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r-hdlm
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public |
Mimics the lm() function found in the package stats to fit high dimensional regression models with point estimates, standard errors, and p-values. Methods for printing and summarizing the results are given.
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2025-04-22 |
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r-hdglm
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Test the significance of coefficients in high dimensional generalized linear models.
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2025-04-22 |
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r-hdf5r
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public |
'HDF5' is a data model, library and file format for storing and managing large amounts of data. This package provides a nearly feature complete, object oriented wrapper for the 'HDF5' API <https://support.hdfgroup.org/HDF5/doc/RM/RM_H5Front.html> using R6 classes. Additionally, functionality is added so that 'HDF5' objects behave very similar to their corresponding R counterparts.
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2025-04-22 |
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r-hddesign
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Determine the sample size requirement to achieve the target probability of correct classification (PCC) for studies employing high-dimensional features. The package implements functions to 1) determine the asymptotic feasibility of the classification problem; 2) compute the upper bounds of the PCC for any linear classifier; 3) estimate the PCC of three design methods given design assumptions; 4) determine the sample size requirement to achieve the target PCC for three design methods.
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2025-04-22 |
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r-hdcpdetect
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Objective: Implement new methods for detecting change points in high-dimensional time series data. These new methods can be applied to non-Gaussian data, account for spatial and temporal dependence, and detect a wide variety of change-point configurations, including changes near the boundary and changes in close proximity. Additionally, this package helps address the “small n, large p” problem, which occurs in many research contexts. This problem arises when a dataset contains changes that are visually evident but do not rise to the level of statistical significance due to the small number of observations and large number of parameters. The problem is overcome by treating the dimensions as a whole and scaling the test statistics only by its standard deviation, rather than scaling each dimension individually. Due to the computational complexity of the functions, the package runs best on datasets with a relatively large number of attributes but no more than a few hundred observations.
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2025-04-22 |
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r-hdbm
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Perform mediation analysis in the presence of high-dimensional mediators based on the potential outcome framework. High dimensional Bayesian mediation (HDBM), developed by Song et al (2018) <doi:10.1101/467399>, relies on two Bayesian sparse linear mixed models to simultaneously analyze a relatively large number of mediators for a continuous exposure and outcome assuming a small number of mediators are truly active. This sparsity assumption also allows the extension of univariate mediator analysis by casting the identification of active mediators as a variable selection problem and applying Bayesian methods with continuous shrinkage priors on the effects.
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2025-04-22 |
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r-hdbinseg
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Binary segmentation methods for detecting and estimating multiple change-points in the mean or second-order structure of high-dimensional time series as described in Cho and Fryzlewicz (2014) <doi:10.1111/rssb.12079> and Cho (2016) <doi:10.1214/16-EJS1155>.
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2025-04-22 |
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r-hbv.ianigla
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The HBV hydrological model (Bergström, S. and Lindström, G., (2015) <doi:10.1002/hyp.10510>) has been split in modules to allow the user to build his/her own model. This version was developed by the author in IANIGLA-CONICET (Instituto Argentino de Nivologia, Glaciologia y Ciencias Ambientales - Consejo Nacional de Investigaciones Cientificas y Tecnicas) for hydroclimatic studies in the Andes. HBV.IANIGLA incorporates routines for clean and debris covered glacier melt simulations.
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2025-04-22 |
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r-hawkes
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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.
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2025-04-22 |
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r-hashr
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Apply an adaptation of the SuperFastHash algorithm to any R object. Hash whole R objects or, for vectors or lists, hash R objects to obtain a set of hash values that is stored in a structure equivalent to the input. See <http://www.azillionmonkeys.com/qed/hash.html> for a description of the hash algorithm.
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2025-04-22 |
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r-hbmem
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Contains functions for fitting hierarchical versions of EVSD, UVSD, DPSD, DPSD with d' restricted to be positive, and our gamma signal detection model to recognition memory confidence-ratings data.
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2025-04-22 |
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r-harmodel
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public |
Estimation, simulation, and forecasting using the HAR model from Corsi(2009) <DOI:10.1093/jjfinec/nbp001> and extensions.
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2025-04-22 |
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r-hapassoc
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The following R functions are used for inference of trait associations with haplotypes and other covariates in generalized linear models. The functions are developed primarily for data collected in cohort or cross-sectional studies. They can accommodate uncertain haplotype phase and handle missing genotypes at some SNPs.
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2025-04-22 |
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r-hapsim
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Package for haplotype-based genotype simulations. Haplotypes are generated such that their allele frequencies and linkage disequilibrium coefficients match those estimated from an input data set.
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2025-04-22 |
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r-gwfa
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Performs Geographically Weighted Fractal Analysis (GWFA) to calculate the local fractal dimension of a set of points. GWFA mixes the Sandbox multifractal algorithm and the Geographically Weighted Regression. Unlike fractal box-counting algorithm, the sandbox algorithm avoids border effects because the boxes are adjusted on the set of points. The Geographically Weighted approach consists in applying a kernel that describes the way the neighbourhood of each estimated point is taken into account to estimate its fractal dimension. GWFA can be used to discriminate built patterns of a city, a region, or a whole country.
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2025-04-22 |
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r-guts
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Given exposure and survival time series as well as parameter values, GUTS allows for the fast calculation of the survival probabilities as well as the logarithm of the corresponding likelihood (see Albert, C., Vogel, S. and Ashauer, R. (2016) <doi:10.1371/journal.pcbi.1004978>).
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2025-04-22 |
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r-gtfsrouter
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Use 'GTFS' (General Transit Feed Specification) data for routing from nominated start and end stations, for extracting 'isochrones', and travel times from any nominated start station to all other stations.
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2025-04-22 |
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r-gte
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Generalized Turnbull's estimator proposed by Dehghan and Duchesne (2011).
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2025-04-22 |
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r-gsmoothr
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Tools rewritten in C for various smoothing tasks
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2025-04-22 |
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r-gscounts
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Design and analysis of group sequential designs for negative binomial outcomes, as described by T Mütze, E Glimm, H Schmidli, T Friede (2018) <doi:10.1177/0962280218773115>.
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2025-04-22 |
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r-grr
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Alternative implementations of some base R functions, including sort, order, and match. Functions are simplified but can be faster or have other advantages.
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2025-04-22 |
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r-groupsubsetselection
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Group subset selection for linear regression models is provided in this package. Given response variable, and explanatory variables, which are organised in groups, group subset selection selects a small number of groups to explain response variable linearly using least squares.
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2025-04-22 |
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r-grf
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Forest-based statistical estimation and inference. GRF provides non-parametric methods for heterogeneous treatment effects estimation (optionally using right-censored outcomes, multiple treatment arms or outcomes, or instrumental variables), as well as least-squares regression, quantile regression, and survival regression, all with support for missing covariates.
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2025-04-22 |
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r-groupremmap
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An implementation of the GroupRemMap penalty for fitting regularized multivariate response regression models under the high-dimension-low-sample-size setting. When the predictors naturally fall into groups, the GroupRemMap penalty encourages procedure to select groups of predictors, while control for the overall sparsity of the final model.
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2025-04-22 |
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r-gridtext
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Provides support for rendering of formatted text using 'grid' graphics. Text can be formatted via a minimal subset of 'Markdown', 'HTML', and inline 'CSS' directives, and it can be rendered both with and without word wrap.
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2025-04-22 |
