Interface to the sTiles framework for tile-based sparse Cholesky factorization: log-determinants, selected inverse (marginal variances) and triangular solves, with symbolic reuse so that repeated factorization of matrices sharing one sparsity pattern pays the ordering cost only once, as in a hyperparameter sweep. The compiled glue in this package resolves its symbols at run time against the sTiles solver library ('libstiles'), which is a separate component distributed under its own terms and is not part of this package. Install it once with sTiles_install_library()', or point the package at a copy you already have with the STILES_LIB environment variable.

This package provides a collection of tools for clinical trial data management and analysis in research and teaching. The package is mainly collected for personal use, but any use beyond that is encouraged. This package has migrated functions from agdamsbo/daDoctoR', and new functions has been added. Version follows months and year. See NEWS/Changelog for release notes. This package includes sampled data from the TALOS trial (Kraglund et al (2018) <doi:10.1161/STROKEAHA.117.020067>). The win_prob() function is based on work by Zou et al (2022) <doi:10.1161/STROKEAHA.121.037744>. The age_calc() function is based on work by Becker (2020) <doi:10.18637/jss.v093.i02>.

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>.

Allows users to calculate pairwise Nei's Genetic Distances (Nei 1972), pairwise Fixation Indexes (Fst) (Weir & Cockerham 1984) and also Genomic Relationship matrixes following Yang et al. (2010) in mixed and single ploidy populations. Bootstrapping across loci is implemented during Fst calculation to generate confidence intervals and p-values around pairwise Fst values. StAMPP utilises SNP genotype data of any ploidy level (with the ability to handle missing data) and is coded to utilise multithreading where available to allow efficient analysis of large datasets. StAMPP is able to handle genotype data from genlight objects allowing integration with other packages such adegenet. Please refer to LW Pembleton, NOI Cogan & JW Forster, 2013, Molecular Ecology Resources, 13(5), 946-952. <doi:10.1111/1755-0998.12129> for the appropriate citation and user manual. Thank you in advance.

This package performs receptor abundance estimation for single cell RNA-sequencing data using a supervised feature selection mechanism and a thresholded gene set scoring procedure. Seurat's normalization method is described in: Hao et al., (2021) <doi:10.1016/j.cell.2021.04.048>, Stuart et al., (2019) <doi:10.1016/j.cell.2019.05.031>, Butler et al., (2018) <doi:10.1038/nbt.4096> and Satija et al., (2015) <doi:10.1038/nbt.3192>. Method for reduced rank reconstruction and rank-k selection is detailed in: Javaid et al., (2022) <doi:10.1101/2022.10.08.511197>. Gene set scoring procedure is described in: Frost et al., (2020) <doi:10.1093/nar/gkaa582>. Clustering method is outlined in: Song et al., (2020) <doi:10.1093/bioinformatics/btaa613> and Wang et al., (2011) <doi:10.32614/RJ-2011-015>.

Fit Hawkes and log-Gaussian Cox process models with extensions. Introduced in Hawkes (1971) <doi:10.2307/2334319> a Hawkes process is a self-exciting temporal point process where the occurrence of an event immediately increases the chance of another. We extend this to consider self-inhibiting process and a non-homogeneous background rate. A log-Gaussian Cox process is a Poisson point process where the log-intensity is given by a Gaussian random field. We extend this to a joint likelihood formulation fitting a marked log-Gaussian Cox model. In addition, the package offers functionality to fit self-exciting spatiotemporal point processes. Models are fitted via maximum likelihood using TMB (Template Model Builder). Where included 1) random fields are assumed to be Gaussian and are integrated over using the Laplace approximation and 2) a stochastic partial differential equation model, introduced by Lindgren, Rue, and Lindström. (2011) <doi:10.1111/j.1467-9868.2011.00777.x>, is defined for the field(s).

Plots a QQ-Norm Plot with several Gaussian simulations.

This package provides data frames that hold certain columns and attributes persistently for data processing in dplyr'.

L2 penalized logistic regression for both continuous and discrete predictors, with forward stagewise/forward stepwise variable selection procedure.

This package provides functions to access Twitter's filter, sample, and user streams, and to parse the output into data frames.

Manages and display stellar tracks and isochrones from Pisa low-mass database. Includes tools for isochrones construction and tracks interpolation.

Loads and manages statistical genetics data objects including reference panels, genotypes, LD matrices, annotations, and summary statistics. Follows the statgen specification for use across Python', R', and MATLAB'/'Octave runtimes.

An Optimization Algorithm Applied to Stratification Problem.This function aims at constructing optimal strata with an optimization algorithm based on a global optimisation technique called Biased Random Key Genetic Algorithms.

Routines for computing different types of linear estimators, based on instrumental variables (IVs), including the semi-parametric Stein-like (SPS) estimator, originally introduced by Judge and Mittelhammer (2004) <DOI:10.1198/016214504000000430>.

Allow to identify motifs in spatial-time series. A motif is a previously unknown subsequence of a (spatial) time series with relevant number of occurrences. For this purpose, the Combined Series Approach (CSA) is used.

Given a coro asynchronous generator instance that produces text, write that text into a document selection in RStudio and Positron'. This is particularly helpful for streaming large language model responses into the user's editor.

This package implements the methodological developments found in Hermes (2025) <doi:10.48550/arXiv.2503.02786>, and allows for the statistical modeling of data consisting of multiple users that provide an ordinal rating for one or multiple items.

This package provides a minimal-dependency R client for OpenAI'-compatible speech-to-text APIs (see <https://developers.openai.com/api/reference/resources/audio>) with optional local fallbacks. Supports OpenAI', local servers, and the whisper package for local transcription.

This package contains three main functions including stddiff.numeric(), stddiff.binary() and stddiff.category(). These are used to calculate the standardized difference between two groups. It is especially used to evaluate the balance between two groups before and after propensity score matching.

Easily create alerts, notifications, modals, info tips and loading screens in Shiny'. Includes several options to customize alerts and notifications by including text, icons, images and buttons. When wrapped around a Shiny output, loading screen is automatically displayed while the output is being recalculated.

This package provides tools for the integration and exploration of data tables measured on the same set of observational units. The package includes methods to assess similarities among tables, extract common patterns across variable blocks, and create visual summaries that highlight shared structures in multiblock data.

Implementation of hybrid STL decomposition based time delay neural network model for univariate time series forecasting. For method details see Jha G K, Sinha, K (2014). <doi:10.1007/s00521-012-1264-z>, Xiong T, Li C, Bao Y (2018). <doi:10.1016/j.neucom.2017.11.053>.

Get started with new projects by dropping a skeleton of a new project into a new or existing directory, initialise git repositories, and create reproducible environments with the renv package. The package allows for dynamically named files, folders, file content, as well as the functionality to drop individual template files into existing projects.

Adds support for R startup configuration via .Renviron.d and .Rprofile.d directories in addition to .Renviron and .Rprofile files. This makes it possible to keep private / secret environment variables separate from other environment variables. It also makes it easier to share specific startup settings by simply copying a file to a directory.