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

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-exampletestr 1.7.3
Propagated dependencies: r-withr@3.0.2 r-usethis@3.2.1 r-styler@1.11.0 r-stringr@1.6.0 r-strex@2.0.1 r-rstudioapi@0.17.1 r-roxygen2@7.3.3 r-rlang@1.1.6 r-readr@2.1.6 r-purrr@1.2.0 r-magrittr@2.0.4 r-fs@1.6.6 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://rorynolan.github.io/exampletestr/
Licenses: GPL 3
Build system: r
Synopsis: Help for Writing Unit Tests Based on Function Examples
Description:

Take the examples written in your documentation of functions and use them to create shells (skeletons which must be manually completed by the user) of test files to be tested with the testthat package. Sort of like python doctests for R.

r-evchargcost 0.1.0
Propagated dependencies: r-ggplot2@4.0.1 r-cowplot@1.2.0
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=EVchargcost
Licenses: GPL 3
Build system: r
Synopsis: Computes and Plot the Optimal Charging Strategy for Electric Vehicles
Description:

The purpose of this library is to compute the optimal charging cost function for a electric vehicle (EV). It is well known that the charging function of a EV is a concave function that can be approximated by a piece-wise linear function, so bigger the state of charge, slower the charging process is. Moreover, the other important function is the one that gives the electricity price. This function is usually step-wise, since depending on the time of the day, the price of the electricity is different. Then, the problem of charging an EV to a certain state of charge is not trivial. This library implements an algorithm to compute the optimal charging cost function, that is, it plots for a given state of charge r (between 0 and 1) the minimum cost we need to pay in order to charge the EV to that state of charge r. The details of the algorithm are described in González-Rodrà guez et at (2023) <https://inria.hal.science/hal-04362876v1>.

r-epitrix 0.4.1
Propagated dependencies: r-tidyr@1.3.1 r-stringi@1.8.7 r-sodium@1.4.0 r-rlang@1.1.6 r-purrr@1.2.0 r-dplyr@1.1.4 r-distcrete@1.0.3
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: http://www.repidemicsconsortium.org/epitrix/
Licenses: Expat
Build system: r
Synopsis: Small Helpers and Tricks for Epidemics Analysis
Description:

This package provides a collection of small functions useful for epidemics analysis and infectious disease modelling. This includes computation of basic reproduction numbers from growth rates, generation of hashed labels to anonymize data, and fitting discretized Gamma distributions.

r-excel2eprime 0.4.0
Propagated dependencies: r-tidyr@1.3.1 r-stringr@1.6.0 r-readxl@1.4.5 r-magrittr@2.0.4 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/wujackwill/excel2eprime
Licenses: Expat
Build system: r
Synopsis: Split Sentences by Factors
Description:

Split experiment sentences by different experiment design given by the user and the result can be used in E-prime (<https://pstnet.com/products/e-prime/>).

r-egretci 2.0.5
Propagated dependencies: r-foreach@1.5.2 r-egret@3.0.11 r-binom@1.1-1.1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=EGRETci
Licenses: CC0
Build system: r
Synopsis: Exploration and Graphics for RivEr Trends Confidence Intervals
Description:

Collection of functions to evaluate uncertainty of results from water quality analysis using the Weighted Regressions on Time Discharge and Season (WRTDS) method. This package is an add-on to the EGRET package that performs the WRTDS analysis. The WRTDS modeling method was initially introduced and discussed in Hirsch et al. (2010) <doi:10.1111/j.1752-1688.2010.00482.x>, and expanded in Hirsch and De Cicco (2015) <doi:10.3133/tm4A10>. The paper describing the uncertainty and confidence interval calculations is Hirsch et al. (2015) <doi:10.1016/j.envsoft.2015.07.017>.

r-extraoperators 0.3.0
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://joshuawiley.com/extraoperators/
Licenses: GPL 3
Build system: r
Synopsis: Extra Binary Relational and Logical Operators
Description:

Speed up common tasks, particularly logical or relational comparisons and routine follow up tasks such as finding the indices and subsetting. Inspired by mathematics, where something like: 3 < x < 6 is a standard, elegant and clear way to assert that x is both greater than 3 and less than 6 (see for example <https://en.wikipedia.org/wiki/Relational_operator>), a chaining operator is implemented. The chaining operator, %c%, allows multiple relational operations to be used in quotes on the right hand side for the same object, on the left hand side. The %e% operator allows something like set-builder notation (see for example <https://en.wikipedia.org/wiki/Set-builder_notation>) to be used on the right hand side. All operators have built in prefixes defined for all, subset, and which to reduce the amount of code needed for common tasks, such as return those values that are true.

r-easyclimate 0.2.2
Propagated dependencies: r-terra@1.8-86 r-rcurl@1.98-1.17 r-r-utils@2.13.0
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/VeruGHub/easyclimate
Licenses: GPL 3+
Build system: r
Synopsis: Easy Access to High-Resolution Daily Climate Data for Europe
Description:

Get high-resolution (1 km) daily climate data (precipitation, minimum and maximum temperatures) for points and polygons within Europe.

r-echogram 0.1.3
Propagated dependencies: r-readhac@1.0 r-pals@1.10 r-geosphere@1.5-20
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/hvillalo/echogram
Licenses: GPL 3
Build system: r
Synopsis: Echogram Visualisation and Analysis
Description:

Easily import multi-frequency acoustic data stored in HAC files (see <doi:10.17895/ices.pub.5482> for more information on the format), and produce echogram visualisations with predefined or customized color palettes. It is also possible to merge consecutive echograms; mask or delete unwanted echogram areas; model and subtract background noise; and more important, develop, test and interpret different combinations of frequencies in order to perform acoustic filtering of the echogram's data.

r-ern 2.1.2
Propagated dependencies: r-zoo@1.8-14 r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-runjags@2.2.2-5 r-rjags@4-17 r-patchwork@1.3.2 r-lubridate@1.9.4 r-ggplot2@4.0.1 r-epiestim@2.2-5 r-dplyr@1.1.4 r-coda@0.19-4.1 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=ern
Licenses: Expat
Build system: r
Synopsis: Effective Reproduction Number Estimation
Description:

Estimate the effective reproduction number from wastewater and clinical data sources.

r-excel-link 0.9.15
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/gdemin/excel.link
Licenses: GPL 2+
Build system: r
Synopsis: Convenient Data Exchange with Microsoft Excel
Description:

Allows access to data in running instance of Microsoft Excel (e. g. xl[a1] = xl[b2]*3 and so on). Graphics can be transferred with xl[a1] = current.graphics()'. Additionally there are function for reading/writing Excel files - xl.read.file'/'xl.save.file'. They are not fast but able to read/write *.xlsb'-files and password-protected files. There is an Excel workbook with examples of calling R from Excel in the doc folder. It tries to keep things as simple as possible - there are no needs in any additional installations besides R, only VBA code in the Excel workbook. Microsoft Excel is required for this package.

r-ehrmuse 0.0.2.2
Dependencies: gsl@2.8
Propagated dependencies: r-xgboost@1.7.11.1 r-survey@4.4-8 r-plotrix@3.8-13 r-nnet@7.3-20 r-nleqslv@3.3.5 r-mass@7.3-65 r-magrittr@2.0.4 r-formula@1.2-5 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/Ritoban1/EHRmuse
Licenses: GPL 2+
Build system: r
Synopsis: Multi-Cohort Selection Bias Correction using IPW and AIPW Methods
Description:

Comprehensive toolkit for addressing selection bias in binary disease models across diverse non-probability samples, each with unique selection mechanisms. It utilizes Inverse Probability Weighting (IPW) and Augmented Inverse Probability Weighting (AIPW) methods to reduce selection bias effectively in multiple non-probability cohorts by integrating data from either individual-level or summary-level external sources. The package also provides a variety of variance estimation techniques. Please refer to Kundu et al. <doi:10.48550/arXiv.2412.00228>.

r-ezmmek 0.2.4
Propagated dependencies: r-tidyr@1.3.1 r-rlang@1.1.6 r-purrr@1.2.0 r-nls2@0.3-4 r-magrittr@2.0.4 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-assertable@0.2.8
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=ezmmek
Licenses: AGPL 3
Build system: r
Synopsis: Easy Michaelis-Menten Enzyme Kinetics
Description:

Serves as a platform for published fluorometric enzyme assay protocols. ezmmek calibrates, calculates, and plots enzyme activities as they relate to the transformation of synthetic substrates. At present, ezmmek implements two common protocols found in the literature, and is modular to accommodate additional protocols. Here, these protocols are referred to as the In-Sample Calibration (Hoppe, 1983; <doi:10.3354/meps011299>) and In-Buffer Calibration (German et al., 2011; <doi:10.1016/j.soilbio.2011.03.017>). protocols. By containing multiple protocols, ezmmek aims to stimulate discussion about how to best optimize fluorometric enzyme assays. A standardized approach would make studies more comparable and reproducible.

r-ecoltest 0.0.1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/hugosal/ecolTest
Licenses: Expat
Build system: r
Synopsis: Community Ecology Tests
Description:

This package provides functions and data sets to perform and demonstrate community ecology statistical tests, including Hutcheson's t-test (Hutcheson (1970) <doi:10.1016/0022-5193(70)90124-4>, Zar (2010) ISBN:9780321656865).

r-edotrans 0.2.5
Propagated dependencies: r-opgmmassessment@0.4 r-abcanalysis@1.2.1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=EDOtrans
Licenses: GPL 3
Build system: r
Synopsis: Euclidean Distance-Optimized Data Transformation
Description:

This package provides a data transformation method which takes into account the special property of scale non-invariance with a breakpoint at 1 of the Euclidean distance.

r-extendedabsurvtdc 0.1.0
Propagated dependencies: r-survival@3.8-3 r-readxl@1.4.5
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=ExtendedABSurvTDC
Licenses: GPL 3
Build system: r
Synopsis: Survival Analysis using Indicators under Time Dependent Covariates
Description:

Survival analysis is employed to model time-to-event data. This package examines the relationship between survival and one or more predictors, termed as covariates, which can include both treatment variables (e.g., season of birth, represented by indicator functions) and continuous variables. To this end, the Cox-proportional hazard (Cox-PH) model, introduced by Cox in 1972, is a widely applicable and commonly used method for survival analysis. This package enables the estimation of the effect of randomization for the treatment variable to account for potential confounders, providing adjustment when estimating the association with exposure. It accommodates both fixed and time-dependent covariates and computes survival probabilities for lactation periods in dairy animals. The package is built upon the algorithm developed by Klein and Moeschberger (2003) <DOI:10.1007/b97377>.

r-egret 3.0.11
Propagated dependencies: r-truncnorm@1.0-9 r-survival@3.8-3 r-mass@7.3-65 r-foreach@1.5.2 r-fields@17.1 r-dataretrieval@2.7.22
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://pubs.usgs.gov/tm/04/a10/
Licenses: CC0
Build system: r
Synopsis: Exploration and Graphics for RivEr Trends
Description:

Statistics and graphics for streamflow history, water quality trends, and the statistical modeling algorithm: Weighted Regressions on Time, Discharge, and Season (WRTDS).

r-epade 0.5.1
Propagated dependencies: r-survival@3.8-3 r-plotrix@3.8-13 r-hmisc@5.2-4
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=epade
Licenses: GPL 2+
Build system: r
Synopsis: Easy Plots
Description:

This package provides a collection of nice plotting functions directly from a data.frame with limited customisation possibilities.

r-ebdm 3.0.0
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=ebdm
Licenses: GPL 3+
Build system: r
Synopsis: Estimating Bivariate Dependency from Marginal Data
Description:

This package provides statistical methods for estimating bivariate dependency (correlation) from marginal summary statistics across multiple studies. The package supports three modules: (1) bivariate correlation estimation for binary outcomes, (2) bivariate correlation estimation for continuous outcomes, and (3) estimation of component-wise means and variances under a conditional two-component Gaussian mixture model for a continuous variable stratified by a binary class label. These methods enable privacy-preserving joint estimation when individual-level data are unavailable. The approaches are detailed in Shang, Tsao, and Zhang (2025a) <doi:10.48550/arXiv.2505.03995> and Shang, Tsao, and Zhang (2025b) <doi:10.48550/arXiv.2508.02057>.

r-esback 0.3.1
Propagated dependencies: r-esreg@0.6.2
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=esback
Licenses: GPL 3
Build system: r
Synopsis: Expected Shortfall Backtesting
Description:

Implementations of the expected shortfall backtests of Bayer and Dimitriadis (2020) <doi:10.1093/jjfinec/nbaa013> as well as other well known backtests from the literature. Can be used to assess the correctness of forecasts of the expected shortfall risk measure which is e.g. used in the banking and finance industry for quantifying the market risk of investments. A special feature of the backtests of Bayer and Dimitriadis (2020) <doi:10.1093/jjfinec/nbaa013> is that they only require forecasts of the expected shortfall, which is in striking contrast to all other existing backtests, making them particularly attractive for practitioners.

r-ecdfniche 0.1.0
Propagated dependencies: r-mass@7.3-65 r-lemon@0.5.2 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=ECDFniche
Licenses: Expat
Build system: r
Synopsis: Empirical Cumulative Distribution Function Niche Modeling Tools
Description:

Simulate ecological niche models using Mahalanobis distance, transform distances to suitability with 1 - empirical cumulative distribution function and 1 - chi-squared, and generate comparison figures.

r-esmtools 1.0.1
Propagated dependencies: r-tidyr@1.3.1 r-stringr@1.6.0 r-lubridate@1.9.4 r-knitr@1.50 r-kableextra@1.4.0 r-jsonlite@2.0.0 r-htmltools@0.5.8.1 r-ggpubr@0.6.2 r-ggplot2@4.0.1 r-fs@1.6.6 r-dt@0.34.0 r-dplyr@1.1.4 r-base64enc@0.1-3
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://gitlab.kuleuven.be/ppw-okpiv/researchers/u0148925/esmtools/
Licenses: GPL 3+
Build system: r
Synopsis: Preprocessing Experience Sampling Method (ESM) Data
Description:

Tailored explicitly for Experience Sampling Method (ESM) data, it contains a suite of functions designed to simplify preprocessing steps and create subsequent reporting. It empowers users with capabilities to extract critical insights during preprocessing, conducts thorough data quality assessments (e.g., design and sampling scheme checks, compliance rate, careless responses), and generates visualizations and concise summary tables tailored specifically for ESM data. Additionally, it streamlines the creation of informative and interactive preprocessing reports, enabling researchers to transparently share their dataset preprocessing methodologies. Finally, it is part of a larger ecosystem which includes a framework and a web gallery (<https://preprocess.esmtools.com/>).

r-emjmcmc 1.5.0
Propagated dependencies: r-withr@3.0.2 r-stringi@1.8.7 r-speedglm@0.3-5 r-hash@2.2.6.3 r-glmnet@4.1-10 r-bigmemory@4.6.4 r-biglm@0.9-3 r-bas@2.0.2
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=EMJMCMC
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Evolutionary Mode Jumping Markov Chain Monte Carlo Expert Toolbox
Description:

Implementation of the Mode Jumping Markov Chain Monte Carlo algorithm from Hubin, A., Storvik, G. (2018) <doi:10.1016/j.csda.2018.05.020>, Genetically Modified Mode Jumping Markov Chain Monte Carlo from Hubin, A., Storvik, G., & Frommlet, F. (2020) <doi:10.1214/18-BA1141>, Hubin, A., Storvik, G., & Frommlet, F. (2021) <doi:10.1613/jair.1.13047>, and Hubin, A., Heinze, G., & De Bin, R. (2023) <doi:10.3390/fractalfract7090641>, and Reversible Genetically Modified Mode Jumping Markov Chain Monte Carlo from Hubin, A., Frommlet, F., & Storvik, G. (2021) <doi:10.48550/arXiv.2110.05316>, which allow for estimating posterior model probabilities and Bayesian model averaging across a wide set of Bayesian models including linear, generalized linear, generalized linear mixed, generalized nonlinear, generalized nonlinear mixed, and logic regression models.

r-egst 1.0.0
Propagated dependencies: r-purrr@1.2.0 r-mvtnorm@1.3-3 r-matrixstats@1.5.0 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/ArunabhaCodes/eGST
Licenses: GPL 3
Build system: r
Synopsis: Leveraging eQTLs to Identify Individual-Level Tissue of Interest for a Complex Trait
Description:

Genetic predisposition for complex traits is often manifested through multiple tissues of interest at different time points in the development. As an example, the genetic predisposition for obesity could be manifested through inherited variants that control metabolism through regulation of genes expressed in the brain and/or through the control of fat storage in the adipose tissue by dysregulation of genes expressed in adipose tissue. We present a method eGST (eQTL-based genetic subtyper) that integrates tissue-specific eQTLs with GWAS data for a complex trait to probabilistically assign a tissue of interest to the phenotype of each individual in the study. eGST estimates the posterior probability that an individual's phenotype can be assigned to a tissue based on individual-level genotype data of tissue-specific eQTLs and marginal phenotype data in a genome-wide association study (GWAS) cohort. Under a Bayesian framework of mixture model, eGST employs a maximum a posteriori (MAP) expectation-maximization (EM) algorithm to estimate the tissue-specific posterior probability across individuals. Methodology is available from: A Majumdar, C Giambartolomei, N Cai, MK Freund, T Haldar, T Schwarz, J Flint, B Pasaniuc (2019) <doi:10.1101/674226>.

r-epiinvert 0.3.1
Propagated dependencies: r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/lalvarezmat/EpiInvert
Licenses: GPL 2+
Build system: r
Synopsis: Variational Techniques in Epidemiology
Description:

Using variational techniques we address some epidemiological problems as the incidence curve decomposition by inverting the renewal equation as described in Alvarez et al. (2021) <doi:10.1073/pnas.2105112118> and Alvarez et al. (2022) <doi:10.3390/biology11040540> or the estimation of the functional relationship between epidemiological indicators. We also propose a learning method for the short time forecast of the trend incidence curve as described in Morel et al. (2022) <doi:10.1101/2022.11.05.22281904>.

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