This package provides a collection of functions for computing "r-values" from various kinds of user input such as MCMC output or a list of effect size estimates and associated standard errors. Given a large collection of measurement units, the r-value, r, of a particular unit is a reported percentile that may be interpreted as the smallest percentile at which the unit should be placed in the top r-fraction of units.

Estimate significance of importance metrics for a Random Forest model by permuting the response variable. Produces null distribution of importance metrics for each predictor variable and p-value of observed. Provides summary and visualization functions for randomForest results.

We provide a toolbox to fit univariate and multivariate linear mixed models via data transforming augmentation. Users can also fit these models via typical data augmentation for a comparison. It returns either maximum likelihood estimates of unknown model parameters (hyper-parameters) via an EM algorithm or posterior samples of those parameters via MCMC. Also see Tak et al. (2019) <doi:10.1080/10618600.2019.1704295>.

Read, write and manipulate Praat TextGrid, PitchTier, Pitch, IntensityTier, Formant, Sound, and Collection files <https://www.fon.hum.uva.nl/praat/>.

This package provides an efficient procedure for fitting the entire solution path for high-dimensional regularized quadratic generalized linear models with interactions effects under the strong or weak heredity constraint.

Audits R scripts for behavioural reproducibility risk. Scans scripts for qualified package::function calls and checks them against a curated database of known silent breaking changes across popular CRAN packages. Flags stochastic calls lacking set.seed() and detects locale-sensitive operations that may produce different results across systems. Supports baseline certification of analytical outputs so that silent numerical drift can be detected across package upgrades or platform changes. Generates human-readable audit reports suitable for academic submission or pharmaceutical QC workflows. For more details see <https://github.com/repro-stats/reproducr>.

This package provides popular sampling distributions C++ routines based in armadillo through a header file approach.

R interface for calling OpenCV routines that works by translating R objects to OpenCV classes and back. Low-level wrappers for several OpenCV routines are provided as Rcpp modules. In addition, high level interfaces are provided for a limited selection of common operations.

The goal of Rigma is to provide a user friendly client to the Figma API <https://www.figma.com/developers/api>. It uses the latest `httr2` for a stable interface with the REST API. More than 20 methods are provided to interact with Figma files, and teams. Get design data into R by reading published components and styles, converting and downloading images, getting access to the full Figma file as a hierarchical data structure, and much more. Enhance your creativity and streamline the application development by automating the extraction, transformation, and loading of design data to your applications and HTML documents.

This package provides a general routine, envMU, which allows estimation of the M envelope of span(U) given root n consistent estimators of M and U. The routine envMU does not presume a model. This package implements response envelopes, partial response envelopes, envelopes in the predictor space, heteroscedastic envelopes, simultaneous envelopes, scaled response envelopes, scaled envelopes in the predictor space, groupwise envelopes, weighted envelopes, envelopes in logistic regression, envelopes in Poisson regression envelopes in function-on-function linear regression, envelope-based Partial Partial Least Squares, envelopes with non-constant error covariance, envelopes with t-distributed errors, reduced rank envelopes and reduced rank envelopes with non-constant error covariance. For each of these model-based routines the package provides inference tools including bootstrap, cross validation, estimation and prediction, hypothesis testing on coefficients are included except for weighted envelopes. Tools for selection of dimension include AIC, BIC and likelihood ratio testing. Background is available at Cook, R. D., Forzani, L. and Su, Z. (2016) <doi:10.1016/j.jmva.2016.05.006>. Optimization is based on a clockwise coordinate descent algorithm.

Spatial Dispersion Index (SDI) is a generalized measurement index, or rather a family of indices to evaluate spatial dispersion of movements/flows in a network in a problem neutral way as described in: Gencer (2023) <doi:10.1007/s12061-023-09545-8>. This package computes and optionally visualizes this index with minimal hassle.

Designed for longitudinal data analysis using Hidden Markov Models (HMMs). Tailored for applications in healthcare, social sciences, and economics, the main emphasis of this package is on regularization techniques for fitting HMMs. Additionally, it provides an implementation for fitting HMMs without regularization, referencing Zucchini et al. (2017, ISBN:9781315372488).

Streamlined statistical reporting in Rmarkdown environments. Facilitates the automated reporting of descriptive statistics, multiple univariate models, multivariable models and tables combining these outputs. Plotting functions include customisable survival curves, forest plots from logistic and ordinal regression and bivariate comparison plots.

Download and plot Open Street Map <https://www.openstreetmap.org/>, Bing Maps <https://www.bing.com/maps> and other tiled map sources. Use to create basemaps quickly and add hillshade to vector-based maps.

This package provides a parallel function for multivariate outlier detection named modified Stahel-Donoho estimators is contained in this package. The function RMSDp() is for elliptically distributed datasets and recognizes outliers based on Mahalanobis distance. This function is for higher dimensional datasets that cannot be handled by a single core function RMSD() included in RMSD package. See Wada and Tsubaki (2013) <doi:10.1109/CLOUDCOM-ASIA.2013.86> for the detail of the algorithm.

An extremely simple stack data type, implemented with R6 classes. The size of the stack increases as needed, and the amortized time complexity is O(1). The stack may contain arbitrary objects.

Function for generating random gender and ethnicity correct first and/or last names. Names are chosen proportionally based upon their probability of appearing in a large scale data base of real names.

Utilities for accessing RePEc (Research Papers in Economics) through a RESTful API. You can request a code and get detailed information at the following page: <https://ideas.repec.org/api.html>.

Assists in statistical model building to find optimal and semi-optimal higher order interactions and best subsets. Uses the lm(), glm(), and other R functions to fit models generated from a feasible solution algorithm. Discussed in Subset Selection in Regression, A Miller (2002). Applied and explained for least median of squares in Hawkins (1993) <doi:10.1016/0167-9473(93)90246-P>. The feasible solution algorithm comes up with model forms of a specific type that can have fixed variables, higher order interactions and their lower order terms.

Build regular expressions piece by piece using human readable code. This package contains core functionality, and is primarily intended to be used by package developers.

Base S4-classes and functions for robust asymptotic statistics.

Calculate representativity indicators for survey quality based on survey data and response models. Use partial indicators to analyse the impact of individual variables and categories. Monitor changes in representativity during data collection. Improve representativity through adaptive survey design. Supports both R-indicators and coefficients of variation. See also Schouten, Cobben, Bethlehem (2009) <https://api.semanticscholar.org/CorpusID:33654901>, Shlomo, Skinner, Schouten (2012) <doi:10.1016/j.jspi.2011.07.008> and Schouten, Shlomo (2017) <doi:10.1111/insr.12159>.

Wrapper for the RSpace Electronic Lab Notebook (<https://www.researchspace.com/>) API. This packages provides convenience functions to browse, search, create, and edit your RSpace documents. In addition, it enables filling RSpace templates from R Markdown/Quarto templates or tabular data (e.g., Excel files). This R package is not developed or endorsed by Research Space'.

The function RepaymentPlan() calculates repayment schedule for repayment/mortgage plans.