Perform robust inference based on applying Fast and Robust Bootstrap on robust estimators (Van Aelst and Willems (2013) <doi:10.18637/jss.v053.i03>). This method constitutes an alternative to ordinary bootstrap or asymptotic inference. procedures when using robust estimators such as S-, MM- or GS-estimators. The available methods are multivariate regression, principal component analysis and one-sample and two-sample Hotelling tests. It provides both the robust point estimates and uncertainty measures based on the fast and robust bootstrap.

With no external dependencies and support for 335 languages; all languages spoken by more than one million speakers. Franc is a port of the JavaScript project of the same name, see <https://github.com/wooorm/franc>.

Contingency Tables are a pain to work with when you want to run regressions. This package takes them, flattens them into a long data frame, so you can more easily analyse them! As well, you can calculate other related statistics. All of this is done so in a tidy manner, so it should tie in nicely with tidyverse series of packages.

Fit (generalized) linear regression models in each leaf node of a tree. The tree is constructed using clinical variables only. The linear regression models are constructed using (high-dimensional) omics variables only. The leaf-node-specific regression models are estimated using the penalized likelihood including a standard ridge (L2) penalty and a fusion penalty that links the leaf-node-specific regression models to one another. The intercepts of the leaf nodes reflect the effects of the clinical variables and are left unpenalized. The tree, fitted with the clinical variables only, should be constructed outside of the package with the rpart R package. See Goedhart and others (2024) <doi:10.48550/arXiv.2411.02396> for details on the method.

The Occluded Surface (OS) algorithm is a widely used approach for analyzing atomic packing in biomolecules as described by Pattabiraman N, Ward KB, Fleming PJ (1995) <doi:10.1002/jmr.300080603>. Here, we introduce fibos', an R and Python package that extends the OS methodology, as presented in Soares HHM, Romanelli JPR, Fleming PJ, da Silveira CH (2024) <doi:10.1101/2024.11.01.621530>.

Flexible parametric mixture and non-mixture cure models for time-to-event data.

Fast and numerically stable estimation of a covariance matrix by banding the Cholesky factor using a modified Gram-Schmidt algorithm implemented in RcppArmadilo. See <http://stat.umn.edu/~molst029> for details on the algorithm.

This package provides tools for training and analysing fairness-aware gated neural networks for subgroup-aware prediction and interpretation in clinical datasets. Methods draw on prior work in mixture-of-experts neural networks by Jordan and Jacobs (1994) <doi:10.1007/978-1-4471-2097-1_113>, fairness-aware learning by Hardt, Price, and Srebro (2016) <doi:10.48550/arXiv.1610.02413>, and personalised treatment prediction for depression by Iniesta, Stahl, and McGuffin (2016) <doi:10.1016/j.jpsychires.2016.03.016>.

This package provides an alternative to facilitate the construction of a phylogeny for fish species from a list of species or a community matrix using as a backbone the phylogenetic tree proposed by Rabosky et al. (2018) <doi:10.1038/s41586-018-0273-1>.

Similar to base's unique function, only optimized for working with data frames, especially those that contain date-time columns.

This package provides tools for analyzing remote sensing forest data, including functions for detecting treetops from canopy models, outlining tree crowns, and calculating textural metrics.

Distribution functions and test for over-representation of short distances in the Liland distribution. Simulation functions are included for comparison.

Supports the use of standardized folder names.

Implementation of the Future API <doi:10.32614/RJ-2021-048> on top of the batchtools package. This allows you to process futures, as defined by the future package, in parallel out of the box, not only on your local machine or ad-hoc cluster of machines, but also via high-performance compute ('HPC') job schedulers such as LSF', OpenLava', Slurm', SGE', and TORQUE / PBS', e.g. y <- future.apply::future_lapply(files, FUN = process)'.

Screens daily streamflow time series for temporal trends and change-points. This package has been primarily developed for assessing the quality of daily streamflow time series. It also contains tools for plotting and calculating many different streamflow metrics. The package can be used to produce summary screening plots showing change-points and significant temporal trends for high flow, low flow, and/or baseflow statistics, or it can be used to perform more detailed hydrological time series analyses. The package was designed for screening daily streamflow time series from Water Survey Canada and the United States Geological Survey but will also work with streamflow time series from many other agencies. Package update to version 2.0 made updates to read.flows function to allow loading of GRDC and ROBIN streamflow record formats. This package uses the `changepoint` package for change point detection. For more information on change point methods, see the changepoint package at <https://cran.r-project.org/package=changepoint>.

Create fake datasets that can be used for prototyping and teaching. This package provides a set of functions to generate fake data for a variety of data types, such as dates, addresses, and names. It can be used for prototyping (notably in shiny') or as a tool to teach data manipulation and data visualization.

The main function of this package allows numerical vector objects to be displayed with their values in vulgar fractional form. This is convenient if patterns can then be more easily detected. In some cases replacing the components of a numeric vector by a rational approximation can also be expected to remove some component of round-off error. The main functions form a re-implementation of the functions fractions and rational of the MASS package, but using a radically improved programming strategy.

The futurize() function turns sequential map-reduce functions such as base::lapply(), purrr::map(), foreach::foreach() %do% ... into concurrent alternatives, providing you with a simple, straightforward path to scalable parallel computing via the future ecosystem <doi:10.32614/RJ-2021-048>. By combining this transpiler function with R's native pipe operator, you have a convenient way for speeding up iterative computations with minimal refactoring, e.g. lapply(xs, fcn) |> futurize()', purrr::map(xs, fcn) |> futurize()', and foreach::foreach(x = xs) %do% fcn(x) |> futurize()'. Other map-reduce packages that can be "futurized" are BiocParallel', plyr', crossmap', pbapply packages. There is also support for a growing set of domain-specific packages on CRAN (e.g. boot', caret', DiceKriging', ez', fgsea', fwb', gamlss', glmmTMB', glmnet', kernelshap', lme4', metafor', mgcv', modelsummary', parameters', partykit', pls', pvclust', riskRegression', rugarch', sandwich', seriation', shapr', Sim.DiffProc', SimDesign', stars', strucchange', SuperLearner', tm', TSP', and vegan') and on Bioconductor (e.g. DESeq2', GenomicAlignments', GSVA', Rsamtools', scater', scuttle', SingleCellExperiment', and sva').

Spatio-temporal locations of an animal are computed from annotated data with a hidden Markov model via particle filter algorithm. The package is relatively robust to varying degrees of shading. The hidden Markov model is described in Movement Ecology - Rakhimberdiev et al. (2015) <doi:10.1186/s40462-015-0062-5>, general package description is in the Methods in Ecology and Evolution - Rakhimberdiev et al. (2017) <doi:10.1111/2041-210X.12765> and package accuracy assessed in the Journal of Avian Biology - Rakhimberdiev et al. (2016) <doi:10.1111/jav.00891>.

Estimates and provides inference for quantities that assess high dimensional mediation and potential surrogate markers including the direct effect of treatment, indirect effect of treatment, and the proportion of treatment effect explained by a surrogate/mediator; details are described in Zhou et al (2022) <doi:10.1002/sim.9352> and Zhou et al (2020) <doi:10.1093/biomet/asaa016>. This package relies on the optimization software MOSEK', <https://www.mosek.com>.

Converts large Danish register files ('sas7bdat') into Parquet format with year-based Hive partitioning and chunked reading for larger-than-memory files. Supports parallel conversion with a targets pipeline and reading those registers into DuckDB tables for faster querying and analyses.

Tabacchi et al. (2011) published a very detailed study producing a uniform system of functions to estimate tree volume and phytomass components (stem, branches, stool). The estimates of the 2005 Italian forest inventory (<https://www.inventarioforestale.org/it/>) are based on these functions. The study documents the domain of applicability of each function and the equations to quantify estimates accuracies for individual estimates as well as for aggregated estimates. This package makes the functions available in the R environment. Version 2 exposes two distinct functions for individual and summary estimates. To facilitate access to the functions, tree species identification is now based on EPPO species codes (<https://data.eppo.int/>).

Using the idea of least trimmed square, it could automatically detects and removes outliers from data before estimating the coefficients. It is a robust machine learning tool which can be applied to gene-expression deconvolution technique. Yuning Hao, Ming Yan, Blake R. Heath, Yu L. Lei and Yuying Xie (2019) <doi:10.1101/358366>.

This package provides a utility to scrape and load play-by-play data and statistics from the Premier Hockey Federation (PHF) <https://www.premierhockeyfederation.com/>, formerly known as the National Women's Hockey League (NWHL). Additionally, allows access to the National Hockey League's stats API <https://www.nhl.com/>.