Efficient procedures for fitting the DD-PCA (Ke et al., 2019, <arXiv:1906.00051>) by decomposing a large covariance matrix into a low-rank matrix plus a diagonally dominant matrix. The implementation of DD-PCA includes the convex approach using the Alternating Direction Method of Multipliers (ADMM) and the non-convex approach using the iterative projection algorithm. Applications of DD-PCA to large covariance matrix estimation and global multiple testing are also included in this package.

Simulates cyclic voltammetry, linear-sweep voltammetry (both with and without stirring of the solution), and single-pulse and double-pulse chronoamperometry and chronocoulometry experiments using the implicit finite difference method outlined in Gosser (1993, ISBN: 9781560810261) and in Brown (2015) <doi:10.1021/acs.jchemed.5b00225>. Additional functions provide ways to display and to examine the results of these simulations. The primary purpose of this package is to provide tools for use in courses in analytical chemistry.

This package provides a very flexible framework for building server side logic in R. The framework is unopinionated when it comes to how HTTP requests and WebSocket messages are handled and supports all levels of app complexity; from serving static content to full-blown dynamic web-apps. Fiery does not hold your hand as much as e.g. the shiny package does, but instead sets you free to create your web app the way you want.

This package provides a novel forward stepwise discriminant analysis framework that integrates Pillai's trace with Uncorrelated Linear Discriminant Analysis (ULDA), providing an improvement over traditional stepwise LDA methods that rely on Wilks Lambda. A stand-alone ULDA implementation is also provided, offering a more general solution than the one available in the MASS package. It automatically handles missing values and provides visualization tools. For more details, see Wang (2024) <doi:10.48550/arXiv.2409.03136>.

Several functions that allow by different methods to infer a piecewise polynomial regression model under regularity constraints, namely continuity or differentiability of the link function. The implemented functions are either specific to data with two regimes, or generic for any number of regimes, which can be given by the user or learned by the algorithm. A paper describing all these methods will be submitted soon. The reference will be added to this file as soon as available.

This package provides implementation of various correlation coefficients of common use in Information Retrieval. In particular, it includes Kendall (1970, isbn:0852641990) tau coefficient as well as tau_a and tau_b for the treatment of ties. It also includes Yilmaz et al. (2008) <doi:10.1145/1390334.1390435> tauAP correlation coefficient, and versions tauAP_a and tauAP_b developed by Urbano and Marrero (2017) <doi:10.1145/3121050.3121106> to cope with ties.

These functions take a gene expression value matrix, a primary covariate vector, an additional known covariates matrix. A two stage analysis is applied to counter the effects of latent variables on the rankings of hypotheses. The estimation and adjustment of latent effects are proposed by Sun, Zhang and Owen (2011). "leapp" is developed in the context of microarray experiments, but may be used as a general tool for high throughput data sets where dependence may be involved.

The functions are designed to find the efficient mean-variance frontier or portfolio weights for static portfolio (called Markowitz portfolio) analysis in resource economics or nature conservation. Using the nonlinear programming solver ('Rsolnp'), this package deals with the quadratic minimization of the variance-covariances without shorting (i.e., non-negative portfolio weights) studied in Ando and Mallory (2012) <doi:10.1073/pnas.1114653109>. See the examples, testing versions, and more details from: <https://github.com/ysd2004/portn>.

This is a core implementation of SAS procedures for linear models - GLM, REG, ANOVA, TTEST, FREQ, and UNIVARIATE. Some R packages provide type II and type III SS. However, the results of nested and complex designs are often different from those of SAS. Different results does not necessarily mean incorrectness. However, many wants the same results to SAS. This package aims to achieve that. Reference: Littell RC, Stroup WW, Freund RJ (2002, ISBN:0-471-22174-0).

Programs for analyzing large-scale time series data. They include functions for automatic specification and estimation of univariate time series, for clustering time series, for multivariate outlier detections, for quantile plotting of many time series, for dynamic factor models and for creating input data for deep learning programs. Examples of using the package can be found in the Wiley book Statistical Learning with Big Dependent Data by Daniel Peña and Ruey S. Tsay (2021). ISBN 9781119417385.

Simple tabulation should be dead simple. This package is an opinionated approach to easy tabulations while also providing exact numbers and allowing for re-usability. This is achieved by providing tabulations as data.frames with columns for values, optional variable names, frequency counts including and excluding NAs and percentages for counts including and excluding NAs. Also values are automatically sorted by in decreasing order of frequency counts to allow for fast skimming of the most important information.

R implementation of the software tools developed in the H-CUP (Healthcare Cost and Utilization Project) <https://www.hcup-us.ahrq.gov> and AHRQ (Agency for Healthcare Research and Quality) <https://www.ahrq.gov>. It currently contains functions for mapping ICD-9 codes to the AHRQ comorbidity measures and translating ICD-9 (resp. ICD-10) codes to ICD-10 (resp. ICD-9) codes based on GEM (General Equivalence Mappings) from CMS (Centers for Medicare and Medicaid Services).

The package aims to identify miRNA sponge or ceRNA modules in heterogeneous data. It provides several functions to study miRNA sponge modules at single-sample and multi-sample levels, including popular methods for inferring gene modules (candidate miRNA sponge or ceRNA modules), and two functions to identify miRNA sponge modules at single-sample and multi-sample levels, as well as several functions to conduct modular analysis of miRNA sponge modules.

BiFET identifies transcription factors (TFs) whose footprints are over-represented in target regions compared to background regions after correcting for the bias arising from the imbalance in read counts and GC contents between the target and background regions. For a given TF k, BiFET tests the null hypothesis that the target regions have the same probability of having footprints for the TF k as the background regions while correcting for the read count and GC content bias.

Random generation of survival data from a wide range of regression models, including accelerated failure time (AFT), proportional hazards (PH), proportional odds (PO), accelerated hazard (AH), Yang and Prentice (YP), and extended hazard (EH) models. The package rsurv also stands out by its ability to generate survival data from an unlimited number of baseline distributions provided that an implementation of the quantile function of the chosen baseline distribution is available in R. Another nice feature of the package rsurv lies in the fact that linear predictors are specified via a formula-based approach, facilitating the inclusion of categorical variables and interaction terms. The functions implemented in the package rsurv can also be employed to simulate survival data with more complex structures, such as survival data with different types of censoring mechanisms, survival data with cure fraction, survival data with random effects (frailties), multivariate survival data, and competing risks survival data. Details about the R package rsurv can be found in Demarqui (2024) <doi:10.48550/arXiv.2406.01750>.

An implementation of the model in Steorts (2015) <DOI:10.1214/15-BA965SI>, which performs Bayesian entity resolution for categorical and text data, for any distance function defined by the user. In addition, the precision and recall are in the package to allow one to compare to any other comparable method such as logistic regression, Bayesian additive regression trees (BART), or random forests. The experiments are reproducible and illustrated using a simple vignette. LICENSE: GPL-3 + file license.

Estimates the Concordance Correlation Coefficient to assess agreement. The scenarios considered are non-repeated measures, non-longitudinal repeated measures (replicates) and longitudinal repeated measures. It also includes the estimation of the one-way intraclass correlation coefficient also known as reliability index. The estimation approaches implemented are variance components and U-statistics approaches. Description of methods can be found in Fleiss (1986) <doi:10.1002/9781118032923> and Carrasco et al. (2013) <doi:10.1016/j.cmpb.2012.09.002>.

Analysis of Fluorescence Recovery After Photobleaching (FRAP) experiments using nonlinear mixed-effects regression models and analysis of the results. FRApp is not limited to the analysis of FRAP experiments only. Any nonlinear mixed-effects models with an asymptotic exponential functional relationship to hierarchical data in various domains can be fitted. The analysis of data available in the package is presented in Di Credico, G., Pelucchi, S., Pauli, F. et al. (2025) <doi:10.1038/s41598-025-87154-w>.

Set of functions designed to solve inverse problems. The direct problem is used to calculate a cost function to be minimized. Here are listed some papers using Inverse Problems solvers and sensitivity analysis: (Jader Lugon Jr.; Antonio J. Silva Neto 2011) <doi:10.1590/S1678-58782011000400003>. (Jader Lugon Jr.; Antonio J. Silva Neto; Pedro P.G.W. Rodrigues 2008) <doi:10.1080/17415970802082864>. (Jader Lugon Jr.; Antonio J. Silva Neto; Cesar C. Santana 2008) <doi:10.1080/17415970802082922>.

This is a C++ mutual information (MI) library based on the k-nearest neighbor (KNN) algorithm. There are three functions provided for computing MI for continuous values, mixed continuous and discrete values, and conditional MI for continuous values. They are based on algorithms by A. Kraskov, et. al. (2004) <doi:10.1103/PhysRevE.69.066138>, BC Ross (2014)<doi:10.1371/journal.pone.0087357>, and A. Tsimpiris (2012) <doi:10.1016/j.eswa.2012.05.014>, respectively.

Estimates the sample size needed to detect microbial contamination in a lot with a user-specified detection probability and user-specified analytical sensitivity. Various patterns of microbial contamination are accounted for: homogeneous (Poisson), heterogeneous (Poisson-Gamma) or localized(Zero-inflated Poisson). Ida Jongenburger et al. (2010) <doi:10.1016/j.foodcont.2012.02.004> "Impact of microbial distributions on food safety". Leroy Simon (1963) <doi:10.1017/S0515036100001975> "Casualty Actuarial Society - The Negative Binomial and Poisson Distributions Compared".

We implement a surrogate modeling algorithm to guide simulation-based sample size planning. The method is described in detail in our paper (Zimmer & Debelak (2023) <doi:10.1037/met0000611>). It supports multiple study design parameters and optimization with respect to a cost function. It can find optimal designs that correspond to a desired statistical power or that fulfill a cost constraint. We also provide a tutorial paper (Zimmer et al. (2023) <doi:10.3758/s13428-023-02269-0>).

An n-gram is a sequence of n "words" taken, in order, from a body of text. This is a collection of utilities for creating, displaying, summarizing, and "babbling" n-grams. The tokenization and "babbling" are handled by very efficient C code, which can even be built as its own standalone library. The babbler is a simple Markov chain. The package also offers a vignette with complete example workflows and information about the utilities offered in the package.

This package provides tools for penalised maximum likelihood estimation of hidden semi-Markov models (HSMMs) with flexible state dwell-time distributions. These include functions for model fitting, model checking and state-decoding. The package considers HSMMs for univariate time series with state-dependent gamma, normal, Poisson or Bernoulli distributions. For details, see Pohle, J., Adam, T. and Beumer, L.T. (2021): Flexible estimation of the state dwell-time distribution in hidden semi-Markov models. <arXiv:2101.09197>.