for large-scale digital soil and ecological mapping evaluated under the design-based paradigm of Wadoux et al. (2021) <doi:10.1016/j.ecolmodel.2021.109692>. A random forest is augmented by a cross-fitted, out-of-fold-selected residual correction (residual forests, ordinary kriging, recalibration), together with design-based conformal prediction intervals.

This package provides a genetic algorithm for finding variable subsets in high dimensional data with high prediction performance. The genetic algorithm can use ordinary least squares (OLS) regression models or partial least squares (PLS) regression models to evaluate the prediction power of variable subsets. By supporting different cross-validation schemes, the user can fine-tune the tradeoff between speed and quality of the solution.

This package provides density, distribution and random generation functions for the Linear Ballistic Accumulation (LBA) model, a widely used choice response time model in cognitive psychology. The package supports model specifications, parameter estimation, and likelihood computation, facilitating simulation and statistical inference for LBA-based experiments. For details on the LBA model, see Brown and Heathcote (2008) <doi:10.1016/j.cogpsych.2007.12.002>.

Mixture of logistic regressions parameters (H)estimation with (U)spectral methods. The main methods take d-dimensional inputs and a vector of binary outputs, and return parameters according to the GLMs mixture model (General Linear Model). For more details see chapter 3 in the PhD thesis of Mor-Absa Loum: <https://theses.fr/s156435>, available here <https://theses.hal.science/tel-01877796/document>.

This package provides a comprehensive suite of portfolio spanning tests for asset pricing, such as Huberman and Kandel (1987) <doi:10.1111/j.1540-6261.1987.tb03917.x>, Gibbons et al. (1989) <doi:10.2307/1913625>, Kempf and Memmel (2006) <doi:10.1007/BF03396737>, Pesaran and Yamagata (2024) <doi:10.1093/jjfinec/nbad002>, and Gungor and Luger (2016) <doi:10.1080/07350015.2015.1019510>.

Proxy forward modelling for sediment archived climate proxies such as Mg/Ca, d18O or Alkenones. The user provides a hypothesised "true" past climate, such as output from a climate model, and details of the sedimentation rate and sampling scheme of a sediment core. Sedproxy returns simulated proxy records. Implements the methods described in Dolman and Laepple (2018) <doi:10.5194/cp-14-1851-2018>.

This package provides a collection of functions for automatically creating Stan code for transition diagnostic classification models (TDCMs) as they are defined by Madison and Bradshaw (2018) <DOI:10.1007/s11336-018-9638-5>. This package supports automating the creation of Stan code for TDCMs, fungible TDCMs (i.e., TDCMs with item parameters constrained to be equal across all items), and multi-threaded TDCMs.

Fits hierarchical models of animal abundance and occurrence to data collected using survey methods such as point counts, site occupancy sampling, distance sampling, removal sampling, and double observer sampling. Parameters governing the state and observation processes can be modeled as functions of covariates. References: Kellner et al. (2023) <doi:10.1111/2041-210X.14123>, Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.

Converts arbitrary XML into canonical node tables and analysis-friendly rectangular outputs without requiring a vocabulary-specific parser. Supports conservative structure proposals, explicit reusable profiles, advisory XSD inspection, bounded streaming, CSV and Parquet output, an analyst-oriented single-table projection, and record-level parallel execution with automatic scheduling. The native implementation uses libxml2 for structural acceleration while the R implementation remains the semantic reference.

Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI libraries for simplifying ML compute. These are the provided Ray AI libraries:

• Data: Scalable datasets for ML;

• Train: Distributed training;

• Tune: Scalable hyperparameter tuning;

• RLlib: Scalable reinforcement learning;

• Serve: Scalable and programmable serving.

Roboto is Google’s signature family of fonts, the default font on Android and Chrome OS, and the recommended font for the visual language "Material Design". This is the Roboto Slab family, which can be used alongside the normal Roboto family and the Roboto Condensed family. The Github repo was archive on April 19, 2026 and this is built from a mirror of the last commit.

This package provides functions for bipartite network rewiring through N consecutive switching steps and for the computation of the minimal number of switching steps to be performed in order to maximise the dissimilarity with respect to the original network. It includes functions for the analysis of the introduced randomness across the switching steps and several other routines to analyse the resulting networks and their natural projections.

This package provides a a transcriptomic-based framework to dissect cell communication in a global manner. It integrates an original expert-curated database of ligand-receptor interactions taking into account multiple subunits expression. Based on transcriptomic profiles (gene expression), this package computes communication scores between cells and provides several visualization modes that can be helpful to dig into cell-cell interaction mechanism and extend biological knowledge.

Polytect is an advanced computational tool designed for the analysis of multi-color digital PCR data. It provides automatic clustering and labeling of partitions into distinct groups based on clusters first identified by the flowPeaks algorithm. Polytect is particularly useful for researchers in molecular biology and bioinformatics, enabling them to gain deeper insights into their experimental results through precise partition classification and data visualization.

Interacting with binary files can be difficult because R's types are a subset of what is generally supported by C'. This package provides a suite of functions for reading and writing binary data (with files, connections, and raw vectors) using C type descriptions. These functions convert data between C types and R types while checking for values outside the type limits, NA values, etc.

This package provides automated checks for common Comprehensive R Archive Network (CRAN) submission issues that are not caught by standard R CMD check'. Consolidates ad-hoc requirements that CRAN reviewers enforce but standard checks do not surface, helping R package maintainers identify and fix issues before submission to reduce rejection rates. Covers code-pattern issues, DESCRIPTION-field formatting, documentation problems, and general package structure concerns.

Nonparametric kernel density estimation, bandwidth selection, and other utilities for analyzing directional data. Implements the estimator in Bai, Rao and Zhao (1987) <doi:10.1016/0047-259X(88)90113-3>, the cross-validation bandwidth selectors in Hall, Watson and Cabrera (1987) <doi:10.1093/biomet/74.4.751> and the plug-in bandwidth selectors in Garcà a-Portugués (2013) <doi:10.1214/13-ejs821>.

This package contains data organized by topics: categorical data, regression model, means comparisons, independent and repeated measures ANOVA, mixed ANOVA and ANCOVA. The package code is under GPL-2; the data sets are covered by the terms in the LICENSE.note file (the author's data sets under CC0 1.0, and the heartdisease data under CC BY 4.0, see the heartdisease help page).