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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-opgmmassessment 0.4
Propagated dependencies: r-rlang@1.1.6 r-nbclust@3.0.1 r-multimode@1.5 r-mixtools@2.0.0.1 r-mixak@5.8 r-mclust@6.1.2 r-ggplot2@4.0.1 r-foreach@1.5.2 r-dplyr@1.1.4 r-doparallel@1.0.17 r-distributionoptimization@1.2.6 r-datavisualizations@1.4.0 r-clusterr@1.3.5 r-cluster@2.1.8.1 r-catools@1.18.3 r-adaptgauss@1.6
Channel: guix-cran
Location: guix-cran/packages/o.scm (guix-cran packages o)
Home page: https://cran.r-project.org/package=opGMMassessment
Licenses: GPL 3
Build system: r
Synopsis: Optimized Automated Gaussian Mixture Assessment
Description:

Necessary functions for optimized automated evaluation of the number and parameters of Gaussian mixtures in one-dimensional data. Various methods are available for parameter estimation and for determining the number of modes in the mixture. A detailed description of the methods ca ben found in Lotsch, J., Malkusch, S. and A. Ultsch. (2022) <doi:10.1016/j.imu.2022.101113>.

r-onam 1.0.1
Propagated dependencies: r-scales@1.4.0 r-rlang@1.1.6 r-reticulate@1.44.1 r-proc@1.19.0.1 r-keras3@1.5.0 r-ggplot2@4.0.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/o.scm (guix-cran packages o)
Home page: https://cran.r-project.org/package=ONAM
Licenses: Expat
Build system: r
Synopsis: Fitting Interpretable Neural Additive Models Using Orthogonalization
Description:

An algorithm for fitting interpretable additive neural networks for identifiable and visualizable feature effects using post hoc orthogonalization. Fit custom neural networks intuitively using established R formula notation, including interaction effects of arbitrary order while preserving identifiability to enable a functional decomposition of the prediction function. For more details see Koehler et al. (2025) <doi:10.1038/s44387-025-00033-7>.

r-opdisdownsampling 1.0.1
Propagated dependencies: r-twosamples@2.0.1 r-pracma@2.4.6 r-pbmcapply@1.5.1 r-foreach@1.5.2 r-doparallel@1.0.17 r-catools@1.18.3
Channel: guix-cran
Location: guix-cran/packages/o.scm (guix-cran packages o)
Home page: https://cran.r-project.org/package=opdisDownsampling
Licenses: GPL 3
Build system: r
Synopsis: Optimal Distribution Preserving Down-Sampling of Bio-Medical Data
Description:

An optimized method for distribution-preserving class-proportional down-sampling of bio-medical data.

r-ordinalrr 1.1
Propagated dependencies: r-rjags@4-17
Channel: guix-cran
Location: guix-cran/packages/o.scm (guix-cran packages o)
Home page: https://cran.r-project.org/package=ordinalRR
Licenses: GPL 2
Build system: r
Synopsis: Analysis of Repeatability and Reproducibility Studies with Ordinal Measurements
Description:

This package implements Bayesian data analyses of balanced repeatability and reproducibility studies with ordinal measurements. Model fitting is based on MCMC posterior sampling with rjags'. Function ordinalRR() directly carries out the model fitting, and this function has the flexibility to allow the user to specify key aspects of the model, e.g., fixed versus random effects. Functions for preprocessing data and for the numerical and graphical display of a fitted model are also provided. There are also functions for displaying the model at fixed (user-specified) parameters and for simulating a hypothetical data set at a fixed (user-specified) set of parameters for a random-effects rater population. For additional technical details, refer to Culp, Ryan, Chen, and Hamada (2018) and cite this Technometrics paper when referencing any aspect of this work. The demo of this package reproduces results from the Technometrics paper.

r-offlinechange 0.0.4
Propagated dependencies: r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/o.scm (guix-cran packages o)
Home page: https://cran.r-project.org/package=offlineChange
Licenses: GPL 3
Build system: r
Synopsis: Detect Multiple Change Points from Time Series
Description:

Detect the number and locations of change points. The locations can be either exact or in terms of ranges, depending on the available computational resource. The method is based on Jie Ding, Yu Xiang, Lu Shen, Vahid Tarokh (2017) <doi:10.1109/TSP.2017.2711558>.

r-ohsome 0.2.2
Propagated dependencies: r-sf@1.0-23 r-readr@2.1.6 r-jsonlite@2.0.0 r-httr@1.4.7 r-geojsonsf@2.0.5
Channel: guix-cran
Location: guix-cran/packages/o.scm (guix-cran packages o)
Home page: https://github.com/GIScience/ohsome-r
Licenses: LGPL 3+
Build system: r
Synopsis: An 'ohsome API' Client
Description:

This package provides a client that grants access to the power of the ohsome API from R. It lets you analyze the rich data source of the OpenStreetMap (OSM) history. You can retrieve the geometry of OSM data at specific points in time, and you can get aggregated statistics on the evolution of OSM elements and specify your own temporal, spatial and/or thematic filters.

r-olympicrshiny 1.0.2
Propagated dependencies: r-summarytools@1.1.4 r-shinythemes@1.2.0 r-shinybusy@0.3.3 r-shiny@1.11.1 r-golem@0.5.1 r-ggplot2@4.0.1 r-forcats@1.0.1 r-dplyr@1.1.4 r-config@0.3.2
Channel: guix-cran
Location: guix-cran/packages/o.scm (guix-cran packages o)
Home page: https://github.com/Amalan-ConStat/OlympicRshiny
Licenses: Expat
Build system: r
Synopsis: 'Shiny' Application for Olympic Data
Description:

Shiny Application to visualize Olympic Data. From 1896 to 2016. Even Winter Olympics events are included. Data is from Kaggle at <https://www.kaggle.com/heesoo37/120-years-of-olympic-history-athletes-and-results>.

r-owea 0.1.2
Propagated dependencies: r-shiny@1.11.1 r-mass@7.3-65 r-gtools@3.9.5
Channel: guix-cran
Location: guix-cran/packages/o.scm (guix-cran packages o)
Home page: https://cran.r-project.org/package=OWEA
Licenses: GPL 3
Build system: r
Synopsis: Optimal Weight Exchange Algorithm for Optimal Designs for Three Models
Description:

An implementation of optimal weight exchange algorithm Yang(2013) <doi:10.1080/01621459.2013.806268> for three models. They are Crossover model with subject dropout, crossover model with proportional first order residual effects and interference model. You can use it to find either A-opt or D-opt approximate designs. Exact designs can be automatically rounded from approximate designs and relative efficiency is provided as well.

r-outlierso3 0.6.3
Propagated dependencies: r-tidyr@1.3.1 r-robustx@1.2-7 r-robustbase@0.99-6 r-rlist@0.4.6.2 r-memisc@0.99.31.8.3 r-hdoutliers@1.0.4 r-ggplot2@4.0.1 r-ggally@2.4.0 r-forcats@1.0.1 r-fastpcs@0.1.4 r-dplyr@1.1.4 r-cellwise@2.5.5
Channel: guix-cran
Location: guix-cran/packages/o.scm (guix-cran packages o)
Home page: https://cran.r-project.org/package=OutliersO3
Licenses: GPL 2+
Build system: r
Synopsis: Draws Overview of Outliers (O3) Plots
Description:

Potential outliers are identified for all combinations of a dataset's variables. O3 plots are described in Unwin(2019) <doi:10.1080/10618600.2019.1575226>. The available methods are HDoutliers() from the package HDoutliers', FastPCS() from the package FastPCS', mvBACON() from robustX', adjOutlyingness() from robustbase', DectectDeviatingCells() from cellWise', covMcd() from robustbase'.

r-openml 1.12
Propagated dependencies: r-xml@3.99-0.20 r-stringi@1.8.7 r-memoise@2.0.1 r-jsonlite@2.0.0 r-httr@1.4.7 r-digest@0.6.39 r-data-table@1.17.8 r-curl@7.0.0 r-checkmate@2.3.3 r-bbmisc@1.13 r-backports@1.5.0
Channel: guix-cran
Location: guix-cran/packages/o.scm (guix-cran packages o)
Home page: https://github.com/openml/openml-r
Licenses: Modified BSD
Build system: r
Synopsis: Open Machine Learning and Open Data Platform
Description:

We provide an R interface to OpenML.org which is an online machine learning platform where researchers can access open data, download and upload data sets, share their machine learning tasks and experiments and organize them online to work and collaborate with other researchers. The R interface allows to query for data sets with specific properties, and allows the downloading and uploading of data sets, tasks, flows and runs. See <https://www.openml.org/guide/api> for more information.

r-omicsense 0.2.0
Propagated dependencies: r-kernlab@0.9-33 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/o.scm (guix-cran packages o)
Home page: <https://github.com/takakoizumi/OmicSense>
Licenses: GPL 3+
Build system: r
Synopsis: Biosensor Development using Omics Data
Description:

This package provides a method for the quantitative prediction using omics data. This package provides functions to construct the quantitative prediction model using omics data.

r-occumb 1.2.2
Dependencies: jags@4.3.1
Propagated dependencies: r-knitr@1.50 r-jagsui@1.6.3 r-crayon@1.5.3 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/o.scm (guix-cran packages o)
Home page: https://fukayak.github.io/occumb/
Licenses: GPL 3+
Build system: r
Synopsis: Site Occupancy Modeling for Environmental DNA Metabarcoding
Description:

Fits community site occupancy models to environmental DNA metabarcoding data collected using spatially-replicated survey design. Model fitting results can be used to evaluate and compare the effectiveness of species detection to find an efficient survey design. Reference: Fukaya et al. (2022) <doi:10.1111/2041-210X.13732>, Fukaya and Hasebe (2025) <doi:10.1002/1438-390X.12219>.

r-ordinallbm 1.0
Propagated dependencies: r-reshape2@1.4.5 r-rcolorbrewer@1.1-3
Channel: guix-cran
Location: guix-cran/packages/o.scm (guix-cran packages o)
Home page: https://cran.r-project.org/package=ordinalLBM
Licenses: GPL 2+
Build system: r
Synopsis: Co-Clustering of Ordinal Data via Latent Continuous Random Variables
Description:

It implements functions for simulation and estimation of the ordinal latent block model (OLBM), as described in Corneli, Bouveyron and Latouche (2019).

r-observation 0.3.0
Propagated dependencies: r-svdialogs@1.1.1
Channel: guix-cran
Location: guix-cran/packages/o.scm (guix-cran packages o)
Home page: https://github.com/paulhibbing/Observation
Licenses: GPL 3 FSDG-compatible
Build system: r
Synopsis: Collect and Process Physical Activity Direct Observation Data
Description:

Two-part system for first collecting then managing direct observation data, as described by Hibbing PR, Ellingson LD, Dixon PM, & Welk GJ (2018) <doi:10.1249/MSS.0000000000001486>.

r-orddisp 2.1.2
Propagated dependencies: r-vgam@1.1-13
Channel: guix-cran
Location: guix-cran/packages/o.scm (guix-cran packages o)
Home page: https://cran.r-project.org/package=ordDisp
Licenses: GPL 2
Build system: r
Synopsis: Separating Location and Dispersion in Ordinal Regression Models
Description:

Estimate location-shift models or rating-scale models accounting for response styles (RSRS) for the regression analysis of ordinal responses.

r-ordfacreg 1.0.6
Propagated dependencies: r-survival@3.8-3 r-mass@7.3-65 r-eha@2.11.5
Channel: guix-cran
Location: guix-cran/packages/o.scm (guix-cran packages o)
Home page: http://www.kasparrufibach.ch
Licenses: GPL 2+
Build system: r
Synopsis: Least Squares, Logistic, and Cox-Regression with Ordered Predictors
Description:

In biomedical studies, researchers are often interested in assessing the association between one or more ordinal explanatory variables and an outcome variable, at the same time adjusting for covariates of any type. The outcome variable may be continuous, binary, or represent censored survival times. In the absence of a precise knowledge of the response function, using monotonicity constraints on the ordinal variables improves efficiency in estimating parameters, especially when sample sizes are small. This package implements an active set algorithm that efficiently computes such estimators.

r-openebgm 0.9.1
Propagated dependencies: r-ggplot2@4.0.1 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/o.scm (guix-cran packages o)
Home page: https://journal.r-project.org/archive/2017/RJ-2017-063/index.html
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: EBGM Disproportionality Scores for Adverse Event Data Mining
Description:

An implementation of DuMouchel's (1999) <doi:10.1080/00031305.1999.10474456> Bayesian data mining method for the market basket problem. Calculates Empirical Bayes Geometric Mean (EBGM) and posterior quantile scores using the Gamma-Poisson Shrinker (GPS) model to find unusually large cell counts in large, sparse contingency tables. Can be used to find unusually high reporting rates of adverse events associated with products. In general, can be used to mine any database where the co-occurrence of two variables or items is of interest. Also calculates relative and proportional reporting ratios. Builds on the work of the PhViD package, from which much of the code is derived. Some of the added features include stratification to adjust for confounding variables and data squashing to improve computational efficiency. Includes an implementation of the EM algorithm for hyperparameter estimation loosely derived from the mederrRank package.

r-ocf 1.0.3
Propagated dependencies: r-tidyr@1.3.1 r-stringr@1.6.0 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-ranger@0.17.0 r-orf@0.1.4 r-matrix@1.7-4 r-magrittr@2.0.4 r-glmnet@4.1-10 r-ggplot2@4.0.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/o.scm (guix-cran packages o)
Home page: https://riccardo-df.github.io/ocf/
Licenses: GPL 3
Build system: r
Synopsis: Ordered Correlation Forest
Description:

Machine learning estimator specifically optimized for predictive modeling of ordered non-numeric outcomes. ocf provides forest-based estimation of the conditional choice probabilities and the covariatesâ marginal effects. Under an "honesty" condition, the estimates are consistent and asymptotically normal and standard errors can be obtained by leveraging the weight-based representation of the random forest predictions. Please reference the use as Di Francesco (2025) <doi:10.1080/07474938.2024.2429596>.

r-onage 1.0.2
Channel: guix-cran
Location: guix-cran/packages/o.scm (guix-cran packages o)
Home page: https://lbbe.univ-lyon1.fr/fr/onage
Licenses: GPL 3
Build system: r
Synopsis: Test of Between-Group Differences in the Onset of Senescence
Description:

Implementation of a likelihood ratio test of differential onset of senescence between two groups. Given two groups with measures of age and of an individual trait likely to be subjected to senescence (e.g. body mass), OnAge provides an asymptotic p-value for the null hypothesis that senescence starts at the same age in both groups. The package implements the procedure used in Douhard et al. (2017) <doi:10.1111/oik.04421>.

r-ottr 1.5.3
Propagated dependencies: r-zip@2.3.3 r-testthat@3.3.0 r-r6@2.6.1 r-jsonlite@2.0.0
Channel: guix-cran
Location: guix-cran/packages/o.scm (guix-cran packages o)
Home page: https://cran.r-project.org/package=ottr
Licenses: Modified BSD
Build system: r
Synopsis: An R Autograding Extension for Otter-Grader
Description:

An R autograding extension for Otter-Grader (<https://otter-grader.readthedocs.io>). It supports grading R scripts, R Markdown documents, and R Jupyter Notebooks.

r-opendataformat 2.2.2
Propagated dependencies: r-zip@2.3.3 r-xml2@1.5.0 r-tibble@3.3.0 r-magrittr@2.0.4 r-jsonlite@2.0.0 r-data-table@1.17.8 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/o.scm (guix-cran packages o)
Home page: https://github.com/opendataformat/r-package-opendataformat
Licenses: Expat
Build system: r
Synopsis: Reading and Writing Open Data Format Files
Description:

The Open Data Format (ODF) is a new, non-proprietary, multilingual, metadata enriched, and zip-compressed data format with metadata structured in the Data Documentation Initiative (DDI) Codebook standard. This package allows reading and writing of data files in the Open Data Format (ODF) in R, and displaying metadata in different languages. For further information on the Open Data Format, see <https://opendataformat.github.io/>.

r-optconerrf 1.0.1
Propagated dependencies: r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/o.scm (guix-cran packages o)
Home page: https://github.com/morten-dreher/optconerrf
Licenses: GPL 3+
Build system: r
Synopsis: Optimal Monotone Conditional Error Functions
Description:

Design and analysis of confirmatory adaptive clinical trials using the optimal conditional error framework according to Brannath and Bauer (2004) <doi:10.1111/j.0006-341X.2004.00221.x>. An extension to the optimal conditional error function using interim estimates as described in Brannath and Dreher (2024) <doi:10.48550/arXiv.2402.00814> and functions to ensure that the resulting conditional error function is non-increasing are also available.

r-onsvplot 0.3.2
Propagated dependencies: r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/o.scm (guix-cran packages o)
Home page: https://github.com/pabsantos/onsvplot/
Licenses: Expat
Build system: r
Synopsis: National Road Safety Observatory (ONSV) Style for 'ggplot2' Graphics
Description:

Helps to create ggplot2 charts in the style used by the National Road Safety Observatory (ONSV). The package includes functions to customize ggplot2 objects with new theme and colors.

r-orderly 2.0.3
Propagated dependencies: r-yaml@2.3.10 r-withr@3.0.2 r-vctrs@0.6.5 r-rstudioapi@0.17.1 r-rlang@1.1.6 r-r6@2.6.1 r-pkgload@1.4.1 r-openssl@2.3.4 r-jsonlite@2.0.0 r-httr2@1.2.1 r-gert@2.2.0 r-fs@1.6.6 r-diffobj@0.3.6 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/o.scm (guix-cran packages o)
Home page: https://github.com/mrc-ide/orderly
Licenses: Expat
Build system: r
Synopsis: Lightweight Reproducible Reporting
Description:

Distributed reproducible computing framework, adopting ideas from git, docker and other software. By defining a lightweight interface around the inputs and outputs of an analysis, a lot of the repetitive work for reproducible research can be automated. We define a simple format for organising and describing work that facilitates collaborative reproducible research and acknowledges that all analyses are run multiple times over their lifespans.

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