This package implements comprehensive test data engineering methods as described in Shojima (2022, ISBN:978-9811699856). Provides statistical techniques for engineering and processing test data: Classical Test Theory (CTT) with reliability coefficients for continuous ability assessment; Item Response Theory (IRT) including Rasch, 2PL, and 3PL models with item/test information functions; Latent Class Analysis (LCA) for nominal clustering; Latent Rank Analysis (LRA) for ordinal clustering with automatic determination of cluster numbers; Biclustering methods including infinite relational models for simultaneous clustering of examinees and items without predefined cluster numbers; and Bayesian Network Models (BNM) for visualizing inter-item dependencies. Features local dependence analysis through LRA and biclustering, parameter estimation, dimensionality assessment, and network structure visualization for educational, psychological, and social science research.

This package implements clustering and estimates parameters in Exponential-Family Random Graph Models for static undirected and directed networks, developed in Vu et al. (2013) <https://projecteuclid.org/euclid.aoas/1372338477>.

An alternative to Exploratory Factor Analysis (EFA) for metrical data in R. Drawing on characteristics of classical test theory, Exploratory Likert Scaling (ELiS) supports the user exploring multiple one-dimensional data structures. In common research practice, however, EFA remains the go-to method to uncover the (underlying) structure of a data set. Orthogonal dimensions and the potential of overextraction are often accepted as side effects. As described in Müller-Schneider (2001) <doi:10.1515/zfsoz-2001-0404>), ELiS confronts these problems. As a result, elisr provides the platform to fully exploit the exploratory potential of the multiple scaling approach itself.

EM algorithms and several efficient initialization methods for model-based clustering of finite mixture Gaussian distribution with unstructured dispersion in both of unsupervised and semi-supervised learning.

This package creates ensemble taxonomic assignments of amplicon sequencing data in R using outputs of multiple taxonomic assignment algorithms and/or reference databases. Includes flexible algorithms for mapping taxonomic nomenclatures onto one another and for computing ensemble taxonomic assignments.

This package provides tools for integrated sensitivity analysis of evidence factors in observational studies. When an observational study allows for multiple independent or nearly independent inferences which, if vulnerable, are vulnerable to different biases, we have multiple evidence factors. This package provides methods that respect type I error rate control. Examples are provided of integrated evidence factors analysis in a longitudinal study with continuous outcome and in a case-control study. Karmakar, B., French, B., and Small, D. S. (2019)<DOI:10.1093/biomet/asz003>.

Analysis and visualization of similarities between epilepsy ontologies based on text mining results by comparing ranked lists of co-occurring drug terms in the BioASQ corpus. The ranked result lists of neurological drug terms co-occurring with terms from the epilepsy ontologies EpSO, ESSO, EPILONT, EPISEM and FENICS undergo further analysis. The source data to create the ranked lists of drug names is produced using the text mining workflows described in Mueller, Bernd and Hagelstein, Alexandra (2016) <doi:10.4126/FRL01-006408558>, Mueller, Bernd et al. (2017) <doi:10.1007/978-3-319-58694-6_22>, Mueller, Bernd and Rebholz-Schuhmann, Dietrich (2020) <doi:10.1007/978-3-030-43887-6_52>, and Mueller, Bernd et al. (2022) <doi:10.1186/s13326-021-00258-w>.

Automation of the item selection processes for Rasch scales by means of exhaustive search for suitable Rasch models (dichotomous, partial credit, rating-scale) in a list of item-combinations. The item-combinations to test can be either all possible combinations or item-combinations can be defined by several rules (forced inclusion of specific items, exclusion of combinations, minimum/maximum items of a subset of items). Tests for model fit and item fit include ordering of the thresholds, item fit-indices, likelihood ratio test, Martin-Löf test, Wald-like test, person-item distribution, person separation index, principal components of Rasch residuals, empirical representation of all raw scores or Rasch trees for detecting differential item functioning. The tests, their ordering and their parameters can be defined by the user. For parameter estimation and model tests, functions of the packages eRm', psychotools or pairwise can be used.

This package provides a goodness-of-fit test for elliptical distributions with diagnostic capabilities. Gilles R. Ducharme, Pierre Lafaye de Micheaux (2020) <doi:10.1016/j.jmva.2020.104602>.

An implementation of European Forestry Dynamics Model (EFDM) and an estimation algorithm for the transition probabilities. The EFDM is a large-scale forest model that simulates the development of the forest and estimates volume of wood harvested for any given forested area. This estimate can be broken down by, for example, species, site quality, management regime and ownership category. See Packalen et al. (2015) <doi:10.2788/153990>.

This package provides a collection of epidemic/network-related tools. Simulates transmission of diseases through contact networks. Performs Bayesian inference on network and epidemic parameters, given epidemic data.

Easily create interactive charts by leveraging the Echarts Javascript library which includes 36 chart types, themes, Shiny proxies and animations.

The EUNIS habitat classification is a comprehensive pan-European system for habitat identification <https://www.eea.europa.eu/data-and-maps/data/eunis-habitat-classification-1>. This is an R data package providing the EUNIS classification system. The classification is hierarchical and covers all types of habitats from natural to artificial, from terrestrial to freshwater and marine. The habitat types are identified by specific codes, names and descriptions and come with schema crosswalks to other habitat typologies.

This package provides an interface to e-Stat API, the one-stop service for official statistics of the Japanese government.

Test hypotheses and construct confidence intervals for AUC (area under Receiver Operating Characteristic curve) and pAUC (partial area under ROC curve), from the given two samples of test data with disease/healthy subjects. The method used is based on TWO SAMPLE empirical likelihood and PROFILE empirical likelihood, as described in <https://www.ms.uky.edu/~mai/research/eAUC1.pdf>.

Utilities for managing egocentrically sampled network data and a wrapper around the ergm package to facilitate ERGM inference and simulation from such data. See Krivitsky and Morris (2017) <doi:10.1214/16-AOAS1010>.

Analyses districted electoral systems of any magnitude by computing district-party conversion ratios and seats-to-votes deviations, decomposing the sources of deviation. Traditional indexes are also computed. References: Kedar, O., Harsgor, L. and Sheinerman, R.A. (2016). <doi:10.1111/ajps.12225>. Penades, A and Pavia, J.M. (2025) The decomposition of seats-to-votes distortion in elections: mean, variance, malapportionment and participation''. Acknowledgements: The authors wish to thank Consellerà a de Educación, Cultura, Universidades y Empleo, Generalitat Valenciana (grant CIACO/2023/031) for supporting this research.

Network-centric framework for integrative analysis of high-throughput gene expression data using user-supplied gene-gene interaction graphs. Constructs seed-centered multi-generation networks constrained by expression correlations and simulates expression perturbation scenarios via regression-based prediction (van Dam, 2018).

This new extreme conformal prediction framework provides informative prediction intervals at the high-confidence levels for which classical conformal methods fail. In applications with potentially high-impact events, a very high level of confidence is often required for predictions. If that level is too large relative to the amount of data used for calibration, classical conformal methods provide infinitely wide, thus, uninformative prediction intervals. Our extreme conformal procedure bridges extreme value statistics and conformal prediction to provide reliable and informative prediction intervals with high-confidence coverage, which can be constructed using any black-box extreme quantile regression method. A weighted version of the approach can account for nonstationary data. The methodology was introduced in Pasche, Lam, and Engelke (2026) <doi:10.1007/s10687-026-00536-9>.

This package implements forecasting methods for individual electricity load curves, including Kernel Wavelet Functional (KWF), clustered KWF, Generalized Additive Models (GAM), Multivariate Adaptive Regression Splines (MARS), and Long Short-Term Memory (LSTM) models. Provides normalized dataset adapters for iFlex, StoreNet, Smart-Grid Smart-City, Low Carbon London, and REFIT; download and read support for IDEAL and GX; explicit Python backend selection for TensorFlow-based LSTM fits; helpers for daily segmentation and rolling-origin benchmarking; and compact shipped example panels and benchmark-result datasets.

This package provides a set of functions for organising and analysing datasets from experiments run using Eyelink eye-trackers. Organising functions help to clean and prepare eye-tracking datasets for analysis, and mark up key events such as display changes and responses made by participants. Analysing functions help to create means for a wide range of standard measures (such as mean fixation durations'), which can then be fed into the appropriate statistical analyses and graphing packages as necessary.

This package provides basic distribution functions for a mixture model of a Gaussian and exponential distribution.

Instead of counting observations before and after a subset() call, the ExclusionTable() function reports the number before and after each subset() call together with the number of observations that have been excluded. This is especially useful in observational studies for keeping track how many observations have been excluded for each in-/ or exclusion criteria. You just need to provide ExclusionTable() with a dataset and a list of logical filter statements.

This package provides functions for signal detection and identification designed for Event-Related Potentials (ERP) data in a linear model framework. The functional F-test proposed in Causeur, Sheu, Perthame, Rufini (2018, submitted) for analysis of variance issues in ERP designs is implemented for signal detection (tests for mean difference among groups of curves in One-way ANOVA designs for example). Once an experimental effect is declared significant, identification of significant intervals is achieved by the multiple testing procedures reviewed and compared in Sheu, Perthame, Lee and Causeur (2016, <DOI:10.1214/15-AOAS888>). Some of the methods gathered in the package are the classical FDR- and FWER-controlling procedures, also available using function p.adjust. The package also implements the Guthrie-Buchwald procedure (Guthrie and Buchwald, 1991 <DOI:10.1111/j.1469-8986.1991.tb00417.x>), which accounts for the auto-correlation among t-tests to control erroneous detection of short intervals. The Adaptive Factor-Adjustment method is an extension of the method described in Causeur, Chu, Hsieh and Sheu (2012, <DOI:10.3758/s13428-012-0230-0>). It assumes a factor model for the correlation among tests and combines adaptively the estimation of the signal and the updating of the dependence modelling (see Sheu et al., 2016, <DOI:10.1214/15-AOAS888> for further details).