_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
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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-eespca 0.8.0
Propagated dependencies: r-rifle@1.0 r-pma@1.2-4 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=EESPCA
Licenses: GPL 2+
Build system: r
Synopsis: Eigenvectors from Eigenvalues Sparse Principal Component Analysis (EESPCA)
Description:

This package contains logic for computing sparse principal components via the EESPCA method, which is based on an approximation of the eigenvector/eigenvalue identity. Includes logic to support execution of the TPower and rifle sparse PCA methods, as well as logic to estimate the sparsity parameters used by EESPCA, TPower and rifle via cross-validation to minimize the out-of-sample reconstruction error. H. Robert Frost (2021) <doi:10.1080/10618600.2021.1987254>.

r-exhaustivesearch 1.0.2
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/RudolfJagdhuber/ExhaustiveSearch
Licenses: GPL 3+
Build system: r
Synopsis: Fast and Scalable Exhaustive Feature Selection Framework
Description:

The goal of this package is to provide an easy to use, fast and scalable exhaustive search framework. Exhaustive feature selections typically require a very large number of models to be fitted and evaluated. Execution speed and memory management are crucial factors here. This package provides solutions for both. Execution speed is optimized by using a multi-threaded C++ backend, and memory issues are solved by by only storing the best results during execution and thus keeping memory usage constant.

r-euclimatch 1.0.2
Propagated dependencies: r-terra@1.8-86 r-rcppparallel@5.1.11-1 r-rcpp@1.1.0 r-rcolorbrewer@1.1-3 r-foreach@1.5.2 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=Euclimatch
Licenses: GPL 3+
Build system: r
Synopsis: Euclidean Climatch Algorithm
Description:

An interface for performing climate matching using the Euclidean "Climatch" algorithm. Functions provide a vector of climatch scores (0-10) for each location (i.e., grid cell) within the recipient region, the percent of climatch scores >= a threshold value, and mean climatch score. Tools for parallelization and visualizations are also provided. Note that the floor function that rounds the climatch score down to the nearest integer has been removed in this implementation and the â Climatchâ algorithm, also referred to as the â Climateâ algorithm, is described in: Crombie, J., Brown, L., Lizzio, J., & Hood, G. (2008). â Climatch user manualâ . The method for the percent score is described in: Howeth, J.G., Gantz, C.A., Angermeier, P.L., Frimpong, E.A., Hoff, M.H., Keller, R.P., Mandrak, N.E., Marchetti, M.P., Olden, J.D., Romagosa, C.M., and Lodge, D.M. (2016). <doi:10.1111/ddi.12391>.

r-eva 0.2.6
Propagated dependencies: r-matrix@1.7-4 r-envstats@3.1.0
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/brianbader/eva_package
Licenses: GPL 2+
Build system: r
Synopsis: Extreme Value Analysis with Goodness-of-Fit Testing
Description:

Goodness-of-fit tests for selection of r in the r-largest order statistics (GEVr) model. Goodness-of-fit tests for threshold selection in the Generalized Pareto distribution (GPD). Random number generation and density functions for the GEVr distribution. Profile likelihood for return level estimation using the GEVr and Generalized Pareto distributions. P-value adjustments for sequential, multiple testing error control. Non-stationary fitting of GEVr and GPD. Bader, B., Yan, J. & Zhang, X. (2016) <doi:10.1007/s11222-016-9697-3>. Bader, B., Yan, J. & Zhang, X. (2018) <doi:10.1214/17-AOAS1092>.

r-equalprognosis 0.1.2
Propagated dependencies: r-survival@3.8-3 r-stringr@1.6.0 r-proc@1.19.0.1 r-predtools@0.0.3 r-mime@0.13 r-ggplot2@4.0.1 r-calibrationcurves@3.0.0 r-base64enc@0.1-3
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://sites.google.com/view/equal-group/home
Licenses: GPL 3+
Build system: r
Synopsis: Analysing Prognostic Studies
Description:

This package provides functions that help with analysis of prognostic study data. This allows users with little experience of developing models to develop models and assess the performance of the prognostic models. This also summarises the information, so the performance of multiple models can be displayed simultaneously. This minor update fixes issues related to memory requirements with large number of simulations and deals with situations when there is overfitting of data. Gurusamy, K (2026)<https://github.com/kurinchi2k/EQUALPrognosis>.

r-emirt 0.0.15
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-pscl@1.5.9
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=emIRT
Licenses: GPL 3+
Build system: r
Synopsis: EM Algorithms for Estimating Item Response Theory Models
Description:

Various Expectation-Maximization (EM) algorithms are implemented for item response theory (IRT) models. The package includes IRT models for binary and ordinal responses, along with dynamic and hierarchical IRT models with binary responses. The latter two models are fitted using variational EM. The package also includes variational network and text scaling models. The algorithms are described in Imai, Lo, and Olmsted (2016) <DOI:10.1017/S000305541600037X>.

r-enetlts 1.1.0
Propagated dependencies: r-robusthd@0.8.4 r-robustbase@0.99-6 r-reshape@0.8.10 r-glmnet@4.1-10 r-ggplot2@4.0.1 r-cvtools@0.3.3
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=enetLTS
Licenses: GPL 3+
Build system: r
Synopsis: Robust and Sparse Methods for High Dimensional Linear and Binary and Multinomial Regression
Description:

Fully robust versions of the elastic net estimator are introduced for linear and binary and multinomial regression, in particular high dimensional data. The algorithm searches for outlier free subsets on which the classical elastic net estimators can be applied. A reweighting step is added to improve the statistical efficiency of the proposed estimators. Selecting appropriate tuning parameters for elastic net penalties are done via cross-validation.

r-epidata 0.4.0
Propagated dependencies: r-xml2@1.5.0 r-tinytest@1.4.1 r-tidyr@1.3.1 r-stringi@1.8.7 r-rvest@1.0.5 r-readr@2.1.6 r-purrr@1.2.0 r-jsonlite@2.0.0 r-httr@1.4.7 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://gitlab.com/hrbrmstr/epidata
Licenses: FSDG-compatible
Build system: r
Synopsis: Tools to Retrieve Economic Policy Institute Data Library Extracts
Description:

The Economic Policy Institute (<https://www.epi.org/>) provides researchers, media, and the public with easily accessible, up-to-date, and comprehensive historical data on the American labor force. It is compiled from Economic Policy Institute analysis of government data sources. Use it to research wages, inequality, and other economic indicators over time and among demographic groups. Data is usually updated monthly.

r-esaddle 0.0.7
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-plyr@1.8.9 r-mvnfast@0.2.8 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/mfasiolo/esaddle
Licenses: GPL 2+
Build system: r
Synopsis: Extended Empirical Saddlepoint Density Approximations
Description:

This package provides tools for fitting the Extended Empirical Saddlepoint (EES) density of Fasiolo et al. (2018) <doi:10.1214/18-EJS1433>.

r-evolution 0.1.0
Propagated dependencies: r-jsonlite@2.0.0 r-httr2@1.2.1 r-cli@3.6.5 r-base64enc@0.1-3
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/StrategicProjects/evolution/
Licenses: Expat
Build system: r
Synopsis: Client for 'Evolution Cloud API'
Description:

This package provides an R interface to the Evolution API <https://evoapicloud.com>, enabling sending and receiving WhatsApp messages directly from R'. Functions include sending text, media (image/video/document), audio, stickers, geographic locations, contacts, polls, interactive lists and button messages. Also includes number verification and structured CLI logging for debugging.

r-elaborator 1.3.5
Propagated dependencies: r-tidyselect@1.2.1 r-tidyr@1.3.1 r-shinywidgets@0.9.0 r-shinydashboard@0.7.3 r-shiny@1.11.1 r-shape@1.4.6.1 r-seriation@1.5.8 r-rlang@1.1.6 r-reshape2@1.4.5 r-rcolorbrewer@1.1-3 r-purrr@1.2.0 r-here@1.0.2 r-haven@2.5.5 r-forcats@1.0.1 r-dt@0.34.0 r-dplyr@1.1.4 r-dendextend@1.19.1 r-bsplus@0.1.5
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/openpharma/elaborator
Licenses: GPL 3
Build system: r
Synopsis: 'shiny' Application for Exploring Laboratory Data
Description:

This package provides a novel concept for generating knowledge and gaining insights into laboratory data. You will be able to efficiently and easily explore your laboratory data from different perspectives. Janitza, S., Majumder, M., Mendolia, F., Jeske, S., & Kulmann, H. (2021) <doi:10.1007/s43441-021-00318-4>.

r-eemdsvr 0.1.0
Propagated dependencies: r-rlibeemd@1.4.4 r-e1071@1.7-16
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=EEMDSVR
Licenses: GPL 3
Build system: r
Synopsis: Ensemble Empirical Mode Decomposition and Its Variant Based Support Vector Regression Model
Description:

Application of Ensemble Empirical Mode Decomposition and its variant based Support Vector regression model for univariate time series forecasting. For method details see Das (2020).<http://krishi.icar.gov.in/jspui/handle/123456789/44138>.

r-eirm 0.5
Propagated dependencies: r-shinydashboard@0.7.3 r-shinycssloaders@1.1.0 r-shiny@1.11.1 r-reshape2@1.4.5 r-readxl@1.4.5 r-optimx@2025-4.9 r-magrittr@2.0.4 r-lme4@1.1-37 r-ggplot2@4.0.1 r-ggeffects@2.3.1 r-blme@1.0-6
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/okanbulut/eirm
Licenses: GPL 3+
Build system: r
Synopsis: Explanatory Item Response Modeling for Dichotomous and Polytomous Items
Description:

Analysis of dichotomous and polytomous response data using the explanatory item response modeling framework, as described in Bulut, Gorgun, & Yildirim-Erbasli (2021) <doi:10.3390/psych3030023>, Stanke & Bulut (2019) <doi:10.21449/ijate.515085>, and De Boeck & Wilson (2004) <doi:10.1007/978-1-4757-3990-9>. Generalized linear mixed modeling is used for estimating the effects of item-related and person-related variables on dichotomous and polytomous item responses.

r-evildice 1.0
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=evilDice
Licenses: LGPL 3
Build system: r
Synopsis: Test Dice Sets for Intransitive Properties
Description:

Checks to see whether a supplied set of dice (their face values) are transitive, returning pair-win and group-roll win probabilities. Expected returns (mean magnitude of win/loss) are presented as well.

r-enpls 6.1.1
Propagated dependencies: r-spls@2.3-2 r-reshape2@1.4.5 r-pls@2.8-5 r-plotly@4.11.0 r-ggplot2@4.0.1 r-foreach@1.5.2 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://nanx.me/enpls/
Licenses: GPL 3+
Build system: r
Synopsis: Ensemble Partial Least Squares Regression
Description:

An algorithmic framework for measuring feature importance, outlier detection, model applicability domain evaluation, and ensemble predictive modeling with (sparse) partial least squares regressions.

r-elmr 1.0
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=ELMR
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: Extreme Machine Learning (ELM)
Description:

Training and prediction functions are provided for the Extreme Learning Machine algorithm (ELM). The ELM use a Single Hidden Layer Feedforward Neural Network (SLFN) with random generated weights and no gradient-based backpropagation. The training time is very short and the online version allows to update the model using small chunk of the training set at each iteration. The only parameter to tune is the hidden layer size and the learning function.

r-edear 1.0.0
Propagated dependencies: r-zoo@1.8-14 r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-shinytime@1.0.3 r-shiny@1.11.1 r-rstudioapi@0.17.1 r-rlang@1.1.6 r-purrr@1.2.0 r-miniui@0.1.2 r-magrittr@2.0.4 r-lubridate@1.9.4 r-lifecycle@1.0.4 r-hms@1.1.4 r-glue@1.8.0 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-data-table@1.17.8 r-cli@3.6.5 r-bupar@1.0.1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://bupar.net/
Licenses: Expat
Build system: r
Synopsis: Exploratory and Descriptive Event-Based Data Analysis
Description:

Exploratory and descriptive analysis of event based data. Provides methods for describing and selecting process data, and for preparing event log data for process mining. Builds on the S3-class for event logs implemented in the package bupaR'.

r-enmpa 0.2.3
Propagated dependencies: r-vegan@2.7-2 r-terra@1.8-86 r-snow@0.4-4 r-rcpp@1.1.0 r-mgcv@1.9-4 r-foreach@1.5.2 r-ellipse@0.5.0 r-dosnow@1.0.20
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/Luisagi/enmpa
Licenses: GPL 3+
Build system: r
Synopsis: Ecological Niche Modeling using Presence-Absence Data
Description:

This package provides a set of tools to perform Ecological Niche Modeling with presence-absence data. It includes algorithms for data partitioning, model fitting, calibration, evaluation, selection, and prediction. Other functions help to explore signals of ecological niche using univariate and multivariate analyses, and model features such as variable response curves and variable importance. Unique characteristics of this package are the ability to exclude models with concave quadratic responses, and the option to clamp model predictions to specific variables. These tools are implemented following principles proposed in Cobos et al., (2022) <doi:10.17161/bi.v17i.15985>, Cobos et al., (2019) <doi:10.7717/peerj.6281>, and Peterson et al., (2008) <doi:10.1016/j.ecolmodel.2007.11.008>.

r-einsum 0.1.2
Propagated dependencies: r-rcpp@1.1.0 r-mathjaxr@1.8-0 r-glue@1.8.0
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://const-ae.github.io/einsum/
Licenses: Expat
Build system: r
Synopsis: Einstein Summation
Description:

The summation notation suggested by Einstein (1916) <doi:10.1002/andp.19163540702> is a concise mathematical notation that implicitly sums over repeated indices of n-dimensional arrays. Many ordinary matrix operations (e.g. transpose, matrix multiplication, scalar product, diag()', trace etc.) can be written using Einstein notation. The notation is particularly convenient for expressing operations on arrays with more than two dimensions because the respective operators ('tensor products') might not have a standardized name.

r-ensr 0.1.0
Propagated dependencies: r-glmnet@4.1-10 r-ggplot2@4.0.1 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/dewittpe/ensr
Licenses: GPL 2
Build system: r
Synopsis: Elastic Net SearcheR
Description:

Elastic net regression models are controlled by two parameters, lambda, a measure of shrinkage, and alpha, a metric defining the model's location on the spectrum between ridge and lasso regression. glmnet provides tools for selecting lambda via cross validation but no automated methods for selection of alpha. Elastic Net SearcheR automates the simultaneous selection of both lambda and alpha. Developed, in part, with support by NICHD R03 HD094912.

r-epipvr 0.0.1
Propagated dependencies: r-stanheaders@2.32.10 r-rstantools@2.5.0 r-rstan@2.32.7 r-rcppparallel@5.1.11-1 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-posterior@1.6.1 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://CRAN.R-project.org/package=EpiPvr
Licenses: GPL 3
Build system: r
Synopsis: Estimating Plant Pathogen Epidemiology Parameters from Laboratory Assays
Description:

This package provides functions for estimating plant pathogen parameters from access period (AP) experiments. Separate functions are implemented for semi-persistently transmitted (SPT) and persistently transmitted (PT) pathogens. The common AP experiment exposes insect cohorts to infected source plants, healthy test plants, and intermediate plants (for PT pathogens). The package allows estimation of acquisition and inoculation rates during feeding, recovery rates, and latent progression rates (for PT pathogens). Additional functions support inference of epidemic risk from pathogen and local parameters, and also simulate AP experiment data. The functions implement probability models for epidemiological analysis, as derived in Donnelly et al. (2025), <doi:10.32942/X29K9P>. These models were originally implemented in the EpiPv GitHub package.

r-excerptr 2.1.0
Dependencies: python@3.11.14
Propagated dependencies: r-reticulate@1.44.1 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://gitlab.com/fvafrcu/excerptr
Licenses: FreeBSD
Build system: r
Synopsis: Excerpt Structuring Comments from Your Code File and Set a Table of Contents
Description:

Ever read or wrote source files containing sectioning comments? If these comments are markdown style section comments, you can excerpt them and set a table of contents using the python package excerpts (<https://pypi.org/project/excerpts/>).

r-extremevalues 2.4.1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/markvanderloo/extremevalues
Licenses: GPL 2
Build system: r
Synopsis: Univariate Outlier Detection
Description:

Detect outliers in one-dimensional data.

r-etrader 0.1.5
Propagated dependencies: r-urltools@1.7.3.1 r-rvest@1.0.5 r-purrr@1.2.0 r-magrittr@2.0.4 r-jsonlite@2.0.0 r-httr@1.4.7 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://exploringfinance.github.io/etrader/
Licenses: GPL 3
Build system: r
Synopsis: 'ETRADE' API Interface for R
Description:

Use R to interface with the ETRADE API <https://developer.etrade.com/home>. Functions include authentication, trading, quote requests, account information, and option chains. A user will need an ETRADE brokerage account and ETRADE API approval. See README for authentication process and examples.

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