_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
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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-exams2sakai 0.4
Propagated dependencies: r-xml2@1.5.0 r-stringr@1.6.0 r-stringi@1.8.7 r-glue@1.8.0 r-exams@2.4-3
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/jesusmmp/exams2sakai
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: Automatic Generation of Exams in R for 'Sakai'
Description:

Automatic Generation of Exams in R for Sakai'. Question templates in the form of the exams package (see <https://www.r-exams.org/>) are transformed into XML format required by Sakai'.

r-e2e 0.1.2
Propagated dependencies: r-xgboost@1.7.11.1 r-timeroc@0.4 r-tidyr@1.3.1 r-survminer@0.5.1 r-survivalroc@1.0.3.1 r-survival@3.8-3 r-shapviz@0.10.3 r-rsnns@0.4-18 r-readr@2.1.6 r-randomforestsrc@2.9.3 r-prroc@1.4 r-proc@1.19.0.1 r-plsrcox@1.8.1 r-patchwork@1.3.2 r-mass@7.3-65 r-magrittr@2.0.4 r-glmnet@4.1-10 r-ggplot2@4.0.1 r-gbm@2.2.2 r-dplyr@1.1.4 r-cowplot@1.2.0 r-caret@7.0-1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://xiaojie0519.github.io/E2E/
Licenses: Expat
Build system: r
Synopsis: Ensemble Learning Framework for Diagnostic and Prognostic Modeling
Description:

This package provides a framework to build and evaluate diagnosis or prognosis models using stacking, voting, and bagging ensemble techniques with various base learners. The package also includes tools for visualization and interpretation of models. The development version of the package is available on GitHub at <https://github.com/xiaojie0519/E2E>. The methods are based on the foundational work of Breiman (1996) <doi:10.1007/BF00058655> on bagging and Wolpert (1992) <doi:10.1016/S0893-6080(05)80023-1> on stacking.

r-exactcidiff 2.1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://urldefense.proofpoint.com/v2/url?u=http-3A__www.R-2Dproject.org&d=DwICaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=u5749-0kOlGR1AfizUVmaw&m=qjJHLdipM5gUyXllZWH5kL0wENGFef6KuJ57hW5lHx14eZasfdmgFN_1geUbxk5J&s=VFf9h_9FOlJBpRMIjim8tGuVnJ7GlWc5qZkrA9kmoMM&e=
Licenses: GPL 2+
Build system: r
Synopsis: Inductive Confidence Intervals for the Difference Between Two Proportions
Description:

This is a package for exact Confidence Intervals for the difference between two independent or dependent proportions.

r-ebprs 2.1.0
Propagated dependencies: r-rocr@1.0-11 r-data-table@1.17.8 r-bedmatrix@2.0.4
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=EBPRS
Licenses: GPL 3
Build system: r
Synopsis: Derive Polygenic Risk Score Based on Emprical Bayes Theory
Description:

EB-PRS is a novel method that leverages information for effect sizes across all the markers to improve the prediction accuracy. No parameter tuning is needed in the method, and no external information is needed. This R-package provides the calculation of polygenic risk scores from the given training summary statistics and testing data. We can use EB-PRS to extract main information, estimate Empirical Bayes parameters, derive polygenic risk scores for each individual in testing data, and evaluate the PRS according to AUC and predictive r2. See Song et al. (2020) <doi:10.1371/journal.pcbi.1007565> for a detailed presentation of the method.

r-ecostats 1.2.2
Propagated dependencies: r-mvtnorm@1.3-3 r-mvabund@4.2.1 r-mgcv@1.9-4 r-mass@7.3-65 r-get@1.0-7 r-ecocopula@1.0.6
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://dwarton.github.io/ecostats/
Licenses: LGPL 2.1+
Build system: r
Synopsis: Code and Data Accompanying the Eco-Stats Text (Warton 2022)
Description:

This package provides functions and data supporting the Eco-Stats text (Warton, 2022, Springer), and solutions to exercises. Functions include tools for using simulation envelopes in diagnostic plots, and a function for diagnostic plots of multivariate linear models. Datasets mentioned in the package are included here (where not available elsewhere) and there is a vignette for each chapter of the text with solutions to exercises.

r-engression 0.1.5
Propagated dependencies: r-torch@0.16.3
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/xwshen51/engression/
Licenses: Expat
Build system: r
Synopsis: Engression Modelling
Description:

Fits engression models for nonlinear distributional regression. Predictors and targets can be univariate or multivariate. Functionality includes estimation of conditional mean, estimation of conditional quantiles, or sampling from the fitted distribution. Training is done full-batch on CPU (the python version offers GPU-accelerated stochastic gradient descent). Based on "Engression: Extrapolation through the lens of distributional regression" by Xinwei Shen and Nicolai Meinshausen (2024) in JRSSB. Also supports classification (experimental). <doi:10.1093/jrsssb/qkae108>.

r-eikosograms 1.0.0
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/rwoldford/eikosograms
Licenses: GPL 3
Build system: r
Synopsis: Visualizing Probabilities, Frequencies, and Conditional Independence for Categorical Variates
Description:

An eikosogram (ancient Greek for probability picture) divides the unit square into rectangular regions whose areas, sides, and widths represent various probabilities associated with the values of one or more categorical variates. Rectangle areas are joint probabilities, widths are always marginal (though possibly joint margins, i.e. marginal joint distributions of two or more variates), and heights of rectangles are always conditional probabilities. Eikosograms embed the rules of probability and are useful for introducing elementary probability theory, including axioms, marginal, conditional, and joint probabilities, and their relationships (including Bayes theorem as a completely trivial consequence). They provide advantages over Venn diagrams for this purpose, particularly in distinguishing probabilistic independence, mutually exclusive events, coincident events, and associations. They also are useful for identifying and understanding conditional independence structure. Eikosograms can be thought of as mosaic plots when only two categorical variates are involved; the layout is quite different when there are more than two variates. Only one categorical variate, designated the "response", presents on the vertical axis and all others, designated the "conditioning" variates, appear on the horizontal. In this way, conditional probability appears only as height and marginal probabilities as widths. The eikosogram is ideal for response models (e.g. logistic models) but equally useful when no variate is distinguished as the response. In such cases, each variate can appear in turn as the response, which is handy for assessing conditional independence in discrete graphical models (i.e. "Bayesian networks" or "BayesNets"). The eikosogram and its value over Venn diagrams in teaching probability is described in W.H. Cherry and R.W. Oldford (2003) <https://math.uwaterloo.ca/~rwoldfor/papers/eikosograms/paper.pdf>, its value in exploring conditional independence structure and relation to graphical and log-linear models is described in R.W. Oldford (2003) <https://math.uwaterloo.ca/~rwoldfor/papers/eikosograms/independence/paper.pdf>, and a number of problems, puzzles, and paradoxes that are easily explained with eikosograms are given in R.W. Oldford (2003) <https://math.uwaterloo.ca/~rwoldfor/papers/eikosograms/examples/paper.pdf>.

r-eatme 0.1.0
Propagated dependencies: r-qcr@1.4
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=EATME
Licenses: GPL 3
Build system: r
Synopsis: Exponentially Weighted Moving Average with Adjustments to Measurement Error
Description:

The univariate statistical quality control tool aims to address measurement error effects when constructing exponentially weighted moving average p control charts. The method primarily focuses on binary random variables, but it can be applied to any continuous random variables by using sign statistic to transform them to discrete ones. With the correction of measurement error effects, we can obtain the corrected control limits of exponentially weighted moving average p control chart and reasonably adjusted exponentially weighted moving average p control charts. The methods in this package can be found in some relevant references, such as Chen and Yang (2022) <arXiv: 2203.03384>; Yang et al. (2011) <doi: 10.1016/j.eswa.2010.11.044>; Yang and Arnold (2014) <doi: 10.1155/2014/238719>; Yang (2016) <doi: 10.1080/03610918.2013.763980> and Yang and Arnold (2016) <doi: 10.1080/00949655.2015.1125901>.

r-emss 1.1.1
Propagated dependencies: r-sampleselection@1.2-14 r-mvtnorm@1.3-3
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/SangkyuStat/EMSS
Licenses: GPL 2
Build system: r
Synopsis: Some EM-Type Estimation Methods for the Heckman Selection Model
Description:

Some EM-type algorithms to estimate parameters for the well-known Heckman selection model are provided in the package. Such algorithms are as follow: ECM(Expectation/Conditional Maximization), ECM(NR)(the Newton-Raphson method is adapted to the ECM) and ECME(Expectation/Conditional Maximization Either). Since the algorithms are based on the EM algorithm, they also have EMâ s main advantages, namely, stability and ease of implementation. Further details and explanations of the algorithms can be found in Zhao et al. (2020) <doi: 10.1016/j.csda.2020.106930>.

r-eientropy 0.0.1.4
Propagated dependencies: 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://cran.r-project.org/package=EIEntropy
Licenses: GPL 3
Build system: r
Synopsis: Ecological Inference Applying Entropy
Description:

This package implements two estimations related to the foundations of info metrics applied to ecological inference. These methodologies assess the lack of disaggregated data and provide an approach to obtaining disaggregated territorial-level data. For more details, see the following references: Fernández-Vázquez, E., Dà az-Dapena, A., Rubiera-Morollón, F. et al. (2020) "Spatial Disaggregation of Social Indicators: An Info-Metrics Approach." <doi:10.1007/s11205-020-02455-z>. Dà az-Dapena, A., Fernández-Vázquez, E., Rubiera-Morollón, F., & Vinuela, A. (2021) "Mapping poverty at the local level in Europe: A consistent spatial disaggregation of the AROPE indicator for France, Spain, Portugal and the United Kingdom." <doi:10.1111/rsp3.12379>.

r-emmageo 0.9.9
Propagated dependencies: r-shiny@1.11.1 r-nnls@1.6 r-gparotation@2025.3-1 r-catools@1.18.3
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=EMMAgeo
Licenses: GPL 3
Build system: r
Synopsis: End-Member Modelling of Grain-Size Data
Description:

End-member modelling analysis of grain-size data is an approach to unmix a data set's underlying distributions and their contribution to the data set. EMMAgeo provides deterministic and robust protocols for that purpose.

r-entropyestimation 1.2.1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=EntropyEstimation
Licenses: GPL 3+
Build system: r
Synopsis: Estimation of Entropy and Related Quantities
Description:

This package contains methods for the estimation of Shannon's entropy, variants of Renyi's entropy, mutual information, Kullback-Leibler divergence, and generalized Simpson's indices. The estimators used have a bias that decays exponentially fast.

r-electdecomp 0.0.1-8
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=ElectDecomp
Licenses: FSDG-compatible
Build system: r
Synopsis: Decomposition of Seats-to-Votes Distortions
Description:

Analyses districted electoral systems of any magnitude by computing district-party conversion ratios and seats-to-votes deviations, decomposing the sources of deviation. Traditional indexes are also computed. References: Kedar, O., Harsgor, L. and Sheinerman, R.A. (2016). <doi:10.1111/ajps.12225>. Penades, A and Pavia, J.M. (2025) The decomposition of seats-to-votes distortion in elections: mean, variance, malapportionment and participation''. Acknowledgements: The authors wish to thank Consellerà a de Educación, Cultura, Universidades y Empleo, Generalitat Valenciana (grant CIACO/2023/031) for supporting this research.

r-eventtrack 1.0.4
Propagated dependencies: r-survival@3.8-3 r-muhaz@1.2.6.4
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=eventTrack
Licenses: GPL 2+
Build system: r
Synopsis: Event Prediction for Time-to-Event Endpoints
Description:

This package implements the hybrid framework for event prediction described in Fang & Zheng (2011, <doi:10.1016/j.cct.2011.05.013>). To estimate the survival function the event prediction is based on, a piecewise exponential hazard function is fit to the time-to-event data to infer the potential change points. Prior to the last identified change point, the survival function is estimated using Kaplan-Meier, and the tail after the change point is fit using piecewise exponential.

r-ecolmod 1.2.6.4
Propagated dependencies: r-shape@1.4.6.1 r-rootsolve@1.8.2.4 r-diagram@1.6.5 r-desolve@1.40
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=ecolMod
Licenses: GPL 2+
Build system: r
Synopsis: "A Practical Guide to Ecological Modelling - Using R as a Simulation Platform"
Description:

Figures, data sets and examples from the book "A practical guide to ecological modelling - using R as a simulation platform" by Karline Soetaert and Peter MJ Herman (2009). Springer. All figures from chapter x can be generated by "demo(chapx)", where x = 1 to 11. The R-scripts of the model examples discussed in the book are in subdirectory "examples", ordered per chapter. Solutions to model projects are in the same subdirectories.

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-eqrn 0.1.2
Propagated dependencies: r-torch@0.16.3 r-magrittr@2.0.4 r-ismev@1.43 r-future@1.68.0 r-foreach@1.5.2 r-evd@2.3-7.1 r-dofuture@1.1.2 r-coro@1.1.0
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/opasche/EQRN
Licenses: GPL 3+
Build system: r
Synopsis: Extreme Quantile Regression Neural Networks for Risk Forecasting
Description:

This framework enables forecasting and extrapolating measures of conditional risk (e.g. of extreme or unprecedented events), including quantiles and exceedance probabilities, using extreme value statistics and flexible neural network architectures. It allows for capturing complex multivariate dependencies, including dependencies between observations, such as sequential dependence (time-series). The methodology was introduced in Pasche and Engelke (2024) <doi:10.1214/24-AOAS1907> (also available in preprint: Pasche and Engelke (2022) <doi:10.48550/arXiv.2208.07590>).

r-epocakir 1.0.0
Propagated dependencies: r-units@1.0-0 r-tidyr@1.3.1 r-tibble@3.3.0 r-rlang@1.1.6 r-magrittr@2.0.4 r-lubridate@1.9.4 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/alwinw/epocakir
Licenses: Expat
Build system: r
Synopsis: Clinical Coding of Patients with Kidney Disease
Description:

Clinical coding and diagnosis of patients with kidney using clinical practice guidelines. The guidelines used are the evidence-based KDIGO guidelines, see <https://kdigo.org/guidelines/> for more information. This package covers acute kidney injury (AKI), anemia, and chronic kidney disease (CKD).

r-eyeread 0.0.4
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/SanVerhavert/eyeRead
Licenses: GPL 3
Build system: r
Synopsis: Prepare/Analyse Eye Tracking Data for Reading
Description:

This package provides functions to prepare and analyse eye tracking data of reading exercises. The functions allow some basic data preparations and code fixations as first and second pass. First passes can be further devided into forward and reading. The package further allows for aggregating fixation times per AOI or per AOI and per type of pass (first forward, first rereading, second). These methods are based on Hyönä, Lorch, and Rinck (2003) <doi:10.1016/B978-044451020-4/50018-9> and Hyönä, and Lorch (2004) <doi:10.1016/j.learninstruc.2004.01.001>. It is also possible to convert between metric length and visual degrees.

r-energyr 0.2
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/paulgovan/energyr
Licenses: FSDG-compatible
Build system: r
Synopsis: Data Published by the United States Federal Energy Regulatory Commission
Description:

Data published by the United States Federal Energy Regulatory Commission including electric company financial data, natural gas company financial data, hydropower plant data, liquified natural gas plant data, oil company financial data natural gas company financial data, and natural gas storage field data.

r-exteriormatch 1.0.0
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=exteriorMatch
Licenses: GPL 2
Build system: r
Synopsis: Constructs the Exterior Match from Two Matched Control Groups
Description:

If one treated group is matched to one control reservoir in two different ways to produce two sets of treated-control matched pairs, then the two control groups may be entwined, in the sense that some control individuals are in both control groups. The exterior match is used to compare the two control groups.

r-epico 1.0.1
Propagated dependencies: r-treemapify@2.6.0 r-spdep@1.4-1 r-scales@1.4.0 r-rlang@1.1.6 r-magrittr@2.0.4 r-lubridate@1.9.4 r-leaflet@2.2.3 r-incidence@1.7.6 r-igraph@2.2.1 r-ggraph@2.2.2 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://epiverse-trace.github.io/epiCo/
Licenses: Expat
Build system: r
Synopsis: Statistical and Viz Tools for Vector-Borne Diseases in Colombia
Description:

This package provides statistical and visualization tools for the analysis of demographic indicators, and spatio-temporal behavior and characterization of outbreaks of vector-borne diseases (VBDs) in Colombia. It implements travel times estimated in Bravo-Vega C., Santos-Vega M., & Cordovez J.M. (2022), and the endemic channel method (Bortman, M. (1999) <https://iris.paho.org/handle/10665.2/8562>).

r-ecolottery 1.0.2
Propagated dependencies: r-ggplot2@4.0.1 r-abc@2.2.2
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/frmunoz/ecolottery
Licenses: GPL 2+
Build system: r
Synopsis: Coalescent-Based Simulation of Ecological Communities
Description:

Coalescent-Based Simulation of Ecological Communities as proposed by Munoz et al. (2018) <doi:10.1111/2041-210X.12918>. The package includes a tool for estimating parameters of community assembly by using Approximate Bayesian Computation.

r-equil2 1.0.0
Propagated dependencies: r-units@1.0-0
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://billdenney.github.io/equil2/
Licenses: Expat
Build system: r
Synopsis: Calculate Urinary Saturation with the EQUIL2 Algorithm
Description:

Saturation of ionic substances in urine is calculated based on sodium, potassium, calcium, magnesium, ammonia, chloride, phosphate, sulfate, oxalate, citrate, ph, and urate. This program is intended for research use, only. The code within is translated from EQUIL2 Visual Basic code based on Werness, et al (1985) "EQUIL2: a BASIC computer program for the calculation of urinary saturation" <doi:10.1016/s0022-5347(17)47703-2> to R. The Visual Basic code was kindly provided by Dr. John Lieske of the Mayo Clinic.

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