Algorithms for robust multivariate statistics (STochAstic Robust multivaRiate Statistics) including geometric median and geometric median covariance computation, k-medians clustering and robust median Principal Compenents Analysis (PCA), robust estimation of parameters for Gaussian, Student, or Laplace mixture models. STARRS provides an independent, clean, consolidated and cohesive framework, while drawing inspiration from the approaches implemented in packages Gmedian', Kmedians', RGMM', RobRegression'. Methods used in the package refer to H. Robbins, S. Monro (1951) <doi:10.1214/aoms/1177729586>; D. Kraus, V. M. Panaretos (2012) <doi:10.1093/biomet/ass037>; H. Cardot, A. Godichon-Baggioni (2015) <doi:10.48550/arXiv.1504.02852>; A. Godichon-Baggioni, S. Robin (2024) <doi:10.1007/s11222-023-10362-9>.

Univariate and multivariate normal data simulation. They also supply a brief summary of the analysis for each experiment/design: - Independent samples. - One-way and two-way Anova. - Paired samples (T-Test & Regression). - Repeated measures (Anova & Multiple Regression). - Clinical Assay.

This package implements shrinkage principal fitted components for sufficient dimension reduction in high-dimensional regression and classification. Provides regularised covariance estimation using Oracle Approximating Shrinkage and Maximum Entropy Covariance, structural-dimension selection using conventional and information-complexity criteria, response-guided feature screening, reduced-space prediction, and simulation utilities. Methodological foundations include Cook and Forzani (2008) <doi:10.1214/08-STS275>, Chen et al. (2010) <doi:10.1109/TSP.2010.2053029>, Bozdogan (2000) <doi:10.1006/jmps.1999.1277>, and Olorede and Yahya (2019) <doi:10.48550/arXiv.1909.13017>.

Nonparametric estimation of Spearman's rank correlation with bivariate survival (right-censored) data as described in Eden, S.K., Li, C., Shepherd B.E. (2021), Nonparametric Estimation of Spearman's Rank Correlation with Bivariate Survival Data, Biometrics (under revision). The package also provides functions that visualize bivariate survival data and bivariate probability mass function.

Implementation of SING algorithm to extract joint and individual non-Gaussian components from two datasets. SING uses an objective function that maximizes the skewness and kurtosis of latent components with a penalty to enhance the similarity between subject scores. Unlike other existing methods, SING does not use PCA for dimension reduction, but rather uses non-Gaussianity, which can improve feature extraction. Benjamin B.Risk, Irina Gaynanova (2021) <doi:10.1214/21-AOAS1466>.

Computes the extended spring indices (SI-x) and false spring exposure indices (FSEI). The SI-x indices are standard indices used for analysis in spring phenology studies. In addition, the FSEI is also from research on the climatology of false springs and adjusted to include an early and late false spring exposure index. The indices include the first leaf index, first bloom index, and false spring exposure indices, along with all calculations for all functions needed to calculate each index. The main function returns all indices, but each function can also be run separately. Allstadt et al. (2015) <doi: 10.1088/1748-9326/10/10/104008> Ault et al. (2015) <doi: 10.1016/j.cageo.2015.06.015> Peterson and Abatzoglou (2014) <doi: 10.1002/2014GL059266> Schwarz et al. (2006) <doi: 10.1111/j.1365-2486.2005.01097.x> Schwarz et al. (2013) <doi: 10.1002/joc.3625>.

Create short sprint acceleration-velocity (AVP) and force-velocity (FVP) profiles and predict kinematic and kinetic variables using the timing-gate split times, laser or radar gun data, tether devices data, as well as the data provided by the GPS and LPS monitoring systems. The modeling method utilized in this package is based on the works of Furusawa K, Hill AV, Parkinson JL (1927) <doi: 10.1098/rspb.1927.0035>, Greene PR. (1986) <doi: 10.1016/0025-5564(86)90063-5>, Chelly SM, Denis C. (2001) <doi: 10.1097/00005768-200102000-00024>, Clark KP, Rieger RH, Bruno RF, Stearne DJ. (2017) <doi: 10.1519/JSC.0000000000002081>, Samozino P. (2018) <doi: 10.1007/978-3-319-05633-3_11>, Samozino P. and Peyrot N., et al (2022) <doi: 10.1111/sms.14097>, Clavel, P., et al (2023) <doi: 10.1016/j.jbiomech.2023.111602>, Jovanovic M. (2023) <doi: 10.1080/10255842.2023.2170713>, and Jovanovic M., et al (2024) <doi: 10.3390/s24092894>.

For biparental, three and four-way crosses Identity by Descent (IBD) probabilities can be calculated using Hidden Markov Models and inheritance vectors following Lander and Green (<https://www.jstor.org/stable/29713>) and Huang (<doi:10.1073/pnas.1100465108>). One of a series of statistical genetic packages for streamlining the analysis of typical plant breeding experiments developed by Biometris.

Simulation of event histories with possibly non-linear baseline hazard rate functions, non-linear (time-varying) covariate effect functions, and dependencies on the past of the history. Random generation of event histories is performed using inversion sampling on the cumulative all-cause hazard rate functions.

In stability selection (N Meinshausen, P Bühlmann (2010) <doi:10.1111/j.1467-9868.2010.00740.x>) and consensus clustering (S Monti et al (2003) <doi:10.1023/A:1023949509487>), resampling techniques are used to enhance the reliability of the results. In this package (B Bodinier et al (2025) <doi:10.18637/jss.v112.i05>), hyper-parameters are calibrated by maximising model stability, which is measured under the null hypothesis that all selection (or co-membership) probabilities are identical (B Bodinier et al (2023a) <doi:10.1093/jrsssc/qlad058> and B Bodinier et al (2023b) <doi:10.1093/bioinformatics/btad635>). Functions are readily implemented for the use of LASSO regression, sparse PCA, sparse (group) PLS or graphical LASSO in stability selection, and hierarchical clustering, partitioning around medoids, K means or Gaussian mixture models in consensus clustering.

Easily integrate and control Lottie animations within shiny applications', without the need for idiosyncratic expression or use of JavaScript'. This includes utilities for generating animation instances, controlling playback, manipulating animation properties, and more. For more information on Lottie', see: <https://airbnb.io/lottie/#/>. Additionally, see the official Lottie GitHub repository at <https://github.com/airbnb/lottie>.

Basic functions for dealing with wav files and sound samples.

This package implements the Savvy Parity Regression savvyPR methodology for multivariate linear regression analysis. The package solves an optimization problem that balances the contribution of each predictor variable to ensure estimation stability in the presence of multicollinearity. It supports two distinct parameterization methods, a Budget-based approach that allocates a fixed loss contribution to each predictor, and a Target-based approach (t-tuning) that utilizes a relative elasticity weight for the response variable. The package provides comprehensive tools for model estimation, risk distribution analysis, and parameter tuning via cross-validation (PR1, PR2, and PR3 model types) to optimize predictive accuracy. Methods are based on Asimit, Chen, Ichim and Millossovich (2026) <https://openaccess.city.ac.uk/id/eprint/37017/>.

Simultaneous/joint diagonalization of local autocovariance matrices to estimate spatio-temporally uncorrelated random fields.

Computation of second-generation p-values as described in Blume et al. (2018) <doi:10.1371/journal.pone.0188299> and Blume et al. (2019) <doi:10.1080/00031305.2018.1537893>. There are additional functions which provide power and type I error calculations, create graphs (particularly suited for large-scale inference usage), and a function to estimate false discovery rates based on second-generation p-value inference.

Cellular population mapping (CPM) a deconvolution algorithm in which single-cell genomics is required in only one or a few samples, where in other samples of the same tissue, only bulk genomics is measured and the underlying fine resolution cellular heterogeneity is inferred.

Setaria viridis (green foxtail) is a common weed. This package contains measurements from individual branches of a wild Setaria viridis plant collected near the author's home. The data is intended for use in data analysis practice.

Utility functions for scale-dependent and alternative hyperpriors. The distribution parameters may capture location, scale, shape, etc. and every parameter may depend on complex additive terms (fixed, random, smooth, spatial, etc.) similar to a generalized additive model. Hyperpriors for all effects can be elicitated within the package. Including complex tensor product interaction terms and variable selection priors. The basic model is explained in in Klein and Kneib (2016) <doi:10.1214/15-BA983>.

Simplicially constrained regression models for proportions in both sides. The constraint is always that the betas are non-negative and sum to 1. References: Iverson S.J.., Field C., Bowen W.D. and Blanchard W. (2004) "Quantitative Fatty Acid Signature Analysis: A New Method of Estimating Predator Diets". Ecological Monographs, 74(2): 211-235. <doi:10.1890/02-4105>.

The stress addition approach is an alternative to the traditional concentration addition or effect addition models. It allows the modelling of tri-phasic concentration-response relationships either as single toxicant experiments, in combination with an environmental stressor or as mixtures of two toxicants. See Liess et al. (2019) <doi:10.1038/s41598-019-51645-4> and Liess et al. (2020) <doi:10.1186/s12302-020-00394-7>.

Offers a suite of functions for converting to and from (atomic) vectors, matrices, data.frames, and (3D+) arrays as well as lists of these objects. It is an alternative to the base R as.<str>.<method>() functions (e.g., as.data.frame.array()) that provides more useful and/or flexible restructuring of R objects. To do so, it only works with common structuring of R objects (e.g., data.frames with only atomic vector columns).

This package provides nonparametric Steinian shrinkage estimators of the covariance matrix that are suitable in high dimensional settings, that is when the number of variables is larger than the sample size.

This package provides a statistical learning method to simultaneously predict a range of target phenotypes using codified and natural language processing (NLP)-derived Electronic Health Record (EHR) data. See Ahuja et al (2020) JAMIA <doi:10.1093/jamia/ocaa079> for details.

This package provides drop-in Liquid Glass themes for shiny'. Call glass_theme() and pass the result as theme = to fluidPage(), navbarPage(), or any bslib'-aware page function to get translucent surfaces, backdrop blur, and system typography on Bootstrap components. Includes light and dark presets with runtime switching and an OS-following auto mode, an iOS-style intensity control from Ultra Clear to Tinted, optional persistence of the look, named wallpaper scenes, helpers to match ggplot2', plotly', gt', and DT output to the glass pack, a flatten mode for print and screenshots, and documented CSS tokens.