Multivariate conditional and marginal densities, moments, cumulative distribution functions as well as binary choice and sample selection models based on Hermite polynomial approximation which was proposed and described by A. Gallant and D. W. Nychka (1987) <doi:10.2307/1913241>.

This package provides a simple and time saving multiple linear regression function (OLS) with interpretation, optional bootstrapping, effect size calculation and all tested requirements.

This package provides functions to perform dimensionality reduction for classification if the covariance matrices of the classes are unequal.

This package provides tools for the estimation of Heckman selection models with robust variance-covariance matrices. It includes functions for computing the bread and meat matrices, as well as clustered standard errors for generalized Heckman models, see Fernando de Souza Bastos and Wagner Barreto-Souza and Marc G. Genton (2022, ISSN: <https://www.jstor.org/stable/27164235>). The package also offers cluster-robust inference with sandwich estimators, and tools for handling issues related to eigenvalues in covariance matrices.

This package provides a set of tools supporting more flexible heatmaps. The graphics is grid-like using the old graphics system. The main function is heatmap.n2(), which is a wrapper around the various functions constructing individual parts of the heatmap, like sidebars, picket plots, legends etc. The function supports zooming and splitting, i.e., having (unlimited) small heatmaps underneath each other in one plot deriving from the same data set, e.g., clustered and ordered by a supervised clustering method.

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.

Code Syntax Highlighting made easy for code snippets or complete files. Whether you're documenting your data analysis or creating interactive shiny apps.

Fitting hidden Markov models using automatic differentiation and Laplace approximation, allowing for fast inference and flexible covariate effects (including random effects and smoothing splines) on model parameters. The package is described by Michelot (2025) <doi:10.18637/jss.v114.i05>.

Tests for two high-dimensional population mean vectors. The user has the option to compute the asymptotic, the permutation or the bootstrap based p-value of the test. Some references are: Chen S.X. and Qin Y.L. (2010). <doi:10.1214/09-AOS716>, Cai T.T., Liu W., and Xia Y. (2014) <doi:10.1111/rssb.12034> and Yu X., Li D., Xue L. and Li, R. (2023) <doi:10.1080/01621459.2022.2061354>.

Given a high-dimensional dataset that typically represents a cytometry dataset, and a subset of the datapoints, this algorithm outputs an hyperrectangle so that datapoints within the hyperrectangle best correspond to the specified subset. In essence, this allows the conversion of clustering algorithms outputs to gating strategies outputs.

Read, plot, manipulate and process hydro-meteorological data records (with special features for Argentina and Chile data-sets).

Test the significance of coefficients in high dimensional generalized linear models.

Using the MDL principle, it is possible to estimate parameters for a histogram-like model. The package contains the implementation of such an estimation method.

This package provides a multi-core R package that allows for the statistical modeling of multi-group multivariate mixed data using Gaussian graphical models. Combining the Gaussian copula framework with the fused graphical lasso penalty, the heteromixgm package can handle a wide variety of datasets found in various sciences. The package also includes an option to perform model selection using the AIC, BIC and EBIC information criteria, a function that plots partial correlation graphs based on the selected precision matrices, as well as simulate mixed heterogeneous data for exploratory or simulation purposes and one multi-group multivariate mixed agricultural dataset pertaining to maize yields. The package implements the methodological developments found in Hermes et al. (2024) <doi:10.1080/10618600.2023.2289545>.

Built by Hodges lab members for current and future Hodges lab members. Other individuals are welcome to use as well. Provides useful functions that the lab uses everyday to analyze various genomic datasets. Critically, only general use functions are provided; functions specific to a given technique are reserved for a separate package. As the lab grows, we expect to continue adding functions to the package to build on previous lab members code.

This package provides methods for closed testing using Simes local tests. In particular, calculates adjusted p-values for Hommel's multiple testing method, and provides lower confidence bounds for true discovery proportions. A robust but more conservative variant of the closed testing procedure that does not require the assumption of Simes inequality is also implemented. The methods have been described in detail in Goeman et al (Biometrika 106, 841-856, 2019).

This package provides functions to implement a hierarchical approach which is designed to perform joint analysis of summary statistics using the framework of Mendelian Randomization or transcriptome analysis. Reference: Lai Jiang, Shujing Xu, Nicholas Mancuso, Paul J. Newcombe, David V. Conti (2020). "A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates in a Mendelian Randomization or Transcriptome Analysis." <bioRxiv><doi:10.1101/2020.02.03.924241>.

The package allows to simulate Hawkes process both in univariate and multivariate settings. It gives functions to compute different moments of the number of jumps of the process on a given interval, such as mean, variance or autocorrelation of process jumps on time intervals separated by a lag.

Higher order likelihood inference is a promising approach for analyzing small sample size data. The holi package provides web applications for higher order likelihood inference. It currently supports linear, logistic, and Poisson generalized linear models through the rstar_glm() function, based on Pierce and Bellio (2017) <doi:10.1111/insr.12232> and likelihoodAsy'. The package offers two main features: LA_rstar(), which launches an interactive shiny application allowing users to fit models with rstar_glm() through their web browser, and sim_rstar_glm_pgsql(), which streamlines the process of launching a web-based shiny simulation application that saves results to a user-created PostgreSQL database.

Published meta-analyses routinely present one of the measures of heterogeneity introduced in Higgins and Thompson (2002) <doi:10.1002/sim.1186>. For critiquing articles it is often better to convert to another of those measures. Some conversions are provided here and confidence intervals are also available.

This package provides a deterministic, framework-agnostic Domain-Specific Language for building HTML nodes and rendering them to a string.

This package provides a generic function and a set of methods to calculate highest density intervals for a variety of classes of objects which can specify a probability density distribution, including MCMC output, fitted density objects, and functions.

This package provides a tool for Hierarchical Climate Regionalization applicable to any correlation-based clustering. It adds several features and a new clustering method (called, regional linkage) to hierarchical clustering in R ('hclust function in stats library): data regridding, coarsening spatial resolution, geographic masking, contiguity-constrained clustering, data filtering by mean and/or variance thresholds, data preprocessing (detrending, standardization, and PCA), faster correlation function with preliminary big data support, different clustering methods, hybrid hierarchical clustering, multivariate clustering (MVC), cluster validation, visualization of regionalization results, and exporting region map and mean timeseries into NetCDF-4 file. The technical details are described in Badr et al. (2015) <doi:10.1007/s12145-015-0221-7>.

This package provides methods for analysing and forecasting hierarchical and grouped time series. The available forecast methods include bottom-up, top-down, optimal combination reconciliation (Hyndman et al. 2011) <doi:10.1016/j.csda.2011.03.006>, and trace minimization reconciliation (Wickramasuriya et al. 2018) <doi:10.1080/01621459.2018.1448825>.