_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
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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-esvis 0.3.1
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-sfsmisc@1.1-23 r-rlang@1.1.6 r-purrr@1.2.0 r-magrittr@2.0.4 r-hmisc@5.2-4 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/datalorax/esvis
Licenses: Expat
Build system: r
Synopsis: Visualization and Estimation of Effect Sizes
Description:

This package provides a variety of methods are provided to estimate and visualize distributional differences in terms of effect sizes. Particular emphasis is upon evaluating differences between two or more distributions across the entire scale, rather than at a single point (e.g., differences in means). For example, Probability-Probability (PP) plots display the difference between two or more distributions, matched by their empirical CDFs (see Ho and Reardon, 2012; <doi:10.3102/1076998611411918>), allowing for examinations of where on the scale distributional differences are largest or smallest. The area under the PP curve (AUC) is an effect-size metric, corresponding to the probability that a randomly selected observation from the x-axis distribution will have a higher value than a randomly selected observation from the y-axis distribution. Binned effect size plots are also available, in which the distributions are split into bins (set by the user) and separate effect sizes (Cohen's d) are produced for each bin - again providing a means to evaluate the consistency (or lack thereof) of the difference between two or more distributions at different points on the scale. Evaluation of empirical CDFs is also provided, with built-in arguments for providing annotations to help evaluate distributional differences at specific points (e.g., semi-transparent shading). All function take a consistent argument structure. Calculation of specific effect sizes is also possible. The following effect sizes are estimable: (a) Cohen's d, (b) Hedges g, (c) percentage above a cut, (d) transformed (normalized) percentage above a cut, (e) area under the PP curve, and (f) the V statistic (see Ho, 2009; <doi:10.3102/1076998609332755>), which essentially transforms the area under the curve to standard deviation units. By default, effect sizes are calculated for all possible pairwise comparisons, but a reference group (distribution) can be specified.

r-eix 1.2.1
Propagated dependencies: r-xgboost@1.7.11.1 r-tidyr@1.3.1 r-scales@1.4.0 r-purrr@1.2.0 r-mass@7.3-65 r-ibreakdown@2.1.2 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-ggiraphextra@0.3.0 r-data-table@1.17.8 r-dalex@2.5.3
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/ModelOriented/EIX
Licenses: GPL 2
Build system: r
Synopsis: Explain Interactions in 'XGBoost'
Description:

Structure mining from XGBoost and LightGBM models. Key functionalities of this package cover: visualisation of tree-based ensembles models, identification of interactions, measuring of variable importance, measuring of interaction importance, explanation of single prediction with break down plots (based on xgboostExplainer and iBreakDown packages). To download the LightGBM use the following link: <https://github.com/Microsoft/LightGBM>. EIX is a part of the DrWhy.AI universe.

r-exif 0.1.1
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/Ironholds/exif
Licenses: FreeBSD
Build system: r
Synopsis: Read EXIF Metadata from JPEGs
Description:

Extracts Exchangeable Image File Format (EXIF) metadata, such as camera make and model, ISO speed and the date-time the picture was taken on, from JPEG images. Incorporates the easyexif <https://github.com/mayanklahiri/easyexif> library.

r-evesim 1.0.0
Propagated dependencies: r-rcppparallel@5.1.11-1 r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: http://qtj.me/evesim/
Licenses: Expat
Build system: r
Synopsis: Evolution Emulator: Species Diversification under an Evolutionary Relatedness Dependent Scenario
Description:

Evolutionary relatedness dependent diversification simulation powered by the Rcpp back-end SimTable'.

r-ediutils 2.0.0
Propagated dependencies: r-xml2@1.5.0 r-jsonlite@2.0.0 r-httr@1.4.7 r-curl@7.0.0
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/ropensci/EDIutils
Licenses: Expat
Build system: r
Synopsis: An API Client for the Environmental Data Initiative Repository
Description:

This package provides a client for the Environmental Data Initiative repository REST API. The EDI data repository <https://portal.edirepository.org/nis/home.jsp> is for publication and reuse of ecological data with emphasis on metadata accuracy and completeness. It is built upon the PASTA+ software stack <https://pastaplus-core.readthedocs.io/en/latest/index.html#> and was developed in collaboration with the US LTER Network <https://lternet.edu/>. EDIutils includes functions to search and access existing data, evaluate and upload new data, and assist other data management tasks common to repository users.

r-ensemblemos 0.8.2
Propagated dependencies: r-evd@2.3-7.1 r-ensemblebma@5.1.8 r-chron@2.3-62
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=ensembleMOS
Licenses: GPL 2+
Build system: r
Synopsis: Ensemble Model Output Statistics
Description:

Ensemble Model Output Statistics to create probabilistic forecasts from ensemble forecasts and weather observations.

r-easyr 0.5-11
Propagated dependencies: r-xml@3.99-0.20 r-stringr@1.6.0 r-rprojroot@2.1.1 r-rlang@1.1.6 r-readxl@1.4.5 r-lubridate@1.9.4 r-hmisc@5.2-4 r-glue@1.8.0 r-foreign@0.8-90 r-dplyr@1.1.4 r-digest@0.6.39 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/oliver-wyman-actuarial/easyr
Licenses: GPL 2+
Build system: r
Synopsis: Helpful Functions from Oliver Wyman Actuarial Consulting
Description:

Makes difficult operations easy. Includes these types of functions: shorthand, type conversion, data wrangling, and work flow. Also includes some helpful data objects: NA strings, U.S. state list, color blind charting colors. Built and shared by Oliver Wyman Actuarial Consulting. Accepting proposed contributions through GitHub.

r-elochoice 0.29.4
Propagated dependencies: r-rdpack@2.6.4 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-psychotools@0.7-5
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/gobbios/EloChoice
Licenses: GPL 3+
Build system: r
Synopsis: Preference Rating for Visual Stimuli Based on Elo Ratings
Description:

Allows calculating global scores for characteristics of visual stimuli as assessed by human raters. Stimuli are presented as sequence of pairwise comparisons ('contests'), during each of which a rater expresses preference for one stimulus over the other (forced choice). The algorithm for calculating global scores is based on Elo rating, which updates individual scores after each single pairwise contest. Elo rating is widely used to rank chess players according to their performance. Its core feature is that dyadic contests with expected outcomes lead to smaller changes of participants scores than outcomes that were unexpected. As such, Elo rating is an efficient tool to rate individual stimuli when a large number of such stimuli are paired against each other in the context of experiments where the goal is to rank stimuli according to some characteristic of interest. Clark et al (2018) <doi:10.1371/journal.pone.0190393> provide details.

r-ergmito 0.3-2
Propagated dependencies: r-texreg@1.39.5 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-network@1.19.0 r-mass@7.3-65 r-ergm@4.11.0
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://muriteams.github.io/ergmito/
Licenses: Expat
Build system: r
Synopsis: Exponential Random Graph Models for Small Networks
Description:

Simulation and estimation of Exponential Random Graph Models (ERGMs) for small networks using exact statistics as shown in Vega Yon et al. (2020) <DOI:10.1016/j.socnet.2020.07.005>. As a difference from the ergm package, ergmito circumvents using Markov-Chain Maximum Likelihood Estimator (MC-MLE) and instead uses Maximum Likelihood Estimator (MLE) to fit ERGMs for small networks. As exhaustive enumeration is computationally feasible for small networks, this R package takes advantage of this and provides tools for calculating likelihood functions, and other relevant functions, directly, meaning that in many cases both estimation and simulation of ERGMs for small networks can be faster and more accurate than simulation-based algorithms.

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-experimentr 0.1.0
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=experimentr
Licenses: Expat
Build system: r
Synopsis: Datasets Used in Social Science Experiments: A Hands-on Introduction
Description:

This package contains all the datasets that were used in Social Science Experiments: A Hands-On Introduction and in its R Companion. Relevant materials can be found at <https://osf.io/b78je>.

r-enveomics-r 1.10.0
Propagated dependencies: r-sn@2.1.1 r-investr@1.4.2 r-fitdistrplus@1.2-4
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: http://enve-omics.ce.gatech.edu/enveomics/
Licenses: Artistic License 2.0
Build system: r
Synopsis: Various Utilities for Microbial Genomics and Metagenomics
Description:

This package provides a collection of functions for microbial ecology and other applications of genomics and metagenomics. Companion package for the Enveomics Collection (Rodriguez-R, L.M. and Konstantinidis, K.T., 2016 <DOI:10.7287/peerj.preprints.1900v1>).

r-evtree 1.0-8
Propagated dependencies: r-partykit@1.2-24
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=evtree
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: Evolutionary Learning of Globally Optimal Trees
Description:

Commonly used classification and regression tree methods like the CART algorithm are recursive partitioning methods that build the model in a forward stepwise search. Although this approach is known to be an efficient heuristic, the results of recursive tree methods are only locally optimal, as splits are chosen to maximize homogeneity at the next step only. An alternative way to search over the parameter space of trees is to use global optimization methods like evolutionary algorithms. The evtree package implements an evolutionary algorithm for learning globally optimal classification and regression trees in R. CPU and memory-intensive tasks are fully computed in C++ while the partykit package is leveraged to represent the resulting trees in R, providing unified infrastructure for summaries, visualizations, and predictions.

r-ecodist 2.1.3
Propagated dependencies: r-igraph@2.2.1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=ecodist
Licenses: GPL 2+
Build system: r
Synopsis: Dissimilarity-Based Functions for Ecological Analysis
Description:

Dissimilarity-based analysis functions including ordination and Mantel test functions, intended for use with spatial and community ecological data. The original package description is in Goslee and Urban (2007) <doi:10.18637/jss.v022.i07>, with further statistical detail in Goslee (2010) <doi:10.1007/s11258-009-9641-0>.

r-edfinr 0.1.1
Propagated dependencies: r-tibble@3.3.0 r-rlang@1.1.6 r-dplyr@1.1.4 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/bellwetherorg/edfinr
Licenses: FSDG-compatible
Build system: r
Synopsis: Access Tidy Education Finance Data
Description:

This package provides easy access to tidy education finance data using Bellwether's methodology to combine NCES F-33 Survey, Census Bureau Small Area Income Poverty Estimates (SAIPE), and community data from the ACS 5-Year Estimates. The package simplifies downloading, caching, and filtering education finance data by year and state, enabling researchers and analysts to explore K-12 education funding patterns, revenue sources, expenditure categories, and demographic factors across U.S. school districts.

r-epifitter 0.3.0
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-minpack-lm@1.2-4 r-magrittr@2.0.4 r-knitr@1.50 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-desolve@1.40 r-desctools@0.99.60
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/AlvesKS/epifitter
Licenses: Expat
Build system: r
Synopsis: Analysis and Simulation of Plant Disease Progress Curves
Description:

Analysis and visualization of plant disease progress curve data. Functions for fitting two-parameter population dynamics models (exponential, monomolecular, logistic and Gompertz) to proportion data for single or multiple epidemics using either linear or no-linear regression. Statistical and visual outputs are provided to aid in model selection. Synthetic curves can be simulated for any of the models given the parameters. See Laurence V. Madden, Gareth Hughes, and Frank van den Bosch (2007) <doi:10.1094/9780890545058> for further information on the methods.

r-emf 0.2.0
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=emf
Licenses: GPL 3
Build system: r
Synopsis: Ecosystem Multifunctionality: Richness, Divergence, and Regularity
Description:

Analyzes and quantifies ecosystem multifunctionality with functions to calculate multifunctionality richness (MFric), multifunctionality divergence (MFdiv), and multifunctionality regularity (MFreg). These indices help assess the relationship between biodiversity and multiple ecosystem functions. For more details, see Byrnes et al. (2014) <doi:10.1111/2041-210X.12143> and Chao et al. (2024) <doi:10.1111/ele.14336>.

r-excursions 2.5.11
Dependencies: gsl@2.8
Propagated dependencies: r-withr@3.0.2 r-matrix@1.7-4 r-lifecycle@1.0.4 r-fmesher@0.5.0
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/davidbolin/excursions
Licenses: GPL 3+
Build system: r
Synopsis: Excursion Sets and Contour Credibility Regions for Random Fields
Description:

This package provides functions that compute probabilistic excursion sets, contour credibility regions, contour avoiding regions, and simultaneous confidence bands for latent Gaussian random processes and fields. The package also contains functions that calculate these quantities for models estimated with the INLA package. The main references for excursions are Bolin and Lindgren (2015) <doi:10.1111/rssb.12055>, Bolin and Lindgren (2017) <doi:10.1080/10618600.2016.1228537>, and Bolin and Lindgren (2018) <doi:10.18637/jss.v086.i05>. These can be generated by the citation function in R.

r-exams-forge-data 0.1.3
Propagated dependencies: r-exams-forge@1.0.12 r-exams@2.4-3
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=exams.forge.data
Licenses: GPL 3
Build system: r
Synopsis: Sample and Precomputed Data for Use with 'exams.forge'
Description:

This package provides a small collection of datasets supporting Pearson correlation and linear regression analysis. It includes the precomputed dataset sos100', with integer values summing to zero and squared sum equal to 100. For other values of n and user-defined parameters, the sos() function from the exams.forge package can be used to generate datasets on the fly. In addition, the package contains around 500 german R Markdown exercises that illustrate the usage of exams.forge commands.

r-ebmc 1.0.1
Propagated dependencies: r-smotefamily@1.4.0 r-rpart@4.1.24 r-randomforest@4.7-1.2 r-proc@1.19.0.1 r-e1071@1.7-16 r-c50@0.2.0
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cran.r-project.org/package=ebmc
Licenses: GPL 3+
Build system: r
Synopsis: Ensemble-Based Methods for Class Imbalance Problem
Description:

Four ensemble-based methods (SMOTEBoost, RUSBoost, UnderBagging, and SMOTEBagging) for class imbalance problem are implemented for binary classification. Such methods adopt ensemble methods and data re-sampling techniques to improve model performance in presence of class imbalance problem. One special feature offers the possibility to choose multiple supervised learning algorithms to build weak learners within ensemble models. References: Nitesh V. Chawla, Aleksandar Lazarevic, Lawrence O. Hall, and Kevin W. Bowyer (2003) <doi:10.1007/978-3-540-39804-2_12>, Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, and Amri Napolitano (2010) <doi:10.1109/TSMCA.2009.2029559>, R. Barandela, J. S. Sanchez, R. M. Valdovinos (2003) <doi:10.1007/s10044-003-0192-z>, Shuo Wang and Xin Yao (2009) <doi:10.1109/CIDM.2009.4938667>, Yoav Freund and Robert E. Schapire (1997) <doi:10.1006/jcss.1997.1504>.

r-eye 1.3.0
Propagated dependencies: r-tidyselect@1.2.1 r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-rlang@1.1.6 r-purrr@1.2.0 r-pillar@1.11.1 r-magrittr@2.0.4 r-lubridate@1.9.4 r-english@1.2-6 r-dplyr@1.1.4 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://github.com/tjebo/eye
Licenses: Expat
Build system: r
Synopsis: Analysis of Eye Data
Description:

There is no ophthalmic researcher who has not had headaches from the handling of visual acuity entries. Different notations, untidy entries. This shall now be a matter of the past. Eye makes it as easy as pie to work with VA data - easy cleaning, easy conversion between Snellen, logMAR, ETDRS letters, and qualitative visual acuity shall never pester you again. The eye package automates the pesky task to count number of patients and eyes, and can help to clean data with easy re-coding for right and left eyes. It also contains functions to help reshaping eye side specific variables between wide and long format. Visual acuity conversion is based on Schulze-Bonsel et al. (2006) <doi:10.1167/iovs.05-0981>, Gregori et al. (2010) <doi:10.1097/iae.0b013e3181d87e04>, Beck et al. (2003) <doi:10.1016/s0002-9394(02)01825-1> and Bach (2007) <https://michaelbach.de/sci/acuity.html>.

r-eggcounts 2.5-1
Propagated dependencies: r-stanheaders@2.32.10 r-rstantools@2.5.0 r-rstan@2.32.7 r-rootsolve@1.8.2.4 r-rcppparallel@5.1.11-1 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-numbers@0.9-2 r-lattice@0.22-7 r-coda@0.19-4.1 r-boot@1.3-32 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://www.math.uzh.ch/pages/eggcount/
Licenses: GPL 3+
Build system: r
Synopsis: Hierarchical Modelling of Faecal Egg Counts
Description:

An implementation of Bayesian hierarchical models for faecal egg count data to assess anthelmintic efficacy. Bayesian inference is done via MCMC sampling using Stan <https://mc-stan.org/>.

r-ecodive 2.2.2
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://cmmr.github.io/ecodive/
Licenses: Expat
Build system: r
Synopsis: Parallel and Memory-Efficient Ecological Diversity Metrics
Description:

Computes alpha and beta diversity metrics using concurrent C threads. Metrics include UniFrac', Faith's phylogenetic diversity, Bray-Curtis dissimilarity, Shannon diversity index, and many others. Also parses newick trees into phylo objects and rarefies feature tables.

r-eigenmodel 1.12
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://pdhoff.github.io/
Licenses: GPL 2
Build system: r
Synopsis: Semiparametric Factor and Regression Models for Symmetric Relational Data
Description:

Estimation of the parameters in a model for symmetric relational data (e.g., the above-diagonal part of a square matrix), using a model-based eigenvalue decomposition and regression. Missing data is accommodated, and a posterior mean for missing data is calculated under the assumption that the data are missing at random. The marginal distribution of the relational data can be arbitrary, and is fit with an ordered probit specification. See Hoff (2007) <doi:10.48550/arXiv.0711.1146>. for details on the model.

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Total results: 68658