_            _    _        _         _
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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-emgaussian 0.2.2
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-matrixcalc@1.0-6 r-matrix@1.7-4 r-lavaan@0.6-20 r-glassofast@1.0.1 r-glasso@1.11 r-caret@7.0-1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=EMgaussian
Licenses: GPL 3+
Build system: r
Synopsis: Expectation-Maximization Algorithm for Multivariate Normal (Gaussian) with Missing Data
Description:

Initially designed to distribute code for estimating the Gaussian graphical model with Lasso regularization, also known as the graphical lasso (glasso), using an Expectation-Maximization (EM) algorithm based on work by Städler and Bühlmann (2012) <doi:10.1007/s11222-010-9219-7>. As a byproduct, code for estimating means and covariances (or the precision matrix) under a multivariate normal (Gaussian) distribution is also available.

r-evolqg 0.3-6
Propagated dependencies: r-vegan@2.7-2 r-reshape2@1.4.5 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-plyr@1.8.9 r-numderiv@2016.8-1.1 r-mvtnorm@1.3-3 r-morpho@2.13 r-mcmcpack@1.7-1 r-matrix@1.7-4 r-igraph@2.2.1 r-ggplot2@4.0.1 r-expm@1.0-0 r-coda@0.19-4.1 r-ape@5.8-1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=evolqg
Licenses: Expat
Build system: r
Synopsis: Evolutionary Quantitative Genetics
Description:

This package provides functions for covariance matrix comparisons, estimation of repeatabilities in measurements and matrices, and general evolutionary quantitative genetics tools. Melo D, Garcia G, Hubbe A, Assis A P, Marroig G. (2016) <doi:10.12688/f1000research.7082.3>.

r-elmethodvar 0.1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/jingru-zhang/ELmethod
Licenses: GPL 2+
Build system: r
Synopsis: Empirical Likelihood Inference of Variance Components in Linear Mixed-Effects Models
Description:

This package provides empirical likelihood-based methods for the inference of variance components in linear mixed-effects models.

r-empiricaldynamics 0.1.2
Dependencies: julia@1.8.5
Propagated dependencies: r-tseries@0.10-58 r-signal@1.8-1 r-minpack-lm@1.2-4 r-lmtest@0.9-40 r-juliacall@0.17.6 r-gridextra@2.3 r-ggplot2@4.0.1 r-cvxr@1.0-15
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/IsadoreNabi/EmpiricalDynamics
Licenses: Expat
Build system: r
Synopsis: Empirical Discovery of Differential Equations from Time Series Data
Description:

This package provides a comprehensive toolkit for discovering differential and difference equations from empirical time series data using symbolic regression. The package implements a complete workflow from data preprocessing (including Total Variation Regularized differentiation for noisy economic data), visual exploration of dynamical structure, and symbolic equation discovery via genetic algorithms. It leverages a high-performance Julia backend ('SymbolicRegression.jl') to provide industrial-grade robustness, physics-informed constraints, and rigorous out-of-sample validation. Designed for economists, physicists, and researchers studying dynamical systems from observational data.

r-ebal 0.1-8
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://web.stanford.edu/~jhain/
Licenses: GPL 2+
Build system: r
Synopsis: Entropy Reweighting to Create Balanced Samples
Description:

Package implements entropy balancing, a data preprocessing procedure described in Hainmueller (2008, <doi:10.1093/pan/mpr025>) that allows users to reweight a dataset such that the covariate distributions in the reweighted data satisfy a set of user specified moment conditions. This can be useful to create balanced samples in observational studies with a binary treatment where the control group data can be reweighted to match the covariate moments in the treatment group. Entropy balancing can also be used to reweight a survey sample to known characteristics from a target population.

r-escvtmle 0.0.2
Propagated dependencies: r-tidyselect@1.2.1 r-superlearner@2.0-29 r-stringr@1.6.0 r-origami@1.0.7 r-mass@7.3-65 r-gridextra@2.3 r-ggplot2@4.0.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/Lauren-EylerDang/EScvtmle/tree/main
Licenses: GPL 3
Build system: r
Synopsis: Experiment-Selector CV-TMLE for Integration of Observational and RCT Data
Description:

The experiment selector cross-validated targeted maximum likelihood estimator (ES-CVTMLE) aims to select the experiment that optimizes the bias-variance tradeoff for estimating a causal average treatment effect (ATE) where different experiments may include a randomized controlled trial (RCT) alone or an RCT combined with real-world data. Using cross-validation, the ES-CVTMLE separates the selection of the optimal experiment from the estimation of the ATE for the chosen experiment. The estimated bias term in the selector is a function of the difference in conditional mean outcome under control for the RCT compared to the combined experiment. In order to help include truly unbiased external data in the analysis, the estimated average treatment effect on a negative control outcome may be added to the bias term in the selector. For more details about this method, please see Dang et al. (2022) <arXiv:2210.05802>.

r-edibble 1.1.1
Propagated dependencies: r-vctrs@0.6.5 r-tidyselect@1.2.1 r-tibble@3.3.0 r-rlang@1.1.6 r-r6@2.6.1 r-pillar@1.11.1 r-nestr@0.1.2 r-magrittr@2.0.4 r-lifecycle@1.0.4 r-dplyr@1.1.4 r-dae@3.2.32 r-cli@3.6.5 r-algdesign@1.2.1.2
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://edibble.emitanaka.org/
Licenses: Expat
Build system: r
Synopsis: Encapsulating Elements of Experimental Design
Description:

This package provides a system to facilitate designing comparative (and non-comparative) experiments using the grammar of experimental designs <https://emitanaka.org/edibble-book/>. An experimental design is treated as an intermediate, mutable object that is built progressively by fundamental experimental components like units, treatments, and their relation. The system aids in experimental planning, management and workflow.

r-eyelinkreader 1.0.3
Propagated dependencies: r-tidyr@1.3.1 r-stringr@1.6.0 r-rlang@1.1.6 r-rcppprogress@0.4.2 r-rcpp@1.1.0 r-purrr@1.2.0 r-ggplot2@4.0.1 r-fs@1.6.6 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/alexander-pastukhov/eyelinkReader/
Licenses: GPL 3+
Build system: r
Synopsis: Import Gaze Data for EyeLink Eye Tracker
Description:

Import gaze data from edf files generated by the SR Research <https://www.sr-research.com/> EyeLink eye tracker. Gaze data, both recorded events and samples, is imported per trial. The package allows to extract events of interest, such as saccades, blinks, etc. as well as recorded variables and custom events (areas of interest, triggers) into separate tables. The package requires EDF API library that can be obtained at <https://www.sr-research.com/support/>.

r-eufootball 0.0.1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=EUfootball
Licenses: GPL 3+
Build system: r
Synopsis: Football Match Data of European Leagues
Description:

This package contains match results from seven European men's football leagues, namely Premier League (England), Ligue 1 (France), Bundesliga (Germany), Serie A (Italy), Primera Division (Spain), Eredivisie (The Netherlands), Super Lig (Turkey). Includes Seasons 2010/2011 until 2019/2020 and a set of interesting covariates. Can be used all purposes.

r-episemble 0.1.1
Propagated dependencies: r-tidyverse@2.0.0 r-tibble@3.3.0 r-stringr@1.6.0 r-splitstackshape@1.4.8 r-seqinr@4.2-36 r-randomforest@4.7-1.2 r-party@1.3-18 r-iterators@1.0.14 r-gbm@2.2.2 r-ftrcool@2.0.0 r-foreach@1.5.2 r-entropy@1.3.2 r-e1071@1.7-16 r-doparallel@1.0.17 r-devtools@2.4.6 r-caret@7.0-1 r-biostrings@2.78.0
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=EpiSemble
Licenses: GPL 3
Build system: r
Synopsis: Ensemble Based Machine Learning Approach for Predicting Methylation States
Description:

DNA methylation (6mA) is a major epigenetic process by which alteration in gene expression took place without changing the DNA sequence. Predicting these sites in-vitro is laborious, time consuming as well as costly. This EpiSemble package is an in-silico pipeline for predicting DNA sequences containing the 6mA sites. It uses an ensemble-based machine learning approach by combining Support Vector Machine (SVM), Random Forest (RF) and Gradient Boosting approach to predict the sequences with 6mA sites in it. This package has been developed by using the concept of Chen et al. (2019) <doi:10.1093/bioinformatics/btz015>.

r-edgar 2.0.8
Propagated dependencies: r-xml@3.99-0.20 r-tm@0.7-16 r-stringr@1.6.0 r-stringi@1.8.7 r-r-utils@2.13.0 r-qdapregex@0.7.10 r-httr@1.4.7
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=edgar
Licenses: GPL 2
Build system: r
Synopsis: Tool for the U.S. SEC EDGAR Retrieval and Parsing of Corporate Filings
Description:

In the USA, companies file different forms with the U.S. Securities and Exchange Commission (SEC) through EDGAR (Electronic Data Gathering, Analysis, and Retrieval system). The EDGAR database automated system collects all the different necessary filings and makes it publicly available. This package facilitates retrieving, storing, searching, and parsing of all the available filings on the EDGAR server. It downloads filings from SEC server in bulk with a single query. Additionally, it provides various useful functions: extracts 8-K triggering events, extract "Business (Item 1)" and "Management's Discussion and Analysis(Item 7)" sections of annual statements, searches filings for desired keywords, provides sentiment measures, parses filing header information, and provides HTML view of SEC filings.

r-exams2forms 0.2-0
Propagated dependencies: r-rmarkdown@2.30 r-knitr@1.50 r-exams@2.4-3 r-digest@0.6.39 r-base64enc@0.1-3
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://www.R-exams.org/tutorials/exams2forms/
Licenses: GPL 3
Build system: r
Synopsis: Embedding 'exams' Exercises as Forms in 'rmarkdown' or 'quarto' Documents
Description:

Automatic generation of quizzes or individual questions as (interactive) forms within rmarkdown or quarto documents based on R/exams exercises.

r-emcluster 0.2-17
Propagated dependencies: r-matrix@1.7-4 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/snoweye/EMCluster
Licenses: FSDG-compatible
Build system: r
Synopsis: EM Algorithm for Model-Based Clustering of Finite Mixture Gaussian Distribution
Description:

EM algorithms and several efficient initialization methods for model-based clustering of finite mixture Gaussian distribution with unstructured dispersion in both of unsupervised and semi-supervised learning.

r-extremerisks 0.0.5
Propagated dependencies: r-tmvtnorm@1.7 r-pracma@2.4.6 r-plot3d@1.4.2 r-mvtnorm@1.3-3 r-evd@2.3-7.1 r-copula@1.1-7
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://faculty.unibocconi.it/simonepadoan/
Licenses: GPL 2+
Build system: r
Synopsis: Extreme Risk Measures
Description:

This package provides a set of procedures for estimating risks related to extreme events via risk measures such as Expectile, Value-at-Risk, etc. is provided. Estimation methods for univariate independent observations and temporal dependent observations are available. The methodology is extended to the case of independent multidimensional observations. The statistical inference is performed through parametric and non-parametric estimators. Inferential procedures such as confidence intervals, confidence regions and hypothesis testing are obtained by exploiting the asymptotic theory. Adapts the methodologies derived in Padoan and Stupfler (2022) <doi:10.3150/21-BEJ1375>, Davison et al. (2023) <doi:10.1080/07350015.2022.2078332>, Daouia et al. (2018) <doi:10.1111/rssb.12254>, Drees (2000) <doi:10.1214/aoap/1019487617>, Drees (2003) <doi:10.3150/bj/1066223272>, de Haan and Ferreira (2006) <doi:10.1007/0-387-34471-3>, de Haan et al. (2016) <doi:10.1007/s00780-015-0287-6>, Padoan and Rizzelli (2024) <doi:10.3150/23-BEJ1668>, Daouia et al. (2024) <doi:10.3150/23-BEJ1632>.

r-evidencesynthesis 1.1.0
Dependencies: openjdk@25
Propagated dependencies: r-survival@3.8-3 r-rlang@1.1.6 r-rjava@1.0-11 r-meta@8.2-1 r-hdinterval@0.2.4 r-gridextra@2.3 r-ggplot2@4.0.1 r-ggdist@3.3.3 r-empiricalcalibration@3.1.4 r-dplyr@1.1.4 r-cyclops@3.7.0 r-coda@0.19-4.1 r-beastjar@10.5.1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://ohdsi.github.io/EvidenceSynthesis/
Licenses: ASL 2.0
Build system: r
Synopsis: Synthesizing Causal Evidence in a Distributed Research Network
Description:

Routines for combining causal effect estimates and study diagnostics across multiple data sites in a distributed study, without sharing patient-level data. Allows for normal and non-normal approximations of the data-site likelihood of the effect parameter.

r-estadistica 1.2.1
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-shinydashboard@0.7.3 r-shiny@1.11.1 r-rio@1.2.4 r-plotly@4.11.0 r-openxlsx@4.2.8.1 r-knitr@1.50 r-ggplot2@4.0.1 r-forecast@8.24.0 r-dplyr@1.1.4 r-cowplot@1.2.0 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://www.uv.es/estadistic/
Licenses: GPL 3
Build system: r
Synopsis: Fundamentos de estadística descriptiva e inferencial
Description:

Este paquete pretende apoyar el proceso enseñanza-aprendizaje de estadà stica descriptiva e inferencial. Las funciones contenidas en el paquete estadistica cubren los conceptos básicos estudiados en un curso introductorio. Muchos conceptos son ilustrados con gráficos dinámicos o web apps para facilitar su comprensión. This package aims to help the teaching-learning process of descriptive and inferential statistics. The functions contained in the package estadistica cover the basic concepts studied in a statistics introductory course. Many concepts are illustrated with dynamic graphs or web apps to make the understanding easier. See: Esteban et al. (2005, ISBN: 9788497323741), Newbold et al.(2019, ISBN:9781292315034 ), Murgui et al. (2002, ISBN:9788484424673) .

r-evidenceratio 0.1.0
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=evidenceratio
Licenses: Expat
Build system: r
Synopsis: Likelihood-Based Evidence Ratios for Classical Statistical Tests
Description:

This package implements likelihood-based evidence ratios for unified reporting in classical statistical testing. The package reports effect estimates, uncertainty intervals, and likelihood ratios on the log 10 scale derived from a single statistical model. It applies to standard normal mean tests, contingency tables, and regression coefficients, and provides a direct evidential measure while retaining classical error guarantees. For the Evidence Ratio Reporting Standard see Lawless (2026) <doi:10.5281/zenodo.18261076>.

r-eider 1.0.0
Propagated dependencies: r-tibble@3.3.0 r-stringr@1.6.0 r-rlang@1.1.6 r-purrr@1.2.0 r-magrittr@2.0.4 r-lubridate@1.9.4 r-logger@0.4.1 r-jsonlite@2.0.0 r-fs@1.6.6 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/alan-turing-institute/eider
Licenses: Expat
Build system: r
Synopsis: Declarative Feature Extraction from Tabular Data Records
Description:

Extract features from tabular data in a declarative fashion, with a focus on processing medical records. Features are specified as JSON and are independently processed before being joined. Input data can be provided as CSV files or as data frames. This setup ensures that data is transformed in a modular and reproducible manner, and allows the same pipeline to be easily applied to new data.

r-exhaustiverasch 0.3.7
Propagated dependencies: r-tictoc@1.2.1 r-psychotree@0.16-2 r-psychotools@0.7-5 r-psych@2.5.6 r-pbapply@1.7-4 r-pairwise@0.6.2-0 r-erm@1.0-10 r-arrangements@1.1.9
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/chrgrebe/exhaustiveRasch
Licenses: GPL 3
Build system: r
Synopsis: Item Selection and Exhaustive Search for Rasch Models
Description:

Automation of the item selection processes for Rasch scales by means of exhaustive search for suitable Rasch models (dichotomous, partial credit, rating-scale) in a list of item-combinations. The item-combinations to test can be either all possible combinations or item-combinations can be defined by several rules (forced inclusion of specific items, exclusion of combinations, minimum/maximum items of a subset of items). Tests for model fit and item fit include ordering of the thresholds, item fit-indices, likelihood ratio test, Martin-Löf test, Wald-like test, person-item distribution, person separation index, principal components of Rasch residuals, empirical representation of all raw scores or Rasch trees for detecting differential item functioning. The tests, their ordering and their parameters can be defined by the user. For parameter estimation and model tests, functions of the packages eRm', psychotools or pairwise can be used.

r-ebmaforecast 1.0.32
Propagated dependencies: r-separationplot@1.4 r-rcpp@1.1.0 r-plyr@1.8.9 r-hmisc@5.2-4 r-gtools@3.9.5 r-glue@1.8.0 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/fhollenbach/EBMA/
Licenses: GPL 2+
Build system: r
Synopsis: Estimate Ensemble Bayesian Model Averaging Forecasts using Gibbs Sampling or EM-Algorithms
Description:

Create forecasts from multiple predictions using ensemble Bayesian model averaging (EBMA). EBMA models can be estimated using an expectation maximization (EM) algorithm or as fully Bayesian models via Gibbs sampling. The methods in this package are Montgomery, Hollenbach, and Ward (2015) <doi:10.1016/j.ijforecast.2014.08.001> and Montgomery, Hollenbach, and Ward (2012) <doi:10.1093/pan/mps002>.

r-emt 1.3.2
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=EMT
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Exact Multinomial Test: Goodness-of-Fit Test for Discrete Multivariate Data
Description:

Goodness-of-fit tests for discrete multivariate data. It is tested if a given observation is likely to have occurred under the assumption of an ab-initio model. Monte Carlo methods are provided to make the package capable of solving high-dimensional problems.

r-editdata 0.1.8
Propagated dependencies: r-tibble@3.3.0 r-shinywidgets@0.9.0 r-shiny@1.11.1 r-rstudioapi@0.17.1 r-rio@1.2.4 r-openxlsx@4.2.8.1 r-miniui@0.1.2 r-magrittr@2.0.4 r-dt@0.34.0 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/cardiomoon/editData
Licenses: GPL 3
Build system: r
Synopsis: 'RStudio' Addin for Editing a 'data.frame'
Description:

An RStudio addin for editing a data.frame or a tibble'. You can delete, add or update a data.frame without coding. You can get resultant data as a data.frame'. In the package, modularized shiny app codes are provided. These modules are intended for reuse across applications.

r-easysvg 0.1.0
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/ytdai/easySVG
Licenses: Expat
Build system: r
Synopsis: An Easy SVG Basic Elements Generator
Description:

This SVG elements generator can easily generate SVG elements such as rect, line, circle, ellipse, polygon, polyline, text and group. Also, it can combine and output SVG elements into a SVG file.

r-energygof 0.1
Propagated dependencies: r-statmod@1.5.1 r-gsl@2.1-9 r-fitdistrplus@1.2-4 r-energy@1.7-12 r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/jthaman/energyGOF
Licenses: GPL 3+
Build system: r
Synopsis: Goodness-of-Fit Tests for Univariate Data via Energy
Description:

Conduct one- and two-sample goodness-of-fit tests for univariate data. In the one-sample case, normal, uniform, exponential, Bernoulli, binomial, geometric, beta, Poisson, lognormal, Laplace, asymmetric Laplace, inverse Gaussian, half-normal, chi-squared, gamma, F, Weibull, Cauchy, and Pareto distributions are supported. egof.test() can also test goodness-of-fit to any distribution with a continuous distribution function. A subset of the available distributions can be tested for the composite goodness-of-fit hypothesis, that is, one can test for distribution fit with unknown parameters. P-values are calculated via parametric bootstrap.

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