The FastRCS algorithm of Vakili and Schmitt (2014) for robust fit of the multivariable linear regression model and outliers detection.

This package provides a set of functions that facilitate basic data manipulation and cleaning for statistical analysis including functions for finding and fixing duplicate rows and columns, missing values, outliers, and special characters in column and row names and functions for checking data consistency, distribution, quality, reliability, and structure.

This package provides a friendly interface for modifying data frames with a sequence of piped commands built upon the tidyverse Wickham et al., (2019) <doi:10.21105/joss.01686> . The majority of commands wrap dplyr mutate statements in a convenient way to concisely solve common issues that arise when tidying small to medium data sets. Includes smart defaults and allows flexible selection of columns via tidyselect'.

This is a package for implementation of Flury-Gautschi algorithms.

Lightweight utilities to estimate autoregressive (AR) and autoregressive moving average (ARMA) noise models from residuals and apply matched generalized least squares to whiten functional magnetic resonance imaging (fMRI) design and data matrices. The ARMA estimator follows a classic 1982 approach <doi:10.1093/biomet/69.1.81>, and a restricted AR family mirrors workflows described by Cox (2012) <doi:10.1016/j.neuroimage.2011.08.056>.

All data sets required for the examples and exercises in the book "Forecasting: principles and practice" by Rob J Hyndman and George Athanasopoulos <https://OTexts.com/fpp3/>. All packages required to run the examples are also loaded. Additional data sets not used in the book are also included.

This package provides a pipeline for matching messy company name strings against a clean dictionary (e.g., Orbis'). Implements a cascading strategy: Exact -> Fuzzy ('zoomerjoin') -> FTS5 ('SQLite') -> Rarity Weighted. References: Beniamino Green (2025) <https://beniamino.org/zoomerjoin/>; <https://www.sqlite.org/fts5.html>.

This package provides a research estimation tool for analysts that work with sample-based inventory data from the U.S. Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program.

This package provides a collection of functions designed to retrieve, filter and spatialize data from the Flora e Funga do Brasil dataset. For more information about the dataset, please visit <https://floradobrasil.jbrj.gov.br/consulta/>.

The functions provided in the FADA (Factor Adjusted Discriminant Analysis) package aim at performing supervised classification of high-dimensional and correlated profiles. The procedure combines a decorrelation step based on a factor modeling of the dependence among covariates and a classification method. The available methods are Lasso regularized logistic model (see Friedman et al. (2010)), sparse linear discriminant analysis (see Clemmensen et al. (2011)), shrinkage linear and diagonal discriminant analysis (see M. Ahdesmaki et al. (2010)). More methods of classification can be used on the decorrelated data provided by the package FADA.

The futurize() function transpiles calls to 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 function with R's native pipe operator, you have an 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 be "futurized" are BiocParallel', plyr', crossmap packages. There is also support for growing set of domain-specific packages, including boot', glmnet', mgcv', lme4', and tm'.

We facilitate the analysis of full factorial mating designs with mixed-effects models. The package contains six vignettes containing detailed examples.

Fuzzy clustering of species in an ecological community as common or rare based on their abundance and occupancy. It also includes functions to compute confidence intervals of classification metrics and plot results. See Balbuena et al. (2020, <doi:10.1101/2020.08.12.247502>).

Uses three different correlation coefficients to calculate measurement-level adequate correlations in a feature matrix: Pearson product-moment correlation coefficient, Intraclass correlation and Cramer's V.

This package provides a comprehensive Shiny-based graphical user interface for conducting a wide range of factor analysis procedures. FAfA (Factor Analysis for All) guides users through data uploading, assumption checking (descriptives, collinearity, multivariate normality, outliers), data wrangling (variable exclusion, data splitting), factor retention analysis (e.g., Parallel Analysis, Hull method, EGA), Exploratory Factor Analysis (EFA) with various rotation and extraction methods, Confirmatory Factor Analysis (CFA) for model testing, Reliability Analysis (e.g., Cronbach's Alpha, McDonald's Omega), Measurement Invariance testing across groups, and item weighting techniques. The application leverages established R packages such as lavaan and psych to perform these analyses, offering an accessible platform for researchers and students. Results are presented in user-friendly tables and plots, with options for downloading outputs.

This package provides a flexible framework for post-processing thermal dissipation sap flow data using statistical methods and machine learning. This framework includes anomaly correction, outlier removal, gap-filling, trend removal, signal damping correction, and sap flux density calculation. The functions in this package can also apply to other time series with various artifacts.

Open-source package for computing likelihood ratios in kinship testing and human identification cases. It has the core function of the software GENis, developed by Fundación Sadosky. It relies on a Bayesian Networks framework and is particularly well suited to efficiently perform large-size queries against databases of missing individuals.

This package provides the function feis() to estimate fixed effects individual slope (FEIS) models. The FEIS model constitutes a more general version of the often-used fixed effects (FE) panel model, as implemented in the package plm by Croissant and Millo (2008) <doi:10.18637/jss.v027.i02>. In FEIS models, data are not only person demeaned like in conventional FE models, but detrended by the predicted individual slope of each person or group. Estimation is performed by applying least squares lm() to the transformed data. For more details on FEIS models see Bruederl and Ludwig (2015, ISBN:1446252442); Frees (2001) <doi:10.2307/3316008>; Polachek and Kim (1994) <doi:10.1016/0304-4076(94)90075-2>; Ruettenauer and Ludwig (2020) <doi:10.1177/0049124120926211>; Wooldridge (2010, ISBN:0262294354). To test consistency of conventional FE and random effects estimators against heterogeneous slopes, the package also provides the functions feistest() for an artificial regression test and bsfeistest() for a bootstrapped version of the Hausman test.

Perform optimal transport based tests in factorial designs as introduced in Groppe et al. (2025) <doi:10.48550/arXiv.2509.13970> via the FDOTT() function. These tests are inspired by ANOVA and its nonparametric counterparts. They allow for testing linear relationships in factorial designs between finitely supported probability measures on a metric space. Such relationships include equality of all measures (no treatment effect), interaction effects between a number of factors, as well as main and simple factor effects.

Application of the filtered monotonic polynomial (FMP) item response model to flexibly fit item response models. The package includes tools that allow the item response model to be build on any monotonic transformation of the latent trait metric, as described by Feuerstahler (2019) <doi:10.1007/s11336-018-9642-9>.

This package provides a collection of functions to manage, to investigate and to analyze data sets of financial assets from different points of view.

Opens a shiny app which supports theoretical and computational analysis of block designs for symmetrical and mixed level factorial experiments. This package includes tools to check whether a design has orthogonal factorial structure (OFS) with balance or not and is able to find the orthogonality deviation value if not having OFS. This package includes function to evaluate efficiency factor of all factorial effects in two situations, in the first situation if the design is verified with OFS and balance then calculate the efficiencies of all factorial effects using a specific analytical procedure and in the second situation if the design is verified with non-OFS and balance then a new general method has been developed and used to calculate efficiencies under the condition that the design should be proper and equi-replicated, See Gupta, S.C. and Mukerjee, R. (1987): "A Calculus for factorial arrangements". Lecture Notes in Statistics. No. 59, Springer-Verlag, Berlin, New York, <doi:10.1007/978-1-4419-8730-3>. For the easy use of package, shiny app is used for giving inputs and inputs validation.

All data sets required for the examples and exercises in the book "Forecasting: principles and practice" (2nd ed, 2018) by Rob J Hyndman and George Athanasopoulos <https://otexts.com/fpp2/>. All packages required to run the examples are also loaded.

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