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      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
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/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/

Enter the query into the form above. You can look for specific version of a package by using @ symbol like this: gcc@10.

API method:

GET /api/packages?search=hello&page=1&limit=20

where search is your query, page is a page number and limit is a number of items on a single page. Pagination information (such as a number of pages and etc) is returned in response headers.

If you'd like to join our channel webring send a patch to ~whereiseveryone/toys@lists.sr.ht adding your channel as an entry in channels.scm.


r-hyperbolicdea 1.0.2
Propagated dependencies: r-nloptr@2.2.1 r-lpsolveapi@5.5.2.0-17.14 r-foreach@1.5.2 r-dplyr@1.1.4 r-doparallel@1.0.17 r-benchmarking@0.33
Channel: guix-cran
Location: guix-cran/packages/h.scm (guix-cran packages h)
Home page: https://cran.r-project.org/package=hyperbolicDEA
Licenses: Expat
Build system: r
Synopsis: Hyperbolic DEA Estimation
Description:

This package implements Data Envelopment Analysis (DEA) with a hyperbolic orientation using a non-linear programming solver. It enables flexible estimations with weight restrictions, non-discretionary variables, and a generalized distance function. Additionally, it allows for the calculation of slacks and super-efficiency scores. The methods are detailed in à ttl et al. (2023), <doi:10.1016/j.dajour.2023.100343>. Furthermore, the package provides a non-linear profitability estimation built upon the DEA framework.

r-hyd1d 0.5.4
Propagated dependencies: r-rdpack@2.6.4 r-httr2@1.2.1 r-curl@7.0.0
Channel: guix-cran
Location: guix-cran/packages/h.scm (guix-cran packages h)
Home page: https://hyd1d.bafg.de
Licenses: GPL 2+
Build system: r
Synopsis: 1d Water Level Interpolation along the Rivers Elbe and Rhine
Description:

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>.

r-haplotyper 0.1
Channel: guix-cran
Location: guix-cran/packages/h.scm (guix-cran packages h)
Home page: https://cran.r-project.org/package=haplotyper
Licenses: GPL 3
Build system: r
Synopsis: Tool for Clustering Genotypes in Haplotypes
Description:

Function to identify haplotypes within QTL (Quantitative Trait Loci). One haplotype is a combination of SNP (Single Nucleotide Polymorphisms) within the QTL. This function groups together all individuals of a population with the same haplotype. Each group contains individual with the same allele in each SNP, whether or not missing data. Thus, haplotyper groups individuals, that to be imputed, have a non-zero probability of having the same alleles in the entire sequence of SNP's. Moreover, haplotyper calculates such probability from relative frequencies.

r-hippie 0.1.0
Propagated dependencies: r-sourcetools@0.1.7-1 r-rstudioapi@0.17.1
Channel: guix-cran
Location: guix-cran/packages/h.scm (guix-cran packages h)
Home page: https://github.com/crew102/hippie
Licenses: Expat
Build system: r
Synopsis: Hippie Code Completion in 'RStudio'
Description:

An RStudio Addin for Hippie Expand (AKA Hippie Code Completion or Cyclic Expand Word). This type of completion searches for matching tokens within the user's current source editor file, regardless of file type. By searching only within the current source file, hippie offers a fast way to identify and insert completions that appear around the user's cursor.

r-hgmnd 0.1.0
Propagated dependencies: r-genscore@1.0.2.2
Channel: guix-cran
Location: guix-cran/packages/h.scm (guix-cran packages h)
Home page: https://cran.r-project.org/package=HGMND
Licenses: GPL 3
Build system: r
Synopsis: Heterogeneous Graphical Model for Non-Negative Data
Description:

Graphical model is an informative and powerful tool to explore the conditional dependence relationships among variables. The traditional Gaussian graphical model and its extensions either have a Gaussian assumption on the data distribution or assume the data are homogeneous. However, there are data with complex distributions violating these two assumptions. For example, the air pollutant concentration records are non-negative and, hence, non-Gaussian. Moreover, due to climate changes, distributions of these concentration records in different months of a year can be far different, which means it is uncertain whether datasets from different months are homogeneous. Methods with a Gaussian or homogeneous assumption may incorrectly model the conditional dependence relationships among variables. Therefore, we propose a heterogeneous graphical model for non-negative data (HGMND) to simultaneously cluster multiple datasets and estimate the conditional dependence matrix of variables from a non-Gaussian and non-negative exponential family in each cluster.

r-hdmfa 0.1.1
Propagated dependencies: r-rspectra@0.16-2 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/h.scm (guix-cran packages h)
Home page: https://cran.r-project.org/package=HDMFA
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: High-Dimensional Matrix Factor Analysis
Description:

High-dimensional matrix factor models have drawn much attention in view of the fact that observations are usually well structured to be an array such as in macroeconomics and finance. In addition, data often exhibit heavy-tails and thus it is also important to develop robust procedures. We aim to address this issue by replacing the least square loss with Huber loss function. We propose two algorithms to do robust factor analysis by considering the Huber loss. One is based on minimizing the Huber loss of the idiosyncratic error's Frobenius norm, which leads to a weighted iterative projection approach to compute and learn the parameters and thereby named as Robust-Matrix-Factor-Analysis (RMFA), see the details in He et al. (2023)<doi:10.1080/07350015.2023.2191676>. The other one is based on minimizing the element-wise Huber loss, which can be solved by an iterative Huber regression algorithm (IHR), see the details in He et al. (2023) <arXiv:2306.03317>. In this package, we also provide the algorithm for alpha-PCA by Chen & Fan (2021) <doi:10.1080/01621459.2021.1970569>, the Projected estimation (PE) method by Yu et al. (2022)<doi:10.1016/j.jeconom.2021.04.001>. In addition, the methods for determining the pair of factor numbers are also given.

r-hbsae 1.2
Propagated dependencies: r-matrix@1.7-4
Channel: guix-cran
Location: guix-cran/packages/h.scm (guix-cran packages h)
Home page: https://cran.r-project.org/package=hbsae
Licenses: GPL 3
Build system: r
Synopsis: Hierarchical Bayesian Small Area Estimation
Description:

This package provides functions to compute small area estimates based on a basic area or unit-level model. The model is fit using restricted maximum likelihood, or in a hierarchical Bayesian way. In the latter case numerical integration is used to average over the posterior density for the between-area variance. The output includes the model fit, small area estimates and corresponding mean squared errors, as well as some model selection measures. Additional functions provide means to compute aggregate estimates and mean squared errors, to minimally adjust the small area estimates to benchmarks at a higher aggregation level, and to graphically compare different sets of small area estimates.

r-highmean 3.0
Propagated dependencies: r-mvtnorm@1.3-3 r-mnormt@2.1.1 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/h.scm (guix-cran packages h)
Home page: https://cran.r-project.org/package=highmean
Licenses: GPL 2+
Build system: r
Synopsis: Two-Sample Tests for High-Dimensional Mean Vectors
Description:

This package provides various tests for comparing high-dimensional mean vectors in two sample populations.

r-hqm 2.0
Propagated dependencies: r-timeroc@0.4 r-survival@3.8-3 r-pec@2025.06.24 r-nlme@3.1-168 r-jm@1.5-2
Channel: guix-cran
Location: guix-cran/packages/h.scm (guix-cran packages h)
Home page: https://cran.r-project.org/package=HQM
Licenses: GPL 2+
Build system: r
Synopsis: Superefficient Estimation of Future Conditional Hazards Based on Marker Information
Description:

This package provides univariate and indexed (multivariate) nonparametric smoothed kernel estimators for the future conditional hazard rate function when time-dependent covariates are present, a bandwidth selector for the estimator's implementation and pointwise and uniform confidence bands. Methods used in the package refer to Bagkavos, Isakson, Mammen, Nielsen and Proust-Lima (2025) <doi:10.1093/biomet/asaf008>.

r-hdivar 1.0.2
Propagated dependencies: r-lpsolve@5.6.23 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/h.scm (guix-cran packages h)
Home page: https://cran.r-project.org/package=hdiVAR
Licenses: GPL 2+
Build system: r
Synopsis: Statistical Inference for Noisy Vector Autoregression
Description:

The model is high-dimensional vector autoregression with measurement error, also known as linear gaussian state-space model. Provable sparse expectation-maximization algorithm is provided for the estimation of transition matrix and noise variances. Global and simultaneous testings are implemented for transition matrix with false discovery rate control. For more information, see the accompanying paper: Lyu, X., Kang, J., & Li, L. (2023). "Statistical inference for high-dimensional vector autoregression with measurement error", Statistica Sinica.

r-htsr 2.1.6
Propagated dependencies: r-writexls@6.8.0 r-waiter@0.2.5-1.927501b r-tibble@3.3.0 r-stringr@1.6.0 r-shinyfiles@0.9.3 r-shiny@1.11.1 r-rsqlite@2.4.4 r-readxl@1.4.5 r-readr@2.1.6 r-rcpp@1.1.0 r-purrr@1.2.0 r-lubridate@1.9.4 r-ggplot2@4.0.1 r-fs@1.6.6 r-dplyr@1.1.4 r-dbi@1.2.3
Channel: guix-cran
Location: guix-cran/packages/h.scm (guix-cran packages h)
Home page: https://github.com/p-chevallier/htsr
Licenses: GPL 2
Build system: r
Synopsis: Hydro-Meteorology Time-Series
Description:

This package provides functions for the management and treatment of hydrology and meteorology time-series stored in a Sqlite data base.

r-hdci 1.0-2
Propagated dependencies: r-slam@0.1-55 r-mvtnorm@1.3-3 r-matrix@1.7-4 r-lattice@0.22-7 r-iterators@1.0.14 r-glmnet@4.1-10 r-foreach@1.5.2 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/h.scm (guix-cran packages h)
Home page: https://cran.r-project.org/package=HDCI
Licenses: GPL 2
Build system: r
Synopsis: High Dimensional Confidence Interval Based on Lasso and Bootstrap
Description:

Fits regression models on high dimensional data to estimate coefficients and use bootstrap method to obtain confidence intervals. Choices for regression models are Lasso, Lasso+OLS, Lasso partial ridge, Lasso+OLS partial ridge.

r-hdsinrdata 0.3.0
Channel: guix-cran
Location: guix-cran/packages/h.scm (guix-cran packages h)
Home page: https://cran.r-project.org/package=HDSinRdata
Licenses: FSDG-compatible
Build system: r
Synopsis: Data for the 'Mastering Health Data Science Using R' Online Textbook
Description:

This package contains ten datasets used in the chapters and exercises of Paul, Alice (2023) "Health Data Science in R" <https://alicepaul.github.io/health-data-science-using-r/>.

r-hettreatreg 0.1.0
Channel: guix-cran
Location: guix-cran/packages/h.scm (guix-cran packages h)
Home page: https://github.com/tslocz/hettreatreg
Licenses: GPL 2
Build system: r
Synopsis: Heterogeneous Treatment Effects in Regression Analysis
Description:

Computes diagnostics for linear regression when treatment effects are heterogeneous. The output of hettreatreg represents ordinary least squares (OLS) estimates of the effect of a binary treatment as a weighted average of the average treatment effect on the treated (ATT) and the average treatment effect on the untreated (ATU). The program estimates the OLS weights on these parameters, computes the associated model diagnostics, and reports the implicit OLS estimate of the average treatment effect (ATE). See Sloczynski (2019), <http://people.brandeis.edu/~tslocz/Sloczynski_paper_regression.pdf>.

r-hydroroute 0.1.2
Propagated dependencies: r-scales@1.4.0 r-reshape2@1.4.5 r-lubridate@1.9.4 r-hydropeak@0.1.2 r-gridextra@2.3 r-ggpmisc@0.6.2 r-ggplot2@4.0.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/h.scm (guix-cran packages h)
Home page: https://cran.r-project.org/package=hydroroute
Licenses: GPL 2
Build system: r
Synopsis: Trace Longitudinal Hydropeaking Waves
Description:

This package implements an empirical approach referred to as PeakTrace which uses multiple hydrographs to detect and follow hydropower plant-specific hydropeaking waves at the sub-catchment scale and to describe how hydropeaking flow parameters change along the longitudinal flow path. The method is based on the identification of associated events and uses (linear) regression models to describe translation and retention processes between neighboring hydrographs. Several regression model results are combined to arrive at a power plant-specific model. The approach is proposed and validated in Greimel et al. (2022) <doi:10.1002/rra.3978>. The identification of associated events is based on the event detection implemented in hydropeak'.

r-hetcorfs 1.0.1
Propagated dependencies: r-psych@2.5.6 r-polycor@0.8-1 r-dplyr@1.1.4 r-cluster@2.1.8.1
Channel: guix-cran
Location: guix-cran/packages/h.scm (guix-cran packages h)
Home page: https://cran.r-project.org/package=hetcorFS
Licenses: GPL 2
Build system: r
Synopsis: Unsupervised Feature Selection using the Heterogeneous Correlation Matrix
Description:

Unsupervised multivariate filter feature selection using the UFS-rHCM or UFS-cHCM algorithms based on the heterogeneous correlation matrix (HCM). The HCM consists of Pearson's correlations between numerical features, polyserial correlations between numerical and ordinal features, and polychoric correlations between ordinal features. Tortora C., Madhvani S., Punzo A. (2025). "Designing unsupervised mixed-type feature selection techniques using the heterogeneous correlation matrix." International Statistical Review <doi:10.1111/insr.70016>. This work was supported by the National Science foundation NSF Grant N 2209974 (Tortora) and by the Italian Ministry of University and Research (MUR) under the PRIN 2022 grant number 2022XRHT8R (CUP: E53D23005950006), as part of â The SMILE Project: Statistical Modelling and Inference to Live the Environmentâ , funded by the European Union â Next Generation EU (Punzo).

r-hnp 1.2-7
Propagated dependencies: r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/h.scm (guix-cran packages h)
Home page: https://cran.r-project.org/package=hnp
Licenses: GPL 2+
Build system: r
Synopsis: Half-Normal Plots with Simulation Envelopes
Description:

Generates (half-)normal plots with simulation envelopes using different diagnostics from a range of different fitted models. A few example datasets are included.

r-hetsurrsurv 1.0
Propagated dependencies: r-rsurrogate@3.2 r-mass@7.3-65 r-groc@1.0.10
Channel: guix-cran
Location: guix-cran/packages/h.scm (guix-cran packages h)
Home page: https://cran.r-project.org/package=hetsurrSurv
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Assessing Heterogeneity in Surrogacy Using Censored Data
Description:

This package provides functions to assess and test for heterogeneity in the utility of a surrogate marker with respect to a baseline covariate using censored (survival data), and to test for heterogeneity across multiple time points. More details are available in Parast et al (2024) <doi:10.1002/sim.10122>.

r-huxtable 5.8.0
Dependencies: calc@2.15.1.1
Propagated dependencies: r-xml2@1.5.0 r-tidyselect@1.2.1 r-stringr@1.6.0 r-stringi@1.8.7 r-rlang@1.1.6 r-r6@2.6.1 r-memoise@2.0.1 r-htmltools@0.5.8.1 r-glue@1.8.0 r-generics@0.1.4 r-fansi@1.0.7 r-commonmark@2.0.0 r-base64enc@0.1-3 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/h.scm (guix-cran packages h)
Home page: https://hughjonesd.github.io/huxtable/
Licenses: Expat
Build system: r
Synopsis: Easily Create and Style Tables for LaTeX, HTML and Other Formats
Description:

This package creates styled tables for data presentation. Export to HTML, LaTeX, RTF, Word', Excel', PowerPoint', typst', SVG and PNG. Simple, modern interface to manipulate borders, size, position, captions, colours, text styles and number formatting. Table cells can span multiple rows and/or columns. Includes a huxreg function to create regression tables, and quick_* one-liners to print tables to a new document.

r-healthyr-ai 0.1.1
Propagated dependencies: r-yardstick@1.3.2 r-workflows@1.3.0 r-tune@2.0.1 r-tidyr@1.3.1 r-tibble@3.3.0 r-rlang@1.1.6 r-recipes@1.3.1 r-purrr@1.2.0 r-parsnip@1.3.3 r-modeltime@1.3.5 r-magrittr@2.0.4 r-h2o@3.44.0.3 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-forcats@1.0.1 r-dplyr@1.1.4 r-dials@1.4.2 r-broom@1.0.10
Channel: guix-cran
Location: guix-cran/packages/h.scm (guix-cran packages h)
Home page: https://www.spsanderson.com/healthyR.ai/
Licenses: Expat
Build system: r
Synopsis: The Machine Learning and AI Modeling Companion to 'healthyR'
Description:

Hospital machine learning and ai data analysis workflow tools, modeling, and automations. This library provides many useful tools to review common administrative hospital data. Some of these include predicting length of stay, and readmits. The aim is to provide a simple and consistent verb framework that takes the guesswork out of everything.

r-hawkesbow 1.0.3
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-nloptr@2.2.1 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/h.scm (guix-cran packages h)
Home page: https://cran.r-project.org/package=hawkesbow
Licenses: Expat
Build system: r
Synopsis: Estimation of Hawkes Processes from Binned Observations
Description:

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.

r-heatex 1.0
Channel: guix-cran
Location: guix-cran/packages/h.scm (guix-cran packages h)
Home page: https://cran.r-project.org/package=heatex
Licenses: GPL 3
Build system: r
Synopsis: Heat exchange calculations during physical activity
Description:

The heatex package calculates heat storage in the body and the components of heat exchange (conductive, convective, radiative, and evaporative) between the body and the environment during physical activity based on the principles of partitional calorimetry. The program enables heat exchange calculations for a range of environmental conditions when wearing various clothing ensembles.

r-hydroevents 0.13.0
Channel: guix-cran
Location: guix-cran/packages/h.scm (guix-cran packages h)
Home page: https://github.com/conradwasko/hydroEvents
Licenses: GPL 3
Build system: r
Synopsis: Extract Event Statistics in Hydrologic Time Series
Description:

Events from individual hydrologic time series are extracted, and events are matched across multiple time series. The package has been applied in studies such as Wasko and Guo (2022) <doi:10.1002/hyp.14563> and Mohammadpour Khoie, Guo and Wasko (2025) <doi:10.1016/j.envsoft.2025.106521>.

r-hubensembles 1.0.0
Propagated dependencies: r-tidyselect@1.2.1 r-tidyr@1.3.1 r-rlang@1.1.6 r-purrr@1.2.0 r-matrixstats@1.5.0 r-lifecycle@1.0.4 r-hubutils@1.2.0 r-dplyr@1.1.4 r-distfromq@1.0.4 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/h.scm (guix-cran packages h)
Home page: https://github.com/hubverse-org/hubEnsembles
Licenses: Expat
Build system: r
Synopsis: Ensemble Methods for Combining Hub Model Outputs
Description:

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

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