This package performs Bayesian nonparametric density estimation using Martingale posterior distributions including the Copula Resampling (CopRe) algorithm. Also included are a Gibbs sampler for the marginal Gibbs-type mixture model and an extension to include full uncertainty quantification via a predictive sequence resampling (SeqRe) algorithm. The CopRe and SeqRe samplers generate random nonparametric distributions as output, leading to complete nonparametric inference on posterior summaries. Routines for calculating arbitrary functionals from the sampled distributions are included as well as an important algorithm for finding the number and location of modes, which can then be used to estimate the clusters in the data using, for example, k-means. Implements work developed in Moya B., Walker S. G. (2022). <doi:10.48550/arxiv.2206.08418>, Fong, E., Holmes, C., Walker, S. G. (2021) <doi:10.48550/arxiv.2103.15671>, and Escobar M. D., West, M. (1995) <doi:10.1080/01621459.1995.10476550>.

This package provides tools for advanced analysis of continuous glucose monitoring (CGM) time-series, implementing GRID (Glucose Rate Increase Detector) and GRID-based algorithms for postprandial peak detection, and detection of hypoglycemic and hyperglycemic episodes (Levels 1/2/Extended) aligned with international consensus CGM metrics. Core algorithms are implemented in optimized C++ using Rcpp to provide accurate and fast analysis on large datasets.

Computes the coverage correlation coefficient introduced in <doi:10.48550/arXiv.2508.06402> , a statistical measure that quantifies dependence between two random vectors by computing the union volume of data-centered hypercubes in a uniform space.

This package provides a causal mediation framework for cell type-specific single-cell data based on joint mediator multilevel models. The framework jointly models overdispersed counts and zero inflation for the mediators.

This package provides function declarations and inline function definitions that facilitate communication between R and the Armadillo C++ library for linear algebra and scientific computing. This implementation is detailed in Vargas Sepulveda and Schneider Malamud (2024) <doi:10.1016/j.softx.2025.102087>.

Calculate date of birth, age, and gender, and generate anonymous sequence numbers from CPR numbers. <https://en.wikipedia.org/wiki/Personal_identification_number_(Denmark)>.

The Large Language Model (LLM) represents a groundbreaking advancement in data science and programming, and also allows us to extend the world of R. A seamless interface for integrating the OpenAI Web APIs into R is provided in this package. This package leverages LLM-based AI techniques, enabling efficient knowledge discovery and data analysis. The previous functions such as seamless translation and image generation have been moved to other packages deepRstudio and stableDiffusion4R'.

Implementation of the Wilkinson and Ivany (2002) approach to paleoclimate analysis, applied to isotope data extracted from clams.

Conditional distance correlation <doi:10.1080/01621459.2014.993081> is a novel conditional dependence measurement of two multivariate random variables given a confounding variable. This package provides conditional distance correlation, performs the conditional distance correlation sure independence screening procedure for ultrahigh dimensional data <https://www3.stat.sinica.edu.tw/statistica/J28N1/J28N114/J28N114.html>, and conducts conditional distance covariance test for conditional independence assumption of two multivariate variable.

This package provides a comprehensive API for colour conversion between popular colour spaces ('RGB', HSL', OKLab', OKLch', hex', and named colours) along with clean, modern CSS Color Level 4 syntax output. Integrates seamlessly into Shiny and Quarto workflows. Includes nearest colour name lookup powered by a curated database of over 30,000 colour names. OKLab'/'OKLCh colour spaces are described in Ottosson (2020) <https://bottosson.github.io/posts/oklab/>. CSS Color Level 4 syntax follows the W3C specification <https://www.w3.org/TR/css-color-4/>.

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 provides functions for computing and visualizing generalized canonical discriminant analyses and canonical correlation analysis for a multivariate linear model. Traditional canonical discriminant analysis is restricted to a one-way MANOVA design and is equivalent to canonical correlation analysis between a set of quantitative response variables and a set of dummy variables coded from the factor variable. The candisc package generalizes this to higher-way MANOVA designs for all factors in a multivariate linear model, computing canonical scores and vectors for each term. The graphic functions provide low-rank (1D, 2D, 3D) visualizations of terms in an mlm via the plot.candisc and heplot.candisc methods. Related plots are now provided for canonical correlation analysis when all predictors are quantitative. Methods for linear discriminant analysis are now included.

This package implements methods for querying data from CalPASS using its API. CalPASS Plus. MMAP API V1. <https://mmap.calpassplus.org/docs/index.html>.

Calculating crude sequence ratio, adjusted sequence ratio and confidence intervals using data mapped to the Observational Medical Outcomes Partnership Common Data Model.

CPP is a multiple criteria decision method to evaluate alternatives on complex decision making problems, by a probabilistic approach. The CPP was created and expanded by Sant'Anna, Annibal P. (2015) <doi:10.1007/978-3-319-11277-0>.

An implementation of the statistical methods commonly used for advanced composite materials in aerospace applications. This package focuses on calculating basis values (lower tolerance bounds) for material strength properties, as well as performing the associated diagnostic tests. This package provides functions for calculating basis values assuming several different distributions, as well as providing functions for non-parametric methods of computing basis values. Functions are also provided for testing the hypothesis that there is no difference between strength and modulus data from an alternate sample and that from a "qualification" or "baseline" sample. For a discussion of these statistical methods and their use, see the Composite Materials Handbook, Volume 1 (2012, ISBN: 978-0-7680-7811-4). Additional details about this package are available in the paper by Kloppenborg (2020, <doi:10.21105/joss.02265>).

Draws systematic samples from a population that follows linear trend. The function returns a matrix comprising of the required samples as its column vectors. The samples produced are highly efficient and the inter sampling variance is minimum. The scheme will be useful in various field like Bioinformatics where the samples are expensive and must be precise in reflecting the population by possessing least sampling variance.

This package implements controlled interrupted time series (CITS) analysis for evaluating interventions in comparative time-series data. The package provides tools for preparing panel time-series datasets, fitting models using generalized least squares (GLS) with optional autoregressiveâ moving-average (ARMA) error structures, and computing fitted values and robust standard errors using cluster-robust variance estimators (CR2). Visualization functions enable clear presentation of estimated effects and counterfactual trajectories following interventions. Background on methods for causal inference in interrupted time series can be found in Linden and Adams (2011) <doi:10.1111/j.1365-2753.2010.01504.x> and Lopez Bernal, Cummins, and Gasparrini (2018) <doi:10.1093/ije/dyy135>.

Connect and pull data from the CJA API, which powers CJA Workspace <https://github.com/AdobeDocs/cja-apis>. The package was developed with the analyst in mind and will continue to be developed with the guiding principles of iterative, repeatable, timely analysis. New features are actively being developed and we value your feedback and contribution to the process.

This package provides different datasets parsed from Drugbank <https://www.drugbank.ca/covid-19> database using dbparser package. It is a smaller version from dbdataset package. It contains only information about COVID-19 possible treatment.

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'.

An educational package providing intuitive functions for calculating confidence intervals (CI) for various statistical parameters. Designed primarily for teaching and learning about statistical inference (particularly confidence intervals). Offers user-friendly wrappers around established methods for proportions, means, and bootstrap-based intervals. Integrates seamlessly with Tidyverse workflows, making it ideal for classroom demonstrations and student exercises.

Calculate the colocalization index, NSInC, in two different ways as described in the paper (Liu et al., 2019. Manuscript submitted for publication.) for multiple-species spatial data which contain the precise locations and membership of each spatial point. The two main functions are nsinc.d() and nsinc.z(). They provide the Pearsonâ s correlation coefficients of signal proportions in different memberships within a concerned proximity of every signal (or every base signal if single direction colocalization is considered) across all (base) signals using two different ways of normalization. The proximity sizes could be an individual value or a range of values, where the default ranges of values are different for the two functions.

One degree of freedom contrasts for lm', glm', gls', and geese objects.