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      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
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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-ccpsyc 0.2.6
Propagated dependencies: r-xlsx@0.6.5 r-ufs@25.7.1 r-tidyr@1.3.1 r-tibble@3.3.0 r-rlang@1.1.6 r-readr@2.1.6 r-rcppalgos@2.9.3 r-psych@2.5.6 r-mcmcpack@1.7-1 r-magrittr@2.0.4 r-lavaan@0.6-20 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=ccpsyc
Licenses: GPL 3
Build system: r
Synopsis: Methods for Cross-Cultural Psychology
Description:

With the development of new cross-cultural methods this package is intended to combine multiple functions automating and simplifying functions providing a unified analysis approach for commonly employed methods.

r-compdist 1.0
Propagated dependencies: r-vgam@1.1-13 r-rmutil@1.1.10 r-pearsonds@1.3.2 r-numderiv@2016.8-1.1 r-fextremes@4032.84 r-actuar@3.3-6
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=CompDist
Licenses: GPL 2+
Build system: r
Synopsis: Multisection Composite Distributions
Description:

Computes density function, cumulative distribution function, quantile function and random numbers for a multisection composite distribution specified by the user. Also fits the user specified distribution to a given data set. More details of the package can be found in the following paper submitted to the R journal Wiegand M and Nadarajah S (2017) CompDist: Multisection composite distributions.

r-characterization 2.2.0
Propagated dependencies: r-sqlrender@1.19.4 r-rlang@1.1.6 r-resultmodelmanager@0.6.2 r-readr@2.1.6 r-parallellogger@3.5.1 r-featureextraction@3.13.0 r-dplyr@1.1.4 r-databaseconnector@7.1.0 r-checkmate@2.3.3 r-andromeda@1.2.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://ohdsi.github.io/Characterization/
Licenses: ASL 2.0
Build system: r
Synopsis: Implement Descriptive Studies Using the Common Data Model
Description:

An end-to-end framework that enables users to implement various descriptive studies for a given set of target and outcome cohorts for data mapped to the Observational Medical Outcomes Partnership Common Data Model.

r-cheem 0.4.2
Propagated dependencies: r-spinifex@0.3.10 r-shinythemes@1.2.0 r-shinycssloaders@1.1.0 r-shiny@1.11.1 r-plotly@4.11.0 r-magrittr@2.0.4 r-ggplot2@4.0.1 r-dt@0.34.0 r-conflicted@1.2.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/nspyrison/cheem/
Licenses: Expat
Build system: r
Synopsis: Interactively Explore Local Explanations with the Radial Tour
Description:

Given a non-linear model, calculate the local explanation. We purpose view the data space, explanation space, and model residuals as ensemble graphic interactive on a shiny application. After an observation of interest is identified, the normalized variable importance of the local explanation is used as a 1D projection basis. The support of the local explanation is then explored by changing the basis with the use of the radial tour <doi:10.32614/RJ-2020-027>; <doi:10.1080/10618600.1997.10474754>.

r-circnntsrmult 0.1.0
Propagated dependencies: r-psychtools@2.5.7.22 r-circnntsr@2.3
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=CircNNTSRmult
Licenses: GPL 2+
Build system: r
Synopsis: Multivariate Circular Data using MNNTS Models
Description:

This package provides a collection of utilities for the statistical analysis of multivariate circular data using distributions based on Multivariate Nonnegative Trigonometric Sums (MNNTS). The package includes functions for calculation of densities and distributions, for the estimation of parameters, and more.

r-candisc 1.1.0
Propagated dependencies: r-mass@7.3-65 r-insight@1.4.3 r-heplots@1.8.1 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-car@3.1-3
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/friendly/candisc/
Licenses: GPL 2+
Build system: r
Synopsis: Visualizing Generalized Canonical Discriminant and Canonical Correlation Analysis
Description:

This package provides functions for computing and visualizing generalized canonical discriminant analyses and canonical correlation analysis for a multivariate linear model. Traditional canonical discriminant analysis is restricted to a one-way MANOVA design and is equivalent to canonical correlation analysis between a set of quantitative response variables and a set of dummy variables coded from the factor variable. The candisc package generalizes this to higher-way MANOVA designs for all factors in a multivariate linear model, computing canonical scores and vectors for each term. The graphic functions provide low-rank (1D, 2D, 3D) visualizations of terms in an mlm via the plot.candisc and heplot.candisc methods. Related plots are now provided for canonical correlation analysis when all predictors are quantitative. Methods for linear discriminant analysis are now included.

r-comparemcmcs 0.6.0
Propagated dependencies: r-xtable@1.8-4 r-rlang@1.1.6 r-reshape2@1.4.5 r-r6@2.6.1 r-nimble@1.4.1 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-coda@0.19-4.1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/nimble-dev/compareMCMCs
Licenses: Modified BSD GPL 2+
Build system: r
Synopsis: Compare MCMC Efficiency from 'nimble' and/or Other MCMC Engines
Description:

Manages comparison of MCMC performance metrics from multiple MCMC algorithms. These may come from different MCMC configurations using the nimble package or from other packages. Plug-ins for JAGS via rjags and Stan via rstan are provided. It is possible to write plug-ins for other packages. Performance metrics are held in an MCMCresult class along with samples and timing data. It is easy to apply new performance metrics. Reports are generated as html pages with figures comparing sets of runs. It is possible to configure the html pages, including providing new figure components.

r-coremicrobiomer 0.1.0
Propagated dependencies: r-vegan@2.7-2 r-srs@0.2.3 r-reshape2@1.4.5 r-plotly@4.11.0 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-fastmatch@1.1-6 r-edger@4.8.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=CoreMicrobiomeR
Licenses: GPL 3
Build system: r
Synopsis: Identification of Core Microbiome
Description:

The Core Microbiome refers to the group of microorganisms that are consistently present in a particular environment, habitat, or host species. These microorganisms play a crucial role in the functioning and stability of that ecosystem. Identifying these microorganisms can contribute to the emerging field of personalized medicine. The CoreMicrobiomeR is designed to facilitate the identification, statistical testing, and visualization of this group of microorganisms.This package offers three key functions to analyze and visualize microbial community data. This package has been developed based on the research papers published by Pereira et al.(2018) <doi:10.1186/s12864-018-4637-6> and Beule L, Karlovsky P. (2020) <doi:10.7717/peerj.9593>.

r-cellvolumedist 1.5
Propagated dependencies: r-minpack-lm@1.2-4 r-gplots@3.2.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=cellVolumeDist
Licenses: GPL 2+
Build system: r
Synopsis: Functions to Fit Cell Volume Distributions and Thereby Estimate Cell Growth Rates and Division Times
Description:

This package implements a methodology for using cell volume distributions to estimate cell growth rates and division times that is described in the paper, "Cell Volume Distributions Reveal Cell Growth Rates and Division Times", by Michael Halter, John T. Elliott, Joseph B. Hubbard, Alessandro Tona and Anne L. Plant, which appeared in the Journal of Theoretical Biology. In order to reproduce the analysis used to obtain Table 1 in the paper, execute the command "example(fitVolDist)".

r-cinargenesets 0.1.1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/eonurk/cinaR-genesets
Licenses: GPL 3
Build system: r
Synopsis: Ready-to-Use Curated Gene Sets for 'cinaR'
Description:

Immune related gene sets provided along with the cinaR package.

r-chores 0.3.1
Propagated dependencies: r-streamy@0.2.1 r-shiny@1.11.1 r-rstudioapi@0.17.1 r-rlang@1.1.6 r-miniui@0.1.2 r-glue@1.8.0 r-ellmer@0.4.0 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/simonpcouch/chores
Licenses: Expat
Build system: r
Synopsis: Collection of Large Language Model Assistants
Description:

This package provides a collection of ergonomic large language model assistants designed to help you complete repetitive, hard-to-automate tasks quickly. After selecting some code, press the keyboard shortcut you've chosen to trigger the package app, select an assistant, and watch your chore be carried out. While the package ships with a number of chore helpers for R package development, users can create custom helpers just by writing some instructions in a markdown file.

r-casecohortcoxsurvival 0.0.36
Propagated dependencies: r-survival@3.8-3 r-nnet@7.3-20
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=CaseCohortCoxSurvival
Licenses: GPL 2
Build system: r
Synopsis: Case-Cohort Cox Survival Inference
Description:

Cox model inference for relative hazard and covariate-specific pure risk estimated from stratified and unstratified case-cohort data as described in Etievant, L., Gail, M.H. (Lifetime Data Analysis, 2024) <doi:10.1007/s10985-024-09621-2>.

r-ccoptimalmatch 0.1.0
Propagated dependencies: r-rlang@1.1.6 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=ccoptimalmatch
Licenses: GPL 2
Build system: r
Synopsis: Implementation of Case-Control Optimal Matching
Description:

Cases are matched to controls in an efficient, optimal and computationally flexible way. It uses the idea of sub-sampling in the level of the case, by creating pseudo-observations of controls. The user can select between replacement and without replacement, the number of controls, and several covariates to match upon. See Mamouris (2021) <doi:10.1186/s12874-021-01256-3> for an overview.

r-callme 0.1.11
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/coolbutuseless/callme
Licenses: Expat
Build system: r
Synopsis: Easily Compile and Call Inline 'C' Functions
Description:

Compile inline C code and easily call with automatically generated wrapper functions. By allowing user-defined headers and compilation flags (preprocessor, compiler and linking flags) the user can configure optimization options and linking to third party libraries. Multiple functions may be defined in a single block of code - which may be defined in a string or a path to a source file.

r-coffee 0.4.3
Propagated dependencies: r-rintcal@1.3.1 r-rice@1.6.2 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/Maarten14C/coffee
Licenses: GPL 2+
Build system: r
Synopsis: Chronological Ordering for Fossils and Environmental Events
Description:

While individual calibrated radiocarbon dates can span several centuries, combining multiple dates together with any chronological constraints can make a chronology much more robust and precise. This package uses Bayesian methods to enforce the chronological ordering of radiocarbon and other dates, for example for trees with multiple radiocarbon dates spaced at exactly known intervals (e.g., 10 annual rings). For methods see Christen 2003 <doi:10.11141/ia.13.2>. Another example is sites where the relative chronological position of the dates is taken into account - the ages of dates further down a site must be older than those of dates further up (Buck, Kenworthy, Litton and Smith 1991 <doi:10.1017/S0003598X00080534>; Nicholls and Jones 2001 <doi:10.1111/1467-9876.00250>). The paper accompanying this R package is Blaauw et al. 2024 <doi:10.1017/RDC.2024.56>.

r-cdmconnector 2.4.0
Propagated dependencies: r-withr@3.0.2 r-tidyselect@1.2.1 r-tidyr@1.3.1 r-stringr@1.6.0 r-stringi@1.8.7 r-rlang@1.1.6 r-readr@2.1.6 r-purrr@1.2.0 r-omopgenerics@1.3.7 r-lifecycle@1.0.4 r-jsonlite@2.0.0 r-glue@1.8.0 r-generics@0.1.4 r-dplyr@1.1.4 r-dbplyr@2.5.1 r-dbi@1.2.3 r-cli@3.6.5 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://darwin-eu.github.io/CDMConnector/
Licenses: FSDG-compatible
Build system: r
Synopsis: Connect to an OMOP Common Data Model
Description:

This package provides tools for working with observational health data in the Observational Medical Outcomes Partnership (OMOP) Common Data Model format with a pipe friendly syntax. Common data model database table references are stored in a single compound object along with metadata.

r-cardinalr 1.0.6
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-purrr@1.2.0 r-mvtnorm@1.3-3 r-mass@7.3-65 r-gtools@3.9.5 r-geozoo@0.5.1 r-dplyr@1.1.4 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://jayanilakshika.github.io/cardinalR/
Licenses: Expat
Build system: r
Synopsis: Collection of Data Structures
Description:

This package provides a collection of functions to generate a large variety of structures in high dimensions. These data structures are useful for testing, validating, and improving algorithms used in dimensionality reduction, clustering, machine learning, and visualization.

r-coveffectsplot 1.0.7
Propagated dependencies: r-shiny@1.11.1 r-ggplot2@4.0.1 r-egg@0.4.5 r-data-table@1.17.8 r-colourpicker@1.3.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://smouksassi.github.io/coveffectsplot/
Licenses: Expat
Build system: r
Synopsis: Produce Forest Plots to Visualize Covariate Effects
Description:

Produce forest plots to visualize covariate effects using either the command line or an interactive Shiny application.

r-cdiwg2ws 0.2.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=cdiWG2WS
Licenses: GPL 3+
Build system: r
Synopsis: Words and Gestures to Words and Sentences Score Conversion
Description:

Convert MacArthur-Bates Communicative Development Inventory Words and Gestures scores to would-be scores on Words and Sentences, based on modeling from the Stanford Wordbank <https://wordbank.stanford.edu/>. See Day et al. (2025) <doi:10.1111/desc.70036>.

r-cpd 0.3.3
Propagated dependencies: r-rdpack@2.6.4 r-hypergeo@1.2-14 r-dgof@1.5.1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=cpd
Licenses: GPL 2+
Build system: r
Synopsis: Complex Pearson Distributions
Description:

Probability mass function, distribution function, quantile function and random generation for the Complex Triparametric Pearson (CTP) and Complex Biparametric Pearson (CBP) distributions developed by Rodriguez-Avi et al (2003) <doi:10.1007/s00362-002-0134-7>, Rodriguez-Avi et al (2004) <doi:10.1007/BF02778271> and Olmo-Jimenez et al (2018) <doi:10.1080/00949655.2018.1482897>. The package also contains maximum-likelihood fitting functions for these models.

r-cdgd 1.0.1
Propagated dependencies: r-caret@7.0-1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/ang-yu/cdgd
Licenses: Expat
Build system: r
Synopsis: Causal Decomposition of Group Disparities
Description:

Estimates the causal decompositions of group disparities developed by Yu and Elwert (2025) <doi:10.1214/24-AOAS1990>. For the nuisance functions of the estimators, we provide both parametric and nonparametric options, as well as manual options in case the default models are not satisfying.

r-cleannlp 3.1.0
Dependencies: python@3.11.14
Propagated dependencies: r-udpipe@0.8.16 r-stringi@1.8.7 r-reticulate@1.44.1 r-matrix@1.7-4
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://statsmaths.github.io/cleanNLP/
Licenses: LGPL 2.0
Build system: r
Synopsis: Tidy Data Model for Natural Language Processing
Description:

This package provides a set of fast tools for converting a textual corpus into a set of normalized tables. Users may make use of the udpipe back end with no external dependencies, or a Python back ends with spaCy <https://spacy.io>. Exposed annotation tasks include tokenization, part of speech tagging, named entity recognition, and dependency parsing.

r-colossus 1.4.10
Propagated dependencies: r-tibble@3.3.0 r-testthat@3.3.0 r-stringr@1.6.0 r-rlang@1.1.6 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-processx@3.8.6 r-lubridate@1.9.4 r-dplyr@1.1.4 r-data-table@1.17.8 r-callr@3.7.6
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://ericgiunta.github.io/Colossus/
Licenses: GPL 3+
Build system: r
Synopsis: "Risk Model Regression and Analysis with Complex Non-Linear Models"
Description:

This package performs survival analysis using general non-linear models. Risk models can be the sum or product of terms. Each term is the product of exponential/linear functions of covariates. Additionally sub-terms can be defined as a sum of exponential, linear threshold, and step functions. Cox Proportional hazards <https://en.wikipedia.org/wiki/Proportional_hazards_model>, Poisson <https://en.wikipedia.org/wiki/Poisson_regression>, and Fine-Gray competing risks <https://www.publichealth.columbia.edu/research/population-health-methods/competing-risk-analysis> regression are supported. This work was sponsored by NASA Grants 80NSSC19M0161 and 80NSSC23M0129 through a subcontract from the National Council on Radiation Protection and Measurements (NCRP). The computing for this project was performed on the Beocat Research Cluster at Kansas State University, which is funded in part by NSF grants CNS-1006860, EPS-1006860, EPS-0919443, ACI-1440548, CHE-1726332, and NIH P20GM113109.

r-contoso 2.1.0
Propagated dependencies: r-purrr@1.2.0 r-duckdb@1.4.2 r-dplyr@1.1.4 r-dbi@1.2.3 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://codeberg.org/usrbinr/contoso
Licenses: Expat
Build system: r
Synopsis: Dataset of the 'Contoso' Company
Description:

This package provides a collection of synthetic datasets simulating sales transactions from a fictional company. The dataset includes various related tables that contain essential business and operational data, useful for analyzing sales performance and other business insights. Key tables included in the package are: - "sales": Contains data on individual sales transactions, including order details, pricing, quantities, and customer information. - "customer": Stores customer-specific details such as demographics, geographic location, occupation, and birthday. - "store": Provides information about stores, including location, size, status, and operational dates. - "orders": Contains details about customer orders, including order and delivery dates, store, and customer data. - "product": Contains data on products, including attributes such as product name, category, price, cost, and weight. - "calendar": A time-based table that includes date-related attributes like year, month, quarter, day, and working day indicators. This dataset is ideal for practicing data analysis, performing time-series analysis, creating reports, or simulating business intelligence scenarios.

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