Estimates survey indicators using complex survey designs. Supports mean, proportion, and ratio estimation with multi-stage stratified sampling, weights, and finite population correction. The output is designed to be comparable to results from SPSS (Statistical Package for the Social Sciences) Complex Samples procedures.

Simulate populations and track haplotypes over generations to evaluate population admixture. The HAPTRACE supports customisable population parameters, including size, number of markers, mutation rates and recombination.

An S4 class and several functions which utilize internally stored datasets and gauging data enable 1d water level interpolation. The S4 class (WaterLevelDataFrame) structures the computation and visualisation of 1d water level information along the German federal waterways Elbe and Rhine. hyd1d delivers 1d water level data - extracted from the FLYS database - and validated gauging data - extracted from the hydrological database WISKI7 - package-internally. For computations near real time gauging data are queried externally from the PEGELONLINE REST API <https://pegelonline.wsv.de/webservice/dokuRestapi>.

Generates HIDECAN plots that summarise and combine the results of genome-wide association studies (GWAS) and transcriptomics differential expression analyses (DE), along with manually curated candidate genes of interest. The HIDECAN plot is presented in Angelin-Bonnet et al. (2023) (currently in review).

Reliability Analysis and Maintenance Optimization using Hidden Markov Models (HMM). The use of HMMs to model the state of a system which is not directly observable and instead certain indicators (signals) of the true situation are provided via a control system. A hidden model can provide key information about the system dependability, such as the reliability of the system and related measures. An estimation procedure is implemented based on the Baum-Welch algorithm. Classical structures such as K-out-of-N systems and Shock models are illustrated. Finally, the maintenance of the system is considered in the HMM context and two functions for new preventive maintenance strategies are considered. Maintenance efficiency is measured in terms of expected cost. Methods are described in Gamiz, Limnios, and Segovia-Garcia (2023) <doi:10.1016/j.ejor.2022.05.006>.

Health Calculator helps to find different parameters like basal metabolic rate, body mass index etc. related to fitness and health of a person.

This package provides flexible maximum likelihood estimation and inference for Hidden Markov Models (HMMs) and Hidden Semi-Markov Models (HSMMs), as well as the underlying systems in which they operate. The package supports a wide range of observation and dwell-time distributions, offering a flexible modelling framework suitable for diverse practical data. Efficient implementations of the forward-backward and Viterbi algorithms are provided via Rcpp for enhanced computational performance. Additional functionality includes model simulation, residual analysis, non-initialised estimation, local and global decoding, calculation of diverse information criteria, computation of confidence intervals using parametric bootstrap methods, numerical covariance matrix estimation, and comprehensive visualisation functions for interpreting the data-generating processes inferred from the models. Methods follow standard approaches described by Guédon (2003) <doi:10.1198/1061860032030>, Zucchini and MacDonald (2009, ISBN:9781584885733), and O'Connell and Højsgaard (2011) <doi:10.18637/jss.v039.i04>.

High throughput toxicokinetics ("HTTK") is the combination of 1) chemical-specific in vitro measurements or in silico predictions and 2) generic mathematical models, to predict absorption, distribution, metabolism, and excretion by the body. HTTK methods have been described by Pearce et al. (2017) (<doi:10.18637/jss.v079.i04>) and Breen et al. (2021) (<doi:10.1080/17425255.2021.1935867>). Here we provide examples (vignettes) applying HTTK to solve various problems in bioinformatics, toxicology, and exposure science. In accordance with Davidson-Fritz et al. (2025) (<doi:10.1371/journal.pone.0321321>), whenever a new HTTK model is developed, the code to generate the figures evaluating that model is added as a new vignettte.

Seed germinates through the physical process of water uptake by dry seed driven by the difference in water potential between the seed and the water. There exists seed-to-seed variability in the base seed water potential. Hence, there is a need for a distribution such that a viable seed with its base seed water potential germinates if and only if the soil water potential is more than the base seed water potential. This package estimates the stress tolerance and uniformity parameters of the seed lot for germination under various temperatures by using the hydro-time model of counts of germinated seeds under various water potentials. The distribution of base seed water potential has been considered to follow Normal, Logistic and Extreme value distribution. The estimated proportion of germinated seeds along with the estimates of stress and uniformity parameters are obtained using a generalised linear model. The significance test of the above parameters for within and between temperatures is also performed in the analysis. Details can be found in Kebreab and Murdoch (1999) <doi:10.1093/jxb/50.334.655> and Bradford (2002) <https://www.jstor.org/stable/4046371>.

Calculates a sizing function based on the number of independent sets in the rejected hypergraph (Organ, Kenney & Gu, 2026, <doi:10.48550/arXiv.2606.20514>). The sizing function is designed to be used with the GLSUP package.

This package provides semiparametric sufficient dimension reduction for central mean subspaces for heterogeneous data defined by combinations of binary factors (such as chronic conditions). Subspaces are estimated to be hierarchically nested to respect the structure of subpopulations with overlapping characteristics. This package is an implementation of the proposed methodology of Huling and Yu (2021) <doi:10.1111/biom.13546>.

Compute 21 summary measures of health inequality and its corresponding confidence intervals for ordered and non-ordered dimensions using disaggregated data. Measures for ordered dimensions (e.g., Slope Index of Inequality, Absolute Concentration Index) also accept individual and survey data.

This package provides functions to estimate population genetics summary statistics from haplo-diploid systems, where one sex is haploid and the other diploid (e.g. Hymenoptera insects). It implements a theoretical model assuming equal sex ratio, random mating, no selection, no mutation, and no gene flow, deriving expected genotype frequencies for both sexes under these equilibrium conditions. The package includes windowed calculations (operating over genomic sliding windows from VCF input) for allele and genotype frequencies, the inbreeding coefficient (Fis), pairwise Fst, Nei's H (gene diversity), Watterson's Theta, and sex-specific reference allele frequencies. Most statistics are agnostic to ploidy, allowing the package to be applied to both strictly haplo-diploid and fully diploid systems.

This package provides a simple implementation of doughnut plots - pie charts with a blank center. The package is named after Homer Simpson - arguably the best-known lover of doughnuts.

Miscellaneous convenience functions and wrapper functions to convert frequencies between Hz, semitones, mel and Bark, to create a matrix of dummy columns from a factor, to determine whether x lies in range [a,b], and to add a bracketed line to an existing plot. This package also contains an example data set of a stratified sample of 80 talkers of Dutch.

Spatial heterogeneity can be specified in various ways. hspm is an ambitious project that aims at implementing various methodologies to control for heterogeneity in spatial models. The current version of hspm deals with spatial and (non-spatial) regimes models. In particular, the package allows to estimate a general spatial regimes model with additional endogenous variables, specified in terms of a spatial lag of the dependent variable, the spatially lagged regressors, and, potentially, a spatially autocorrelated error term. Spatial regime models are estimated by instrumental variables and generalized methods of moments (see Arraiz et al., (2010) <doi:10.1111/j.1467-9787.2009.00618.x>, Bivand and Piras, (2015) <doi:10.18637/jss.v063.i18>, Drukker et al., (2013) <doi:10.1080/07474938.2013.741020>, Kelejian and Prucha, (2010) <doi:10.1016/j.jeconom.2009.10.025>).

Estimation of high-dimensional multi-response regression with heterogeneous noises under Heterogeneous group square-root Lasso penalty. For details see: Ren, Z., Kang, Y., Fan, Y. and Lv, J. (2018)<arXiv:1606.03803>.

Allows to estimate and test high-dimensional mediation effects based on advanced mediator screening and penalized regression techniques. Methods used in the package refer to Zhang H, Zheng Y, Hou L, Liu L, HIMA: An R Package for High-Dimensional Mediation Analysis. Journal of Data Science. (2025). <doi:10.6339/25-JDS1192>.

Core set of low-level utilities common across the hubverse'. Used to interact with hubverse schema, Hub configuration files and model outputs and designed to be primarily used internally by other hubverse packages. See Reich et al. (2022) <doi:10.2105/AJPH.2022.306831> for an overview of Collaborative Hubs.

Calculate expected relative risk and proportion protected assuming normally distributed log10 transformed antibody dose for a several component vaccine. Uses Hill models for each component which are combined under Bliss independence. See Saul and Fay, 2007 <DOI:10.1371/journal.pone.0000850>.

This package contains various functions for data analysis, notably helpers and diagnostics for Bayesian modelling using Stan.

Several handy plots for quickly looking at the relationship between two numeric vectors of equal length. Quickly visualize scatter plots, residual plots, qq-plots, box plots, confidence intervals, and prediction intervals.

This package provides methods for testing main and interaction effects in possibly high-dimensional parametric or nonparametric repeated measures in factorial designs. The observations of the subjects are assumed to be multivariate normal if using the parametric test. The nonparametric version tests with regard to nonparametric relative effects (based on pseudo-ranks). It is possible to use up to 2 whole- and 3 subplot factors. See Happ et al. (2017, <doi:10.1080/15598608.2017.1307792>) for details.

The holonomic gradient method (HGM, hgm) gives a way to evaluate normalization constants of unnormalized probability distributions by utilizing holonomic systems of differential or difference equations. The holonomic gradient descent (HGD, hgd) gives a method to find maximal likelihood estimates by utilizing the HGM.