This package provides functions and methods for organizing data in hypercubes (i.e., a multi-dimensional cube). Cubes are generated from molten data frames. Each cube can be manipulated with five operations: rotation (change.dimensionOrder()), dicing and slicing (add.selection(), remove.selection()), drilling down (add.aggregation()), and rolling up (remove.aggregation()).

Analysis of plant pathogen pathotype survey data. Functions provided calculate distribution of susceptibilities, distribution of complexities with statistics, pathotype frequency distribution, as well as diversity indices for pathotypes. This package is meant to be a direct replacement for Herrmann, Löwer and Schachtel's (1999) <doi:10.1046/j.1365-3059.1999.00325.x> Habgood-Gilmour Spreadsheet, HaGiS', previously used for pathotype analysis.

This package provides functions to view files in raw binary form like in a hex editor. Additional functions to specify and read arbitrary binary formats.

Discriminant analysis and data clustering methods for high dimensional data, based on the assumption that high-dimensional data live in different subspaces with low dimensionality proposing a new parametrization of the Gaussian mixture model which combines the ideas of dimension reduction and constraints on the model.

Suite of tools for managing cached files, targeting use in other R packages. Uses rappdirs for cross-platform paths. Provides utilities to manage cache directories, including targeting files by path or by key; cached directories can be compressed and uncompressed easily to save disk space.

Texts for H.C. Andersens fairy tales, ready for text analysis. Fairy tales in German, Danish, English, Spanish and French.

Estimating heterogeneous treatment effects with tree-based machine learning algorithms and visualizing estimated results in flexible and presentation-ready ways. For more information, see Brand, Xu, Koch, and Geraldo (2021) <doi:10.1177/0081175021993503>. Our current package first started as a fork of the causalTree package on GitHub and we greatly appreciate the authors for their extremely useful and free package.

The hybrid model likelihood as described by Ramos-Goñi et al. (2017) <doi:10.1097/MLR.0000000000000283> is implemented and and embedded in a latent class framework. The package is based on flexmix and among others contains an M-step-driver as described by Leisch (2004) <doi:10.18637/jss.v011.i08>. Users can, for example, estimate latent classes for EQ-5D value sets and address preference heterogeneity. Both uncensored and censored data are supported. Furthermore, heteroscedasticity can be taken into account. It is possible to control for different covariates on the continuous and dichotomous data and start values can differ between the expected latent classes.

This package provides a local haplotyping tool for use in trait association and trait prediction analyses pipelines. HaploVar enables users take single nucleotide polymorphisms (SNPs) (in VCF format) and a linkage disequilibrium (LD) matrix, calculate local haplotypes and format the output to be compatible with a wide range of trait association and trait prediction tools. The local haplotypes are calculated from the LD matrix using a clustering algorithm called density-based spatial clustering of applications with noise (DBSCAN) (Ester et al., 1996) <ISBN: 1577350049>.

Shiny-App that allows to annotate vectors of texts to predefined categories by hand.

Functions, data sets, analyses and examples from the second edition of the book A Handbook of Statistical Analyses Using R (Brian S. Everitt and Torsten Hothorn, Chapman & Hall/CRC, 2008). The first chapter of the book, which is entitled An Introduction to R'', is completely included in this package, for all other chapters, a vignette containing all data analyses is available. In addition, the package contains Sweave code for producing slides for selected chapters (see HSAUR2/inst/slides).

Perform hierarchical Bayesian Aldrich-McKelvey scaling using Hamiltonian Monte Carlo via Stan'. Aldrich-McKelvey ('AM') scaling is a method for estimating the latent positions of survey respondents and external objects on a common scale using positional survey data. The hierarchical versions of the Bayesian AM model included in this package outperform other versions both in terms of yielding meaningful posterior distributions for respondent positions and in terms of recovering true respondent positions in simulations. The package contains functions for preparing data, fitting models, extracting estimates, plotting key results, and comparing models using cross-validation. The original version of the default model is described in Bølstad (2024) <doi:10.1017/pan.2023.18>.

This package provides a correlation-based batch process for fast, accurate imputation for high dimensional missing data problems via chained random forests. See Waggoner (2023) <doi:10.1007/s00180-023-01325-9> for more on hdImpute', Stekhoven and Bühlmann (2012) <doi:10.1093/bioinformatics/btr597> for more on missForest', and Mayer (2022) <https://github.com/mayer79/missRanger> for more on missRanger'.

This package provides functions to compute small area estimates based on a basic area or unit-level model. The model is fit using restricted maximum likelihood, or in a hierarchical Bayesian way. In the latter case numerical integration is used to average over the posterior density for the between-area variance. The output includes the model fit, small area estimates and corresponding mean squared errors, as well as some model selection measures. Additional functions provide means to compute aggregate estimates and mean squared errors, to minimally adjust the small area estimates to benchmarks at a higher aggregation level, and to graphically compare different sets of small area estimates.

This package provides a Hierarchical Spatial Autoregressive Model (HSAR), based on a Bayesian Markov Chain Monte Carlo (MCMC) algorithm (Dong and Harris (2014) <doi:10.1111/gean.12049>). The creation of this package was supported by the Economic and Social Research Council (ESRC) through the Applied Quantitative Methods Network: Phase II, grant number ES/K006460/1.

Calculate clinical scores for hidradenitis suppurativa (HS), a dermatologic disease. The scores are typically used for evaluation of efficacy in clinical trials. The scores are not commonly used in clinical practice. The specific scores implemented are Hidradenitis Suppurativa Clinical Response (HiSCR) (Kimball, et al. (2015) <doi:10.1111/jdv.13216>), Hidradenitis Suppurativa Area and Severity Index Revised (HASI-R) (Goldfarb, et al. (2020) <doi:10.1111/bjd.19565>), hidradenitis suppurativa Physician Global Assessment (HS PGA) (Marzano, et al. (2020) <doi:10.1111/jdv.16328>), and the International Hidradenitis Suppurativa Severity Score System (IHS4) (Zouboulis, et al. (2017) <doi:10.1111/bjd.15748>).

Paternal recombination rate and maternal linkage disequilibrium (LD) are estimated for pairs of biallelic markers such as single nucleotide polymorphisms (SNPs) from progeny genotypes and sire haplotypes. The implementation relies on paternal half-sib families. If maternal half-sib families are used, the roles of sire/dam are swapped. Multiple families can be considered. For parameter estimation, at least one sire has to be double heterozygous at the investigated pairs of SNPs. Based on recombination rates, genetic distances between markers can be estimated. Markers with unusually large recombination rate to markers in close proximity (i.e. putatively misplaced markers) shall be discarded in this derivation. *A pipeline is available at GitHub* <https://github.com/wittenburg/hsrecombi> Hampel, Teuscher, Gomez-Raya, Doschoris, Wittenburg (2018) "Estimation of recombination rate and maternal linkage disequilibrium in half-sibs" <doi:10.3389/fgene.2018.00186>. Gomez-Raya (2012) "Maximum likelihood estimation of linkage disequilibrium in half-sib families" <doi:10.1534/genetics.111.137521>.

Construction and analysis of multivalued zero-sum matrix games over the abstract space of probability distributions, which describe the losses in each scenario of defense vs. attack action. The distributions can be compiled directly from expert opinions or other empirical data (insofar available). The package implements the methods put forth in the EU project HyRiM (Hybrid Risk Management for Utility Networks), FP7 EU Project Number 608090. The method has been published in Rass, S., König, S., Schauer, S., 2016. Decisions with Uncertain Consequences-A Total Ordering on Loss-Distributions. PLoS ONE 11, e0168583. <doi:10.1371/journal.pone.0168583>, and applied for advanced persistent thread modeling in Rass, S., König, S., Schauer, S., 2017. Defending Against Advanced Persistent Threats Using Game-Theory. PLoS ONE 12, e0168675. <doi:10.1371/journal.pone.0168675>. A volume covering the wider range of aspects of risk management, partially based on the theory implemented in the package is the book edited by S. Rass and S. Schauer, 2018. Game Theory for Security and Risk Management: From Theory to Practice. Springer, <doi:10.1007/978-3-319-75268-6>, ISBN 978-3-319-75267-9.

Computes the expectation of the number of transmissions and receptions considering a Hop-by-Hop transport model with limited number of retransmissions per packet. It provides the theoretical results shown in Palma et. al.(2016) <DOI:10.1109/TLA.2016.7555237> and also estimated values based on Monte Carlo simulations. It is also possible to consider random data and ACK probabilities.

Hard drive data: Class of data allowing the easy importation/manipulation of out of memory data sets. The data sets are located on disk but look like in-memory, the syntax for manipulation is similar to data.table'. Operations are performed "chunk-wise" behind the scene. See <https://lrberge.github.io/hdd/> for more information.

This package performs hypergraph-based setwise variable selection with false discovery rate control (Organ, Kenney & Gu, 2026, <doi:10.48550/arXiv.2606.20514>). The idea is, in addition to selecting individual predictors when there is sufficient evidence, to also test all pairs of predictors, and when there is insufficient evidence to be sure which is the true predictor, it will select possibly overlapping pairs, for which there is strong evidence that at least one is a true predictor. The method is designed to control a generalised false discovery rate, where discoveries are counted based on the number of independent sets. The function of this package is similar to the hypergraph.sizing package, but this package is optimised for faster computation in the case where we test all pairs of predictors. The package also includes functions for counting independent sets in a graph or hypergraph, either exactly or approximately. There is also a very limited function for isotonic regression, which is designed for fast computation in the specific case needed for hypergraph variable selection, rather than for general use.

This package provides a set of objects and functions for Bayes Linear emulation and history matching. Core functionality includes automated training of emulators to data, diagnostic functions to ensure suitability, and a variety of proposal methods for generating waves of points. For details on the mathematical background, there are many papers available on the topic (see references attached to function help files or the below references); for details of the functions in this package, consult the manual or help files. Iskauskas, A, et al. (2024) <doi:10.18637/jss.v109.i10>. Bower, R.G., Goldstein, M., and Vernon, I. (2010) <doi:10.1214/10-BA524>. Craig, P.S., Goldstein, M., Seheult, A.H., and Smith, J.A. (1997) <doi:10.1007/978-1-4612-2290-3_2>.

Converts among many citation formats, including BibTeX', Citeproc', Codemeta', RDF XML', RIS', Schema.org', and Citation File Format'. A low level R6 class is provided, as well as stand-alone functions for each citation format for both read and write.

Fit an array of decision-making tasks with computational models in a hierarchical Bayesian framework. Can perform hierarchical Bayesian analysis of various computational models with a single line of coding (Ahn et al., 2017) <doi:10.1162/CPSY_a_00002>.