Unified regularized estimating equation solver. Currently the package includes one solver with the l1 penalty only. More solvers and penalties are under development. Reference: Yi Yang, Yuwen Gu, Yue Zhao, Jun Fan (2021) <doi:10.48550/arXiv.2110.11074>.

Generating fractional binomial random variables and computing density, cumulative distribution, and quantiles of fractional binomial distributions. (Lee, J. (2023) <arXiv:2209.01516>.).

Probabilistic distance clustering (PD-clustering) is an iterative, distribution-free, probabilistic clustering method. PD-clustering assigns units to a cluster according to their probability of membership under the constraint that the product of the probability and the distance of each point to any cluster center is a constant. PD-clustering is a flexible method that can be used with elliptical clusters, outliers, or noisy data. PDQ is an extension of the algorithm for clusters of different sizes. GPDC and TPDC use a dissimilarity measure based on densities. Factor PD-clustering (FPDC) is a factor clustering method that involves a linear transformation of variables and a cluster optimizing the PD-clustering criterion. It works on high-dimensional data sets.

This package provides functions for plotting probability density functions, distribution functions, survival functions, hazard functions and computing distribution moments. The implementation is inspired by Delignette-Muller and Dutang (2015) <doi:10.18637/jss.v064.i04>.

Easily analyze relational data from the United States 2016 federal election cycle as reported by the Federal Election Commission. This package contains data about candidates, committees, and a variety of different financial expenditures. Data is from <https://www.fec.gov/data/browse-data/?tab=bulk-data>.

Functional data analysis tools with a high-performance Rust backend. Provides methods for functional data manipulation, depth computation, distance metrics, regression, and statistical testing. Supports both 1D functional data (curves) and 2D functional data (surfaces). Methods are described in Ramsay and Silverman (2005, ISBN:978-0-387-40080-8) "Functional Data Analysis" and Ferraty and Vieu (2006, ISBN:978-0-387-30369-7) "Nonparametric Functional Data Analysis".

This package provides algorithms to fit linear regression models under several popular penalization techniques and functional linear regression models based on Majorizing-Minimizing (MM) and Alternating Direction Method of Multipliers (ADMM) techniques. See Boyd et al (2010) <doi:10.1561/2200000016> for complete introduction to the method.

Defines a collection of functions to compute average power and sample size for studies that use the false discovery rate as the final measure of statistical significance. A three-rectangle approximation method of a p-value histogram is proposed to derive a formula to compute the statistical power for analyses that involve the FDR. The methodology paper of this package is under review.

Computes fungible coefficients and Monte Carlo data. Underlying theory for these functions is described in the following publications: Waller, N. (2008). Fungible Weights in Multiple Regression. Psychometrika, 73(4), 691-703, <DOI:10.1007/s11336-008-9066-z>. Waller, N. & Jones, J. (2009). Locating the Extrema of Fungible Regression Weights. Psychometrika, 74(4), 589-602, <DOI:10.1007/s11336-008-9087-7>. Waller, N. G. (2016). Fungible Correlation Matrices: A Method for Generating Nonsingular, Singular, and Improper Correlation Matrices for Monte Carlo Research. Multivariate Behavioral Research, 51(4), 554-568. Jones, J. A. & Waller, N. G. (2015). The normal-theory and asymptotic distribution-free (ADF) covariance matrix of standardized regression coefficients: theoretical extensions and finite sample behavior. Psychometrika, 80, 365-378, <DOI:10.1007/s11336-013-9380-y>. Waller, N. G. (2018). Direct Schmid-Leiman transformations and rank-deficient loadings matrices. Psychometrika, 83, 858-870. <DOI:10.1007/s11336-017-9599-0>.

This package provides interface to the MATLAB toolbox Flexible Statistical Data Analysis (FSDA) which is comprehensive and computationally efficient software package for robust statistics in regression, multivariate and categorical data analysis. The current R version implements tools for regression: (forward search, S- and MM-estimation, least trimmed squares (LTS) and least median of squares (LMS)), for multivariate analysis (forward search, S- and MM-estimation), for cluster analysis and cluster-wise regression. The distinctive feature of our package is the possibility of monitoring the statistics of interest as a function of breakdown point, efficiency or subset size, depending on the estimator. This is accompanied by a rich set of graphical features, such as dynamic brushing, linking, particularly useful for exploratory data analysis.

The algorithm assigns rareness/ outlierness score to every sample in voluminous datasets. The algorithm makes multiple estimations of the proximity between a pair of samples, in low-dimensional spaces. To compute proximity, FiRE uses Sketching, a variant of locality sensitive hashing. For more details: Jindal, A., Gupta, P., Jayadeva and Sengupta, D., 2018. Discovery of rare cells from voluminous single cell expression data. Nature Communications, 9(1), p.4719. <doi:10.1038/s41467-018-07234-6>.

This package provides a collection of functions for testing various aspects of univariate time series including independence and neglected nonlinearities. Further provides functions to investigate the chaotic behavior of time series processes and to simulate different types of chaotic time series maps.

Modelizations and previsions functions for Functional AutoRegressive processes using nonparametric methods: functional kernel, estimation of the covariance operator in a subspace, ...

This package implements a generalized coordinate descent (GCD) algorithm for computing the solution paths of the hybrid Huberized support vector machine (HHSVM) and its generalizations. Supported models include the (adaptive) LASSO and elastic net penalized least squares, logistic regression, HHSVM, squared hinge loss SVM and expectile regression.

This package provides a group-specific recommendation system to use dependency information from users and items which share similar characteristics under the singular value decomposition framework. Refer to paper A Group-Specific Recommender System <doi:10.1080/01621459.2016.1219261> for the details.

This package provides functions and methods for: splitting large raster objects into smaller chunks, transferring images from a binary format into raster layers, transferring raster layers into an RData file, calculating the maximum gap (amount of consecutive missing values) of a numeric vector, and fitting harmonic regression models to periodic time series. The homoscedastic harmonic regression model is based on G. Roerink, M. Menenti and W. Verhoef (2000) <doi:10.1080/014311600209814>.

Make efficient Rust implementations of graph adjustment identification distances available in R. These distances (based on ancestor, optimal, and parent adjustment) count how often the respective adjustment identification strategy leads to causal inferences that are incorrect relative to a ground-truth graph when applied to a candidate graph instead. See also Henckel, Würtzen, Weichwald (2024) <doi:10.48550/arXiv.2402.08616>.

Simplify ggplot2 visualisation with ggblanket wrapper functions.

This package provides tools to download comprehensive datasets of local, state, and federal election results in Germany from 1990 to 2025. The package facilitates access to data on turnout, vote shares for major parties, and demographic information across different levels of government (municipal, state, and federal). It offers access to geographically harmonized datasets that account for changes in municipal boundaries over time and incorporate mail-in voting districts. Includes bundled county-level covariates from INKAR and municipality-level Census 2022 data. Users can easily retrieve, clean, and standardize German electoral data, making it ready for analysis. Data is sourced from <https://github.com/awiedem/german_election_data>.

Downloads and organizes datasets using BCB's API <https://www.bcb.gov.br/>. Offers options for caching with the memoise package and , multicore/multisession with furrr and format of output data (long/wide).

Enables calculation of image textures (Haralick 1973) <doi:10.1109/TSMC.1973.4309314> from grey-level co-occurrence matrices (GLCMs). Supports processing images that cannot fit in memory.

This package provides functions for fitting and doing predictions with Gaussian process models using Vecchia's (1988) approximation. Package also includes functions for reordering input locations, finding ordered nearest neighbors (with help from FNN package), grouping operations, and conditional simulations. Covariance functions for spatial and spatial-temporal data on Euclidean domains and spheres are provided. The original approximation is due to Vecchia (1988) <http://www.jstor.org/stable/2345768>, and the reordering and grouping methods are from Guinness (2018) <doi:10.1080/00401706.2018.1437476>. Model fitting employs a Fisher scoring algorithm described in Guinness (2019) <doi:10.48550/arXiv.1905.08374>.

Extra geoms and scales for ggplot2', including geom_cloud(), a Normal density cloud replacement for errorbars; transforms ssqrt_trans and pseudolog10_trans, which are loglike but appropriate for negative data; interp_trans() and warp_trans() which provide scale transforms based on interpolation; and an infix compose operator for scale transforms.

This package provides methods to construct and power group sequential clinical trial designs for outcomes at multiple times. Outcomes at earlier times provide information on the final (primary) outcome. A range of recruitment and correlation models are available as are methods to simulate data in order to explore design operating characteristics. For more details see Parsons (2024) <doi:10.1186/s12874-024-02174-w>.