State-of-the-art Multi-Objective Particle Swarm Optimiser (MOPSO), based on the algorithm developed by Lin et al. (2018) <doi:10.1109/TEVC.2016.2631279> with improvements described by Marinao-Rivas & Zambrano-Bigiarini (2020) <doi:10.1109/LA-CCI48322.2021.9769844>. This package is inspired by and closely follows the philosophy of the single objective hydroPSO R package ((Zambrano-Bigiarini & Rojas, 2013) <doi:10.1016/j.envsoft.2013.01.004>), and can be used for global optimisation of non-smooth and non-linear R functions and R-base models (e.g., TUWmodel', GR4J', GR6J'). However, the main focus of hydroMOPSO is optimising environmental and other real-world models that need to be run from the system console (e.g., SWAT+'). hydroMOPSO communicates with the model to be optimised through its input and output files, without requiring modifying its source code. Thanks to its flexible design and the availability of several fine-tuning options, hydroMOPSO can tackle a wide range of multi-objective optimisation problems (e.g., multi-objective functions, multiple model variables, multiple periods). Finally, hydroMOPSO is designed to run on multi-core machines or network clusters, to alleviate the computational burden of complex models with long execution time.

This package provides methods to test whether time series is consistent with white noise. Two new tests based on Haar wavelets and general wavelets described by Nason and Savchev (2014) <doi:10.1002/sta4.69> are provided and, for comparison purposes this package also implements the B test of Bartlett (1967) <doi:10.2307/2333850>. Functionality is provided to compute an approximation to the theoretical power of the general wavelet test in the case of general ARMA alternatives.

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>.

Estimate parameters of the hysteretic threshold autoregressive (HysTAR) model, using conditional least squares. In addition, you can generate time series data from the HysTAR model. For details, see Li, Guan, Li and Yu (2015) <doi:10.1093/biomet/asv017>.

This package provides functions for combining model outputs (e.g. predictions or estimates) from multiple models into an aggregated ensemble model output.

This package provides a protocol that facilitates the processing and analysis of Hydrogen-Deuterium Exchange Mass Spectrometry data using p-value statistics and Critical Interval analysis. It provides a pipeline for analyzing data from HDXExaminer (Sierra Analytics, Trajan Scientific), automating matching and comparison of protein states through Welch's T-test and the Critical Interval statistical framework. Additionally, it simplifies data export, generates PyMol scripts, and ensures calculations meet publication standards. HDXBoxeR assists in various aspects of hydrogen-deuterium exchange data analysis, including reprocessing data, calculating parameters, identifying significant peptides, generating plots, and facilitating comparison between protein states. For details check papers by Hageman and Weis (2019) <doi:10.1021/acs.analchem.9b01325> and Masson et al. (2019) <doi:10.1038/s41592-019-0459-y>. HDXBoxeR citation: Janowska et al. (2024) <doi:10.1093/bioinformatics/btae479>.

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.

Perform Hi-C data differential analysis based on pixel-level differential analysis and a post hoc inference strategy to quantify signal in clusters of pixels. Clusters of pixels are obtained through a connectivity-constrained two-dimensional hierarchical clustering.

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>.

Hedgehog will eat all your bugs. Hedgehog is a property-based testing package in the spirit of QuickCheck'. With Hedgehog', one can test properties of their programs against randomly generated input, providing far superior test coverage compared to unit testing. One of the key benefits of Hedgehog is integrated shrinking of counterexamples, which allows one to quickly find the cause of bugs, given salient examples when incorrect behaviour occurs.

Inference concerning equilibrium and random mating in autopolyploids. Methods are available to test for equilibrium and random mating at any even ploidy level (>2) in the presence of double reduction at biallelic loci. For autopolyploid populations in equilibrium, methods are available to estimate the degree of double reduction. We also provide functions to calculate genotype frequencies at equilibrium, or after one or several rounds of random mating, given rates of double reduction. The main function is hwefit(). This material is based upon work supported by the National Science Foundation under Grant No. 2132247. The opinions, findings, and conclusions or recommendations expressed are those of the author and do not necessarily reflect the views of the National Science Foundation. For details of these methods, see Gerard (2023a) <doi:10.1111/biom.13722> and Gerard (2023b) <doi:10.1111/1755-0998.13856>.

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.

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>.

Calculates a suite of hydrologic indices for daily time series data that are widely used in hydrology and stream ecology.

This package provides a collection of utilities that support creation of network attributes for hydrologic networks. Methods and algorithms implemented are documented in Moore et al. (2019) <doi:10.3133/ofr20191096>), Cormen and Leiserson (2022) <ISBN:9780262046305> and Verdin and Verdin (1999) <doi:10.1016/S0022-1694(99)00011-6>.

This package provides a suite of diagnostic tools for hierarchical (multilevel) linear models. The tools include not only leverage and traditional deletion diagnostics (Cook's distance, covratio, covtrace, and MDFFITS) but also convenience functions and graphics for residual analysis. Models can be fit using either lmer in the lme4 package or lme in the nlme package.

The HMS (Hierarchic Memetic Strategy) is a composite global optimization strategy consisting of a multi-population evolutionary strategy and some auxiliary methods. The HMS makes use of a dynamically-evolving data structure that provides an organization among the component populations. It is a tree with a fixed maximal height and variable internal node degree. Each component population is governed by a particular evolutionary engine. This package provides a simple R implementation with examples of using different genetic algorithms as the population engines. References: J. Sawicki, M. Å oÅ , M. SmoÅ ka, J. Alvarez-Aramberri (2022) <doi:10.1007/s11047-020-09836-w>.

This package provides a self-contained, static build of the HDF5 (Hierarchical Data Format 5) C library (release 2.1.1) for R package developers. Designed for use in the LinkingTo field, it enables zero-dependency integration by building the library entirely from source during installation. Additionally, it compiles and internally links a comprehensive suite of advanced compression filters and their HDF5 plugins (Zstd, LZ4, Blosc/Blosc2, Snappy, ZFP, Bzip2, LZF, Bitshuffle, szip, and gzip). These plugins are integrated out-of-the-box, allowing downstream packages to utilize high-performance compression directly through the standard HDF5 API while keeping the underlying third-party headers fully encapsulated. HDF5 is developed by The HDF Group <https://www.hdfgroup.org/>.

Historical borrowing in clinical trials can improve precision and operating characteristics. This package supports a hierarchical model and a mixture model to borrow historical control data from other studies to better characterize the control response of the current study. It also quantifies the amount of borrowing through benchmark models (independent and pooled). Some of the methods are discussed by Viele et al. (2013) <doi:10.1002/pst.1589>.

Focuses on data processing and visualization in hydrology and climate forecasting. Main function includes data extraction, data downscaling, data resampling, gap filler of precipitation, bias correction of forecasting data, flexible time series plot, and spatial map generation. It is a good pre- processing and post-processing tool for hydrological and hydraulic modellers.

Holistic generalized linear models (HGLMs) extend generalized linear models (GLMs) by enabling the possibility to add further constraints to the model. The holiglm package simplifies estimating HGLMs using convex optimization. Additional information about the package can be found in the reference manual, the README and the accompanying paper <doi:10.18637/jss.v108.i07>.

This package provides two functions that implement the one-sided and two-sided versions of the Hodrick-Prescott filter. The one-sided version is a Kalman filter-based implementation, whereas the two- sided version uses sparse matrices for improved efficiency. References: Hodrick, R. J., and Prescott, E. C. (1997) <doi:10.2307/2953682> Mcelroy, T. (2008) <doi:10.1111/j.1368-423X.2008.00230.x> Meyer-Gohde, A. (2010) <https://ideas.repec.org/c/dge/qmrbcd/181.html> For more references, see the vignette.

Wrapper for Stan that offers a number of in-built models to implement a hierarchical Bayesian longitudinal model for repeat observation data. Model choice selects the differential equation that is fit to the observations. Single and multi-individual models are available. O'Brien et al. (2024) <doi:10.1111/2041-210X.14463>.

Given one or multiple paths to files produced by a PULSE multi-channel or a PULSE one-channel system (<https://electricblue.eu/pulse>) from a single experiment: [1] check pulse files for inconsistencies and read/merge all data, [2] split across time windows, [3] interpolate and smooth to optimize the dataset, [4] compute the heart rate frequency for each channel/window, and [5] facilitate quality control, summarising and plotting. Heart rate frequency is calculated using the Automatic Multi-scale Peak Detection algorithm proposed by Felix Scholkmann and team. For more details see Scholkmann et al (2012) <doi:10.3390/a5040588>. Check original code at <https://github.com/ig248/pyampd>. ElectricBlue is a non-profit technology transfer startup creating research-oriented solutions for the scientific community (<https://electricblue.eu>).