This package provides tools to help with shiny reactivity. The react object offers an alternative way to call reactive expressions to better identify them in the server code.

This package implements the regularized exponentially tilted empirical likelihood method. Details of the method are given in Kim, MacEachern, and Peruggia (2023) <doi:10.48550/arXiv.2312.17015>. This work was supported by the U.S. National Science Foundation under Grants No. SES-1921523 and DMS-2015552.

Processing and analysis of targeted sequencing data. The package provides a user-friendly interface for core VSEARCH (Rognes et al. (2016), <doi:10.7717/peerj.2584>) functions, in addition to tools for visualization and parameter tuning.

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).

The renewal Hawkes (RHawkes) process (Wheatley, Filimonov, and Sornette, 2016 <doi:10.1016/j.csda.2015.08.007>) is an extension to the classical Hawkes self-exciting point process widely used in the modelling of clustered event sequence data. This package provides functions to simulate the RHawkes process with a given immigrant hazard rate function and offspring birth time density function, to compute the exact likelihood of a RHawkes process using the recursive algorithm proposed by Chen and Stindl (2018) <doi:10.1080/10618600.2017.1341324>, to compute the Rosenblatt residuals for goodness-of-fit assessment, and to predict future event times based on observed event times up to a given time. A function implementing the linear time RHawkes process likelihood approximation algorithm proposed in Stindl and Chen (2021) <doi:10.1007/s11222-021-10002-0> is also included.

Allows the user to learn Bayesian networks from datasets containing thousands of variables. It focuses on score-based learning, mainly the BIC and the BDeu score functions. It provides state-of-the-art algorithms for the following tasks: (1) parent set identification - Mauro Scanagatta (2015) <http://papers.nips.cc/paper/5803-learning-bayesian-networks-with-thousands-of-variables>; (2) general structure optimization - Mauro Scanagatta (2018) <doi:10.1007/s10994-018-5701-9>, Mauro Scanagatta (2018) <http://proceedings.mlr.press/v73/scanagatta17a.html>; (3) bounded treewidth structure optimization - Mauro Scanagatta (2016) <http://papers.nips.cc/paper/6232-learning-treewidth-bounded-bayesian-networks-with-thousands-of-variables>; (4) structure learning on incomplete data sets - Mauro Scanagatta (2018) <doi:10.1016/j.ijar.2018.02.004>. Distributed under the LGPL-3 by IDSIA.

Pattern matching, extraction, replacement and other string processing operations using Google's RE2 <https://github.com/google/re2> regular-expression engine. Consistent interface (similar to stringr'). RE2 uses finite-automata based techniques, and offers a fast and safe alternative to backtracking regular-expression engines like those used in stringr', stringi and other PCRE implementations.

Make optimal decisions for your personal or household finances. Use tools and methods that are selected carefully to align with academic consensus, bridging the gap between theoretical knowledge and practical application. They help you find your own personalized optimal discretionary spending or optimal asset allocation, and prepare you for retirement or financial independence. The optimal solution to this problems is extremely complex, and we only have a single lifetime to get it right. Fortunately, we now have the user-friendly tools implemented, that integrate life-cycle models with single-period net-worth mean-variance optimization models. Those tools can be used by anyone who wants to see what highly-personalized optimal decisions can look like. For more details see: Idzorek T., Kaplan P. (2024, ISBN:9781952927379), Haghani V., White J. (2023, ISBN:9781119747918).

This package provides an interface to many endpoints of Mixpanel's Data Export, Engage and JQL API. The R functions allow for event and profile data export as well as for segmentation, retention, funnel and addiction analysis. Results are always parsed into convenient R objects. Furthermore it is possible to load and update profiles.

Distance-sampling (<doi:10.1007/978-3-319-19219-2>) is a field survey and analytical method that estimates density and abundance of survey targets (e.g., animals) when detection probability declines with observation distance. Distance-sampling is popular in ecology, especially when survey targets are observed from aerial platforms (e.g., airplane or drone), surface vessels (e.g., boat or truck), or along walking transects. Analysis involves fitting smooth (parametric) curves to histograms of observation distances and using those functions to adjust density estimates for missed targets. Routines included here fit curves to observation distance histograms, estimate effective sampling area, density of targets in surveyed areas, and the abundance of targets in a surrounding study area. Confidence interval estimation uses built-in bootstrap resampling. Help files are extensive and have been vetted by multiple authors. Many tutorials are available on the package's website (URL below).

An R Interface to EPP-lab v1.0. EPP-lab is a Java program for projection pursuit using genetic algorithms written by Alain Berro and S. Larabi Marie-Sainte and is included in the package.

This package provides a very lightweight package that writes out log messages in an opinionated way. Simpler and lighter than other logging packages, rlog provides a compact feature set that focuses on getting the job done in a Unix-like way.

This package performs Principal Components Analysis (also known as PCA) dimensionality reduction in the context of a linear regression. In most cases, PCA dimensionality reduction is performed independent of the response variable for a regression. This captures the majority of the variance of the model's predictors, but may not actually be the optimal dimensionality reduction solution for a regression against the response variable. An alternative method, optimized for a regression against the response variable, is to use both PCA and a relative importance measure. This package applies PCA to a given data frame of predictors, and then calculates the relative importance of each PCA factor against the response variable. It outputs ordered factors that are optimized for model fit. By performing dimensionality reduction with this method, an individual can achieve a the same r-squared value as performing just PCA, but with fewer PCA factors. References: Yuri Balasanov (2017) <https://ilykei.com>.

R Commander plug-in for repeated-measures and mixed-design ('split-plot') ANOVA. It adds a new menu entry for repeated measures that allows to deal with up to three within-subject factors and optionally with one or several between-subject factors. It also provides supplementary options to oneWayAnova() and multiWayAnova() functions, such as choice of ANOVA type, display of effect sizes and post hoc analysis for multiWayAnova().

This package implements random variables by means of S4 classes and methods.

This package contains three functions that query AuriQ Systems Essentia Database and return the results in R. essQuery takes a single Essentia command and captures the output in R, where you can save the output to a dataframe or stream it directly into additional analysis. read.essentia takes an Essentia script and captures the output csv data into R, where you can save the output to a dataframe or stream it directly into additional analysis. capture.essentia takes a file containing any number of Essentia commands and captures the output of the specified statements into R dataframes. Essentia can be downloaded for free at http://www.auriq.com/documentation/source/install/index.html.

Allows to limit the rate at which one or more functions can be called.

This package provides functionality to prepare data and analyze plausibility of both forecasted and reported epidemiological signals. The functions implement a set of plausibility algorithms that are agnostic to geographic and time resolutions and are calculated independently then presented as a combined score.

For a multisite replication project, computes the consistency metric P_orig, which is the probability that the original study would observe an estimated effect size as extreme or more extreme than it actually did, if in fact the original study were statistically consistent with the replications. Other recommended metrics are: (1) the probability of a true effect of scientifically meaningful size in the same direction as the estimate the original study; and (2) the probability of a true effect of meaningful size in the direction opposite the original study's estimate. These two can be computed using the package \codeMetaUtility::prop_stronger. Additionally computes older metrics used in replication projects (namely expected agreement in "statistical significance" between an original study and replication studies as well as prediction intervals for the replication estimates). See Mathur and VanderWeele (under review; <https://osf.io/apnjk/>) for details.

Calculates and compares various reliability coefficients for unidimensional and multidimensional scales. Supported unidimensional estimators include coefficient alpha, congeneric reliability, the Gilmer-Feldt coefficient, Feldt's classical congeneric reliability, Hancock's H, Heise-Bohrnstedt's omega, Kaiser-Caffrey's alpha, and Ten Berge and Zegers mu series. Multidimensional estimators include stratified alpha, maximal reliability, correlated factors reliability, second-order factor reliability, and bifactor reliability. See Cho (2021) <doi:10.1007/s11336-021-09801-1>, Cho (2024) <doi:10.1037/met0000475>, Cho (2025) <doi:10.1037/met0000525>.

Enhances the R Optimization Infrastructure ('ROI') package with the alabama solver for solving nonlinear optimization problems.

Allows developers to work with many R folders inside a package. It offers functionalities to transfer R scripts (saved outside the R folder) into the R folder while making additional checks.

This package implements a robust multivariate control-chart methodology for batch-based industrial processes with multiple correlated variables using the Dual STATIS (Structuration des Tableaux A Trois Indices de la Statistique) framework. A robust compromise covariance matrix is constructed from Phase I batches with the Minimum Covariance Determinant (MCD) estimator, and a Hotelling-type T² statistic is applied for anomaly detection in Phase II. The package includes functions to simulate clean and contaminated batches, to compute both robust and classical Hotelling T² control charts, to visualize results via robust biplots, and to launch an interactive shiny dashboard. An internal dataset (pharma_data) is provided for reproducibility. See Lavit, Escoufier, Sabatier and Traissac (1994) <doi:10.1016/0167-9473(94)90134-1> for the original STATIS methodology, and Rousseeuw and Van Driessen (1999) <doi:10.1080/00401706.1999.10485670> for the MCD estimator.

This package provides a robust alternative to the aJIVE (angle based Joint and Individual Variation Explained) method (Feng et al 2018: <doi:10.1016/j.jmva.2018.03.008>) for the estimation of joint and individual components in the presence of outliers in multi-source data. It decomposes the multi-source data into joint, individual and residual (noise) contributions. The decomposition is robust to outliers and noise in the data. The method is illustrated in Ponzi et al (2021) <arXiv:2101.09110>.