The reliability of clusters is estimated using random projections. A set of stability measures is provided to assess the reliability of the clusters discovered by a generic clustering algorithm. The stability measures are taylored to high dimensional data (e.g. DNA microarray data) (Valentini, G (2005), <doi:10.1093/bioinformatics/bti817>.

Estimation of population size of migratory caribou herds based on large scale aggregations monitored by radio telemetry. It implements the methodology found in the article by Rivest et al. (1998) about caribou abundance estimation. It also includes a function based on the Lincoln-Petersen Index as applied to radio telemetry data by White and Garrott (1990).

Circumplex models, which organize constructs in a circle around two underlying dimensions, are popular for studying interpersonal functioning, mood/affect, and vocational preferences/environments. This package provides tools for analyzing and visualizing circular data, including scoring functions for relevant instruments and a generalization of the bootstrapped structural summary method from Zimmermann & Wright (2017) <doi:10.1177/1073191115621795> and functions for creating publication-ready tables and figures from the results.

The primary function makeCPMSampler() generates a sampler function which performs the correlated pseudo-marginal method of Deligiannidis, Doucet and Pitt (2017) <arXiv:1511.04992>. If the rho= argument of makeCPMSampler() is set to 0, then the generated sampler function performs the original pseudo-marginal method of Andrieu and Roberts (2009) <DOI:10.1214/07-AOS574>. The sampler function is constructed with the user's choice of prior, parameter proposal distribution, and the likelihood approximation scheme. Note that this algorithm is not automatically tuned--each one of these arguments must be carefully chosen.

This package provides a tool for analyzing conjoint experiments using Bayesian Additive Regression Trees ('BART'), a machine learning method developed by Chipman, George and McCulloch (2010) <doi:10.1214/09-AOAS285>. This tool focuses specifically on estimating, identifying, and visualizing the heterogeneity within marginal component effects, at the observation- and individual-level. It uses a variable importance measure ('VIMP') with delete-d jackknife variance estimation, following Ishwaran and Lu (2019) <doi:10.1002/sim.7803>, to obtain bias-corrected estimates of which variables drive heterogeneity in the predicted individual-level effects.

Salmonella enterica is a major cause of bacterial food-borne disease worldwide. Serotype identification is the most commonly used typing method to characterize Salmonella isolates. However, experimental serotyping needs great cost on manpower and resources. Recently, we found that the newly incorporated spacer in the clustered regularly interspaced short palindromic repeat (CRISPR) could serve as an effective marker for typing of Salmonella. It was further revealed by Li et. al (2014) <doi:10.1128/JCM.00696-14> that recognized types based on the combination of two newly incorporated spacer in both CRISPR loci showed high accordance with serotypes. Here, we developed an R package CSESA to predict the serotype based on this finding. Considering itâ s time saving and of high accuracy, we recommend to predict the serotypes of unknown Salmonella isolates using CSESA before doing the traditional serotyping.

Hardware-based support for CRC32C cyclic redundancy checksum function is made available for x86_64 systems with SSE2 support as well as for arm64', and detected at build-time via cmake with a software-based fallback. This functionality is exported at the C'-language level for use by other packages. CRC32C is described in RFC 3270 at <https://datatracker.ietf.org/doc/html/rfc3720> and is based on Castagnoli et al <doi:10.1109/26.231911>.

Implementation of the Cluster Estimated Standard Errors (CESE) proposed in Jackson (2020) <DOI:10.1017/pan.2019.38> to compute clustered standard errors of linear coefficients in regression models with grouped data.

Easily create color-coded (choropleth) maps in R. No knowledge of cartography or shapefiles needed; go directly from your geographically identified data to a highly customizable map with a single line of code! Supported geographies: U.S. states, counties, census tracts, and zip codes, world countries and sub-country regions (e.g., provinces, prefectures, etc.).

Counts colors within color range(s) in images, and provides a masked version of the image with targeted pixels changed to a different color. Output includes the locations of the pixels in the images, and the proportion of the image within the target color range with optional background masking. Users can specify multiple color ranges for masking.

The statistical analysis of circular data using distributions based on symmetric Nonnegative Trigonometric Sums (NNTS). It includes functions to perform empirical analysis and estimate the parameters of density functions. Fernandez-Duran, J.J. and Gregorio-Dominguez, M.M. (2025), "Multimodal Symmetric Circular Distributions Based on Nonnegative Trigonometric Sums and a Likelihood Ratio Test for Reflective Symmetry", <doi:10.48550/arXiv.2412.19501>.

The implementation of bias-corrected sandwich variance estimators for the analysis of cluster randomized trials with time-to-event outcomes using the marginal Cox model, proposed by Wang et al. (under review).

Create and integrate maps in your R workflow. This package helps to design cartographic representations such as proportional symbols, choropleth, typology, flows or discontinuities maps. It also offers several features that improve the graphic presentation of maps, for instance, map palettes, layout elements (scale, north arrow, title...), labels or legends. See Giraud and Lambert (2017) <doi:10.1007/978-3-319-57336-6_13>.

Estimating mutation and selection coefficients on synonymous codon bias usage based on models of ribosome overhead cost (ROC). Multinomial logistic regression and Markov Chain Monte Carlo are used to estimate and predict protein production rates with/without the presence of expressions and measurement errors. Work flows with examples for simulation, estimation and prediction processes are also provided with parallelization speedup. The whole framework is tested with yeast genome and gene expression data of Yassour, et al. (2009) <doi:10.1073/pnas.0812841106>.

This package provides constructions of series of partially balanced incomplete block designs (PBIB) based on the combinatory method S, introduced by Rezgui et al. (2014) <doi:10.3844/jmssp.2014.45.48>. This package also offers the associated U-type designs. Version 1.1-1 generalizes the approach to designs with v = wnl treatments. It includes various rectangular and generalized rectangular right angular association schemes with 4, 5, and 7 associated classes.

The cmgnd implements the constrained mixture of generalized normal distributions model, a flexible statistical framework for modelling univariate data exhibiting non-normal features such as skewness, multi-modality, and heavy tails. By imposing constraints on model parameters, the cmgnd reduces estimation complexity while maintaining high descriptive power, offering an efficient solution in the presence of distributional irregularities. For more details see Duttilo and Gattone (2025) <doi:10.1007/s00180-025-01638-x> and Duttilo et al (2025) <doi:10.48550/arXiv.2506.03285>.

This package implements a modern, unified estimation strategy for common mediation estimands (natural effects, organic effects, interventional effects, and recanting twins) in combination with modified treatment policies as described in Liu, Williams, Rudolph, and DÃ az (2024) <doi:10.48550/arXiv.2408.14620>. Estimation makes use of recent advancements in Riesz-learning to estimate a set of required nuisance parameters with deep learning. The result is the capability to estimate mediation effects with binary, categorical, continuous, or multivariate exposures with high-dimensional mediators and mediator-outcome confounders using machine learning.

Estimate bivariate common mean vector under copula models with known correlation. In the current version, available copulas are the Clayton, Gumbel, Frank, Farlie-Gumbel-Morgenstern (FGM), and normal copulas. See Shih et al. (2019) <doi:10.1080/02331888.2019.1581782> and Shih et al. (2021) <under review> for details under the FGM and general copulas, respectively.

The cystiSim package provides an agent-based model for Taenia solium transmission and control. cystiSim was developed within the framework of CYSTINET, the European Network on taeniosis/cysticercosis, COST ACTION TD1302.

This package provides a one-stop shop for intuitive and dependency-free color and palette creation and modification. Includes palettes and functionality from popular packages such as viridis', RColorBrewer', and base R grDevices', as well as ggplot2 plot bindings. Users can generate perceptually uniform and colorblind-friendly palettes, adjust palettes in HSL and RGB color spaces, map color gradients to value ranges, and create color-generating functions.

This package implements the model-free multiscale idealisation approaches: Jump-Segmentation by MUltiResolution Filter (JSMURF), Hotz et al. (2013) <doi:10.1109/TNB.2013.2284063>, JUmp Local dEconvolution Segmentation filter (JULES), Pein et al. (2018) <doi:10.1109/TNB.2018.2845126>, and Heterogeneous Idealization by Local testing and DEconvolution (HILDE), Pein et al. (2021) <doi:10.1109/TNB.2020.3031202>. Further details on how to use them are given in the accompanying vignette.

This package provides efficient implementation of the Cross-Covariance Isolate Detect (CCID) methodology for the estimation of the number and location of multiple change-points in the second-order (cross-covariance or network) structure of multivariate, possibly high-dimensional time series. The method is motivated by the detection of change points in functional connectivity networks for functional magnetic resonance imaging (fMRI), electroencephalography (EEG), magentoencephalography (MEG) and electrocorticography (ECoG) data. The main routines in the package have been extensively tested on fMRI data. For details on the CCID methodology, please see Anastasiou et al (2022), Cross-covariance isolate detect: A new change-point method for estimating dynamic functional connectivity. Medical Image Analysis, Volume 75.

This package contains 3 maps. 1) US States 2) US Counties 3) Countries of the world.

An implementation of several functions for feature extraction in categorical time series datasets. Specifically, some features related to marginal distributions and serial dependence patterns can be computed. These features can be used to feed clustering and classification algorithms for categorical time series, among others. The package also includes some interesting datasets containing biological sequences. Practitioners from a broad variety of fields could benefit from the general framework provided by ctsfeatures'.