Analysis and visualization of data from temporal sensory methods, including for temporal check-all-that-apply (TCATA) and temporal dominance of sensations (TDS). Methods are mainly from manuscripts by Castura, J.C., Antúnez, L., Giménez, A., and Ares, G. (2016) <doi:10.1016/j.foodqual.2015.06.017>, Castura, Baker, and Ross (2016) <doi:10.1016/j.foodqual.2016.06.011>, and Pineau et al. (2009) <doi:10.1016/j.foodqual.2009.04.005>.

Create reproducible and transparent research projects in R'. This package is based on the Workflow for Open Reproducible Code in Science (WORCS), a step-by-step procedure based on best practices for Open Science. It includes an RStudio project template, several convenience functions, and all dependencies required to make your project reproducible and transparent. WORCS is explained in the tutorial paper by Van Lissa, Brandmaier, Brinkman, Lamprecht, Struiksma, & Vreede (2021). <doi:10.3233/DS-210031>.

This package contains functions, data sets and examples for the book: Yves Croissant (2025) "Microeconometrics with R", Chapman and Hall/CRC The R Series <doi:10.1201/9781003100263>. The package includes a set of estimators for models used in microeconometrics, especially for count data and limited dependent variables. Test functions include score test, Hausman test, Vuong test, Sargan test and conditional moment test. A small subset of the data set used in the book is also included.

This package provides qualitative methods for the validation of dynamic models. It contains

1. an orthogonal set of deviance measures for absolute, relative and ordinal scale and

2. approaches accounting for time shifts.

The first approach transforms time to take time delays and speed differences into account. The second divides the time series into interval units according to their main features and finds the longest common subsequence (LCS) using a dynamic programming algorithm.

This is a package supporting cluster analysis for cognitive diagnosis based on the Asymptotic Classification Theory (Chiu, Douglas & Li, 2009; doi:10.1007/s11336-009-9125-0). Given the sample statistic of sum-scores, cluster analysis techniques can be used to classify examinees into latent classes based on their attribute patterns. In addition to the algorithms used to classify data, three labeling approaches are proposed to label clusters so that examinees' attribute profiles can be obtained.

Distance-correlation based Gene Set Analysis for longitudinal gene expression profiles. In longitudinal studies, the gene expression profiles were collected at each visit from each subject and hence there are multiple measurements of the gene expression profiles for each subject. The dcGSA package could be used to assess the associations between gene sets and clinical outcomes of interest by fully taking advantage of the longitudinal nature of both the gene expression profiles and clinical outcomes.

TRIP is a software framework that provides analytics services on antigen receptor (B cell receptor immunoglobulin, BcR IG | T cell receptor, TR) gene sequence data. It is a web application written in R Shiny. It takes as input the output files of the IMGT/HighV-Quest tool. Users can select to analyze the data from each of the input samples separately, or the combined data files from all samples and visualize the results accordingly.

This package provides a powerful tool for automating the early detection of disease outbreaks in time series data. aeddo employs advanced statistical methods, including hierarchical models, in an innovative manner to effectively characterize outbreak signals. It is particularly useful for epidemiologists, public health professionals, and researchers seeking to identify and respond to disease outbreaks in a timely fashion. For a detailed reference on hierarchical models, consult Henrik Madsen and Poul Thyregod's book (2011), ISBN: 9781420091557.

This package provides functions for predicting plant microRNAs (miRNAs) associated with cold, drought, heat, and salt stress using the ASmiR machine learning framework. Implements PseKNC feature generation and stress-specific support vector machine models described by Pradhan et al. (2023), "ASmiR: a machine learning framework for prediction of abiotic stress-specific miRNAs in plants", Functional & Integrative Genomics, 23(2), article 92, <doi:10.1007/s10142-023-01014-2>.

This package implements a basis function or functional data analysis framework for several techniques of multivariate analysis in continuous-time setting. Specifically, we introduced continuous-time analogues of several classical techniques of multivariate analysis, such as principal component analysis, canonical correlation analysis, Fisher linear discriminant analysis, K-means clustering, and so on. Details are in Biplab Paul, Philip T. Reiss, Erjia Cui and Noemi Foa (2025) "Continuous-time multivariate analysis" <doi: 10.1080/10618600.2024.2374570>.

Statistical tests for the comparison between two correlations based on either independent or dependent groups. Dependent correlations can either be overlapping or nonoverlapping. A web interface is available on the website <http://comparingcorrelations.org>. A plugin for the R GUI and IDE RKWard is included. Please install RKWard from <https://rkward.kde.org> to use this feature. The respective R package rkward cannot be installed directly from a repository, as it is a part of RKWard.

Decomposing value added growth into explanatory factors. A cost constrained value added function is defined to specify the production frontier. Industry estimates can also be aggregated using a weighted average approach. Details about the methodology and data can be found in Diewert and Fox (2018) <doi:10.1093/oxfordhb/9780190226718.013.19> and Zeng, Parsons, Diewert and Fox (2018) <https://www.business.unsw.edu.au/research-site/centreforappliedeconomicresearch-site/Documents/emg2018-6_SZeng_EMG-Slides.pdf>.

Flexible, interpretable machine learning algorithm for additive tree sums (FIGS). Fits a sum of shallow classification and regression trees (CART) by greedily minimizing residual impurity, growing a new tree or deepening an existing one at each step, whichever reduces the residuals most. Supports regression and two-class classification, variable importance, ggplot2 graphics, bootstrap ensembling and seamless integration with parsnip and tidymodels workflows. The method is described in Tan et al. (2023) <doi:10.1073/pnas.2310151122>.

This package implements dense and sparse generalized contrastive principal component analysis (gcPCA) with S3 fit objects and methods for prediction, summaries, and plotting. The gcPCA is a hyperparameter-free method for comparing high-dimensional datasets collected under different experimental conditions to reveal low-dimensional patterns enriched in one condition compared to the other. Method details are described in de Oliveira, Garg, Hjerling-Leffler, Batista-Brito, and Sjulson (2025) <doi:10.1371/journal.pcbi.1012747>.

This package implements nonparametric density estimation with Averaged Shifted Histogram (ASH), Linear Blend Frequency Polygon (LBFP), and General Linear Blend Frequency Polygon (GLBFP) estimators. The package provides pointwise and grid-based estimation workflows, sparse-prefix grid-count computation, plotting helpers, and plug-in bandwidth selection. Methodological background follows Scott (1992) <doi:10.1002/9780470316849>, Terrell and Scott (1985) <doi:10.1080/01621459.1985.10477163>, and Carbon and Duchesne (2024) <doi:10.1007/s10463-023-00883-5>.

Plot an R package's recursive dependency graph and tabulate the number of unique downstream dependencies added by top-level dependencies. This helps R package developers identify which of their declared dependencies add the most downstream dependencies in order to prioritize them for removal if needed. Uses graph stress minimization adapted from Schoch (2023) <doi:10.21105/joss.05238> and originally reported in Gansner et al. (2004) <doi:10.1007/978-3-540-31843-9_25>.

This package contains data on Post-Secondary Institution Statistics in 2020 <https://nces.ed.gov/ipeds/use-the-data>. The package allows easy access to a wide variety of information regarding Post-secondary Institutions, its students, faculty, and their demographics, financial aid, educational and recreational offerings, and completions. This package can be used by students, college counselors, or involved parents interested in pursuing higher education, considering their options, and securing admission into their school of choice.

The implemented methods reach out to scientists that seek to estimate multiplicity of infection (MOI) and lineage (allele) frequencies and prevalences at molecular markers using the maximum-likelihood method described in Schneider (2018) <doi:10.1371/journal.pone.0194148>, and Schneider and Escalante (2014) <doi:10.1371/journal.pone.0097899>. Users can import data from Excel files in various formats, and perform maximum-likelihood estimation on the imported data by the package's moimle() function.

Surface topography calculations of Dirichlet's normal energy, relief index, surface slope, and orientation patch count for teeth using scans of enamel caps. Importantly, for the relief index and orientation patch count calculations to work, the scanned tooth files must be oriented with the occlusal plane parallel to the x and y axes, and perpendicular to the z axis. The files should also be simplified, and smoothed in some other software prior to uploading into R.

Optimal experimental designs for both population and individual studies based on nonlinear mixed-effect models. Often this is based on a computation of the Fisher Information Matrix. This package was developed for pharmacometric problems, and examples and predefined models are available for these types of systems. The methods are described in Nyberg et al. (2012) <doi:10.1016/j.cmpb.2012.05.005>, and Foracchia et al. (2004) <doi:10.1016/S0169-2607(03)00073-7>.

Check available classification and regression data sets from the PMLB repository and download them. The PMLB repository (<https://github.com/EpistasisLab/pmlbr>) contains a curated collection of data sets for evaluating and comparing machine learning algorithms. These data sets cover a range of applications, and include binary/multi-class classification problems and regression problems, as well as combinations of categorical, ordinal, and continuous features. There are currently over 150 datasets included in the PMLB repository.

This package provides several functions for area level of small area estimation using hierarchical Bayesian (HB) methods with several univariate distributions for variables of interest. The dataset that is used in every function is generated accordingly in the Example. The rjags package is employed to obtain parameter estimates. Model-based estimators involve the HB estimators which include the mean and the variation of mean. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>.

This package provides functions to estimate, predict and interpolate areal data. For estimation and prediction we assume areal data is an average of an underlying continuous spatial process as in Moraga et al. (2017) <doi:10.1016/j.spasta.2017.04.006>, Johnson et al. (2020) <doi:10.1186/s12942-020-00200-w>, and Wilson and Wakefield (2020) <doi:10.1093/biostatistics/kxy041>. The interpolation methodology is (mostly) based on Goodchild and Lam (1980, ISSN:01652273).

This package provides a single-cell Clustering method using Autoencoder and Network fusion ('scCAN') Bang Tran (2022) <doi:10.1038/s41598-022-14218-6> for segregating the cells from the high-dimensional scRNA-Seq data. The software automatically determines the optimal number of clusters and then partitions the cells in a way such that the results are robust to noise and dropouts. scCAN is fast and it supports Windows, Linux, and Mac OS.