In medical research, supervised heterogeneity analysis has important implications. Assume that there are two types of features. Using both types of features, our goal is to conduct the first supervised heterogeneity analysis that satisfies a hierarchical structure. That is, the first type of features defines a rough structure, and the second type defines a nested and more refined structure. A penalization approach is developed, which has been motivated by but differs significantly from penalized fusion and sparse group penalization. Reference: Ren, M., Zhang, Q., Zhang, S., Zhong, T., Huang, J. & Ma, S. (2022). "Hierarchical cancer heterogeneity analysis based on histopathological imaging features". Biometrics, <doi:10.1111/biom.13426>.

Simulate haplotypes through meioses. Allows specification of population parameters.

Estimation procedures and goodness-of-fit test for several Markov regime switching models and mixtures of bivariate copula models. The goodness-of-fit test is based on a Cramer-von Mises statistic and uses Rosenblatt's transform and parametric bootstrap to estimate the p-value. The proposed methodologies are described in Nasri, Remillard and Thioub (2020) <doi:10.1002/cjs.11534>.

Several functions are provided to harmonize CN8 (Combined Nomenclature 8 digits) and PC8 (Production Communautaire 8 digits) product codes over time and the classification systems HS6 and BEC. Harmonization of CN8 codes are possible by default from 1995 to 2022 and of PC8 from 2001 to 2021, respectively.

Kernel density estimation with hexagonal grid for bivariate data. Hexagonal grid has many beneficial properties like equidistant neighbours and less edge bias, making it better for spatial analyses than the more commonly used rectangular grid. Carr, D. B. et al. (1987) <doi:10.2307/2289444>. Diggle, P. J. (2010) <doi:10.1201/9781420072884>. Hill, B. (2017) <https://blog.bruce-hill.com/meandering-triangles>. Jones, M. C. (1993) <doi:10.1007/BF00147776>.

This package provides functions implementing change point detection methods using the maximum pairwise Bayes factor approach. Additionally, the package includes tools for generating simulated datasets for comparing and evaluating change point detection techniques.

This package implements an estimation method for Hawkes processes when count data are only observed in discrete time, using a spectral approach derived from the Bartlett spectrum, see Cheysson and Lang (2020) <arXiv:2003.04314>. Some general use functions for Hawkes processes are also included: simulation of (in)homogeneous Hawkes process, maximum likelihood estimation, residual analysis, etc.

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.

This package implements the Hatemi-J (2008) cointegration test which allows for two unknown structural breaks (regime shifts) in the cointegrating relationship. The test provides three test statistics: ADF* (Augmented Dickey-Fuller), Zt* (Phillips-Perron Z_t), and Za* (Phillips-Perron Z_alpha), along with endogenously determined break dates. Critical values are based on simulations from Hatemi-J (2008) <doi:10.1007/s00181-007-0175-9>.

Can be used for paternity and maternity assignment and outperforms conventional methods where closely related individuals occur in the pool of possible parents. The method compares the genotypes of offspring with any combination of potentials parents and scores the number of mismatches of these individuals at bi-allelic genetic markers (e.g. Single Nucleotide Polymorphisms). It elaborates on a prior exclusion method based on the Homozygous Opposite Test (HOT; Huisman 2017 <doi:10.1111/1755-0998.12665>) by introducing the additional exclusion criterion HIPHOP (Homozygous Identical Parents, Heterozygous Offspring are Precluded; Cockburn et al., in revision). Potential parents are excluded if they have more mismatches than can be expected due to genotyping error and mutation, and thereby one can identify the true genetic parents and detect situations where one (or both) of the true parents is not sampled. Package hiphop can deal with (a) the case where there is contextual information about parentage of the mother (i.e. a female has been seen to be involved in reproductive tasks such as nest building), but paternity is unknown (e.g. due to promiscuity), (b) where both parents need to be assigned, because there is no contextual information on which female laid eggs and which male fertilized them (e.g. polygynandrous mating system where multiple females and males deposit young in a common nest, or organisms with external fertilisation that breed in aggregations). For details: Cockburn, A., Penalba, J.V.,Jaccoud, D.,Kilian, A., Brouwer, L., Double, M.C., Margraf, N., Osmond, H.L., van de Pol, M. and Kruuk, L.E.B. (in revision). HIPHOP: improved paternity assignment among close relatives using a simple exclusion method for bi-allelic markers. Molecular Ecology Resources, DOI to be added upon acceptance.

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.

This package provides a user-friendly interface for the Hierarchical Data Format 5 ('HDF5') library designed to "just work." It bundles the necessary system libraries to ensure easy installation on all platforms. Features smart defaults that automatically map R objects (vectors, matrices, data frames) to efficient HDF5 types, removing the need to manage low-level details like dataspaces or property lists. Uses the HDF5 library developed by The HDF Group <https://www.hdfgroup.org/>.

Generates a fit plot for diagnosing misspecification in models of binary dependent variables, and calculates the related heatmap fit statistic described in Esarey and Pierce (2012) <DOI:10.1093/pan/mps026>.

This code provides a method to fit the hidden compact representation model as well as to identify the causal direction on discrete data. We implement an effective solution to recover the above hidden compact representation under the likelihood framework. Please see the Causal Discovery from Discrete Data using Hidden Compact Representation from NIPS 2018 by Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang and Zhifeng Hao (2018) <https://nips.cc/Conferences/2018/Schedule?showEvent=11274> for a description of some of our methods.

This package provides a handy collection of utility functions designed to aid in package development, plotting and scientific research. Package development functionalities includes among others tools such as cross-referencing package imports with the description file, analysis of redundant package imports, editing of the description file and the creation of package badges for GitHub. Some of the other functionalities include automatic package installation and loading, plotting points without overlap, creating nice breaks for plots, overview tables and many more handy utility functions.

This package provides a collection of functions for sampling and simulating 3D surfaces and objects and estimating metrics like rugosity, fractal dimension, convexity, sphericity, circularity, second moments of area and volume, and more.

Offers methods for visualising, modelling, and forecasting high-dimensional functional time series, also known as functional panel data. Documentation about hdftsa is initially provided via the paper by Cristian F. Jimenez-Varon, Ying Sun and Han Lin Shang (2024, Journal of Computational and Graphical Statistics).

Empirical value of the Hellinger correlation, a measure of dependence between two continuous random variables. More details can be found in Geenens and Lafaye De Micheaux (2019) <arXiv:1810.10276v4>.

Collection of functions to help retrieving data from Hub'Eau the free and public French National APIs on water <https://hubeau.eaufrance.fr/>.

This is a collection of functions for converting coordinates between WGS84UTM, WGS84GEO, HK80UTM, HK80GEO and HK1980GRID Coordinate Systems used in Hong Kong SAR, based on the algorithms described in Explanatory Notes on Geodetic Datums in Hong Kong by Survey and Mapping Office Lands Department, Hong Kong Government (1995).

High level functions for hyperplane fitting (hyper.fit()) and visualising (hyper.plot2d() / hyper.plot3d()). In simple terms this allows the user to produce robust 1D linear fits for 2D x vs y type data, and robust 2D plane fits to 3D x vs y vs z type data. This hyperplane fitting works generically for any N-1 hyperplane model being fit to a N dimension dataset. All fits include intrinsic scatter in the generative model orthogonal to the hyperplane.

This package provides a Bayesian household transmission model to estimate household transmission dynamics, with accounting for infection from community and tertiary cases.

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

An implementation of the nonnegative garrote method that incorporates hierarchical relationships among variables. The core function, HiGarrote(), offers an automated approach for analyzing experiments while respecting hierarchical structures among effects. For methodological details, refer to Yu and Joseph (2025) <doi:10.1080/00224065.2025.2513508>. This work is supported by U.S. National Science Foundation grant DMS-2310637.