_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/

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-crso 0.1.1
Propagated dependencies: r-foreach@1.5.2
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=crso
Licenses: GPL 2
Build system: r
Synopsis: Cancer Rule Set Optimization ('crso')
Description:

An algorithm for identifying candidate driver combinations in cancer. CRSO is based on a theoretical model of cancer in which a cancer rule is defined to be a collection of two or more events (i.e., alterations) that are minimally sufficient to cause cancer. A cancer rule set is a set of cancer rules that collectively are assumed to account for all of ways to cause cancer in the population. In CRSO every event is designated explicitly as a passenger or driver within each patient. Each event is associated with a patient-specific, event-specific passenger penalty, reflecting how unlikely the event would have happened by chance, i.e., as a passenger. CRSO evaluates each rule set by assigning all samples to a rule in the rule set, or to the null rule, and then calculating the total statistical penalty from all unassigned event. CRSO uses a three phase procedure find the best rule set of fixed size K for a range of Ks. A core rule set is then identified from among the best rule sets of size K as the rule set that best balances rule set size and statistical penalty. Users should consult the crso vignette for an example walk through of a full CRSO run. The full description, of the CRSO algorithm is presented in: Klein MI, Cannataro V, Townsend J, Stern DF and Zhao H. "Identifying combinations of cancer driver in individual patients." BioRxiv 674234 [Preprint]. June 19, 2019. <doi:10.1101/674234>. Please cite this article if you use crso'.

r-clic 0.1
Propagated dependencies: r-laplacesdemon@16.1.6 r-fbasics@4041.97
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=CLIC
Licenses: Expat
Build system: r
Synopsis: The LIC for Distributed Cosine Regression Analysis
Description:

This comprehensive framework for periodic time series modeling is designated as "CLIC" (The LIC for Distributed Cosine Regression Analysis) analysis. It is predicated on the assumption that the underlying data exhibits complex periodic structures beyond simple harmonic components. The philosophy of the method is articulated in Guo G. (2020) <doi:10.1080/02664763.2022.2053949>.

r-colocproptest 0.9.3
Propagated dependencies: r-magrittr@2.0.4 r-data-table@1.17.8 r-coloc@5.2.3 r-car@3.1-3
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=colocPropTest
Licenses: GPL 3+
Build system: r
Synopsis: Proportional Testing for Colocalisation Analysis
Description:

Colocalisation analysis tests whether two traits share a causal genetic variant in a specified genomic region. Proportional testing for colocalisation has been previously proposed [Wallace (2013) <doi:10.1002/gepi.21765>], but is reimplemented here to overcome barriers to its adoption. Its use is complementary to the fine- mapping based colocalisation method in the coloc package, and may be used in particular to identify false "H3" conclusions in coloc'.

r-cdghmm 0.1.2
Propagated dependencies: r-mvtnorm@1.3-3 r-mass@7.3-65 r-cluster@2.1.8.1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=CDGHMM
Licenses: GPL 2+
Build system: r
Synopsis: Hidden Markov Models for Multivariate Panel Data
Description:

Estimates hidden Markov models from the family of Cholesky-decomposed Gaussian hidden Markov models (CDGHMM) under various missingness schemes. This family improves upon estimation of traditional Gaussian HMMs by introducing parsimony, as well as, controlling for dropped out observations and non-random missingness. See Neal, Sochaniwsky and McNicholas (2024) <DOI:10.1007/s11222-024-10462-0>.

r-conditionz 0.1.0
Propagated dependencies: r-uuid@1.2-1 r-r6@2.6.1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/ropenscilabs/conditionz
Licenses: Expat
Build system: r
Synopsis: Control How Many Times Conditions are Thrown
Description:

This package provides ability to control how many times in function calls conditions are thrown (shown to the user). Includes control of warnings and messages.

r-charlesschwabapi 1.0.5
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-purrr@1.2.0 r-openssl@2.3.4 r-lubridate@1.9.4 r-httr@1.4.7 r-dplyr@1.1.4 r-anytime@0.3.12
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=charlesschwabapi
Licenses: Expat
Build system: r
Synopsis: Wrapper Functions Around 'Charles Schwab Individual Trader API'
Description:

For those wishing to interact with the Charles Schwab Individual Trader API (<https://developer.schwab.com/products/trader-api--individual>) with R in a simplified manner, this package offers wrapper functions around authentication and the available API calls to streamline the process.

r-coopgame 0.2.2
Propagated dependencies: r-rcdd@1.6 r-gtools@3.9.5 r-geometry@0.5.2
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=CoopGame
Licenses: GPL 2
Build system: r
Synopsis: Important Concepts of Cooperative Game Theory
Description:

The theory of cooperative games with transferable utility offers useful insights into the way parties can share gains from cooperation and secure sustainable agreements, see e.g. one of the books by Chakravarty, Mitra and Sarkar (2015, ISBN:978-1107058798) or by Driessen (1988, ISBN:978-9027727299) for more details. A comprehensive set of tools for cooperative game theory with transferable utility is provided. Users can create special families of cooperative games, like e.g. bankruptcy games, cost sharing games and weighted voting games. There are functions to check various game properties and to compute five different set-valued solution concepts for cooperative games. A large number of point-valued solution concepts is available reflecting the diverse application areas of cooperative game theory. Some of these point-valued solution concepts can be used to analyze weighted voting games and measure the influence of individual voters within a voting body. There are routines for visualizing both set-valued and point-valued solutions in the case of three or four players.

r-cgam 1.31
Propagated dependencies: r-zeallot@0.2.0 r-svdialogs@1.1.1 r-statmod@1.5.1 r-splines2@0.5.4 r-rlang@1.1.6 r-quadprog@1.5-8 r-matrix@1.7-4 r-mass@7.3-65 r-lme4@1.1-37 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-coneproj@1.23
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=cgam
Licenses: GPL 2+
Build system: r
Synopsis: Constrained Generalized Additive Model
Description:

This package provides a constrained generalized additive model is fitted by the cgam routine. Given a set of predictors, each of which may have a shape or order restrictions, the maximum likelihood estimator for the constrained generalized additive model is found using an iteratively re-weighted cone projection algorithm. The ShapeSelect routine chooses a subset of predictor variables and describes the component relationships with the response. For each predictor, the user needs only specify a set of possible shape or order restrictions. A model selection method chooses the shapes and orderings of the relationships as well as the variables. The cone information criterion (CIC) is used to select the best combination of variables and shapes. A genetic algorithm may be used when the set of possible models is large. In addition, the cgam routine implements a two-dimensional isotonic regression using warped-plane splines without additivity assumptions. It can also fit a convex or concave regression surface with triangle splines without additivity assumptions. See Liao X, Meyer MC (2019)<doi:10.18637/jss.v089.i05> for more details.

r-coranking 0.2.5
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://coranking.guido-kraemer.com/
Licenses: GPL 3 FSDG-compatible
Build system: r
Synopsis: Co-Ranking Matrix
Description:

Calculates the co-ranking matrix to assess the quality of a dimensionality reduction.

r-conleyreg 0.1.9
Propagated dependencies: r-sf@1.0-23 r-s2@1.1.9 r-rdpack@2.6.4 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-matrix@1.7-4 r-lwgeom@0.2-14 r-lmtest@0.9-40 r-foreach@1.5.2 r-fixest@0.13.2 r-doparallel@1.0.17 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/cdueben/conleyreg
Licenses: Expat
Build system: r
Synopsis: Estimations using Conley Standard Errors
Description:

This package provides functions calculating Conley (1999) <doi:10.1016/S0304-4076(98)00084-0> standard errors. The package started by merging and extending multiple packages and other published scripts on this econometric technique. It strongly emphasizes computational optimization. Details are available in the function documentation and in the vignette.

r-civ 0.1.0
Propagated dependencies: r-kcmeans@0.1.0 r-aer@1.2-15
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/thomaswiemann/civ
Licenses: GPL 3+
Build system: r
Synopsis: Categorical Instrumental Variables
Description:

Implementation of the categorical instrumental variable (CIV) estimator proposed by Wiemann (2023) <arXiv:2311.17021>. CIV allows for optimal instrumental variable estimation in settings with relatively few observations per category. To obtain valid inference in these challenging settings, CIV leverages a regularization assumption that implies existence of a latent categorical variable with fixed finite support achieving the same first stage fit as the observed instrument.

r-ctmva 1.6.0
Propagated dependencies: r-viridislite@0.4.2 r-vegan@2.7-2 r-rlang@1.1.6 r-polynom@1.4-1 r-mgcv@1.9-4 r-matrix@1.7-4 r-mass@7.3-65 r-ggplot2@4.0.1 r-fda@6.3.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=ctmva
Licenses: GPL 2+
Build system: r
Synopsis: Continuous-Time Multivariate Analysis
Description:

This package implements a basis function or functional data analysis framework for several techniques of multivariate analysis in continuous-time setting. Specifically, we introduced continuous-time analogues of several classical techniques of multivariate analysis, such as principal component analysis, canonical correlation analysis, Fisher linear discriminant analysis, K-means clustering, and so on. Details are in Biplab Paul, Philip T. Reiss, Erjia Cui and Noemi Foa (2025) "Continuous-time multivariate analysis" <doi: 10.1080/10618600.2024.2374570>.

r-cumulocityr 0.1.0
Propagated dependencies: r-jsonlite@2.0.0 r-httr@1.4.7
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://softwareag.github.io/cumulocityr/
Licenses: GPL 3
Build system: r
Synopsis: Client for the 'Cumulocity' API
Description:

Access the Cumulocity API and retrieve data on devices, measurements, and events. Documentation for the API can be found at <https://www.cumulocity.com/guides/reference/rest-implementation/>.

r-certara-rsnlme-modelexecutor 3.0.2
Propagated dependencies: r-stringr@1.6.0 r-shinywidgets@0.9.0 r-shinymeta@0.2.1 r-shinyjs@2.1.0 r-shinyfiles@0.9.3 r-shinyace@0.4.4 r-shiny@1.11.1 r-reshape@0.8.10 r-promises@1.5.0 r-magrittr@2.0.4 r-jsonlite@2.0.0 r-htmltools@0.5.8.1 r-ggplot2@4.0.1 r-future@1.68.0 r-fs@1.6.6 r-dt@0.34.0 r-dplyr@1.1.4 r-certara-rsnlme@3.1.0.1 r-certara-nlme8@3.0.2 r-bslib@0.9.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://certara.github.io/R-RsNLME-model-executor/
Licenses: LGPL 3
Build system: r
Synopsis: Execute Pharmacometric Models Using 'shiny'
Description:

Execute Nonlinear Mixed Effects (NLME) models for pharmacometrics using a shiny interface. Specify engine parameters and select from different run options, including simple estimation, stepwise covariate search, bootstrapping, simulation, visual predictive check, and more. Models are executed using the Certara.RsNLME package.

r-crmetrics 0.3.2
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-sparsematrixstats@1.22.0 r-sccore@1.0.6 r-scales@1.4.0 r-r6@2.6.1 r-matrix@1.7-4 r-magrittr@2.0.4 r-ggrepel@0.9.6 r-ggpubr@0.6.2 r-ggpmisc@0.6.2 r-ggplot2@4.0.1 r-ggbeeswarm@0.7.2 r-dplyr@1.1.4 r-cowplot@1.2.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/khodosevichlab/CRMetrics
Licenses: GPL 3
Build system: r
Synopsis: Cell Ranger Output Filtering and Metrics Visualization
Description:

Sample and cell filtering as well as visualisation of output metrics from Cell Ranger by Grace X.Y. Zheng et al. (2017) <doi:10.1038/ncomms14049>. CRMetrics allows for easy plotting of output metrics across multiple samples as well as comparative plots including statistical assessments of these. CRMetrics allows for easy removal of ambient RNA using SoupX by Matthew D Young and Sam Behjati (2020) <doi:10.1093/gigascience/giaa151> or CellBender by Stephen J Fleming et al. (2022) <doi:10.1101/791699>. Furthermore, it is possible to preprocess data using Pagoda2 by Nikolas Barkas et al. (2021) <https://github.com/kharchenkolab/pagoda2> or Seurat by Yuhan Hao et al. (2021) <doi:10.1016/j.cell.2021.04.048> followed by embedding of cells using Conos by Nikolas Barkas et al. (2019) <doi:10.1038/s41592-019-0466-z>. Finally, doublets can be detected using scrublet by Samuel L. Wolock et al. (2019) <doi:10.1016/j.cels.2018.11.005> or DoubletDetection by Gayoso et al. (2020) <doi:10.5281/zenodo.2678041>. In the end, cells are filtered based on user input for use in downstream applications.

r-curricularcomplexitydata 0.1.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=CurricularComplexityData
Licenses: Expat
Build system: r
Synopsis: Data for Exploring Curricular Complexity
Description:

This package provides igraph objects representing engineering plans of study across multiple disciplines and institutions. The data are intended for use with the CurricularComplexity package (Reeping, 2026) <https://CRAN.R-project.org/package=CurricularComplexity> to support analyses of curricular structure. The package leverages network analysis approaches implemented in igraph (Csárdi et al., 2025) <doi:10.5281/zenodo.7682609>.

r-clrtools 0.1.0
Propagated dependencies: r-tidyr@1.3.1 r-survival@3.8-3 r-rstan@2.32.7 r-rlang@1.1.6 r-rcolorbrewer@1.1-3 r-patchwork@1.3.2 r-loo@2.8.0 r-lmtest@0.9-40 r-ggpubr@0.6.2 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-caret@7.0-1 r-bayesplot@1.14.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=CLRtools
Licenses: GPL 3
Build system: r
Synopsis: Diagnostic Tools for Logistic and Conditional Logistic Regression
Description:

This package provides tools for fitting, assessing, and comparing logistic and conditional logistic regression models. Includes residual diagnostics and goodness of fit measures for model development and evaluation in matched case control studies.

r-cricketdata 0.3.0
Propagated dependencies: r-xml2@1.5.0 r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-rvest@1.0.5 r-readr@2.1.6 r-lubridate@1.9.4 r-jsonlite@2.0.0 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://pkg.robjhyndman.com/cricketdata/
Licenses: GPL 3
Build system: r
Synopsis: International Cricket Data
Description:

Data on international and other major cricket matches from ESPNCricinfo <https://www.espncricinfo.com> and Cricsheet <https://cricsheet.org>. This package provides some functions to download the data into tibbles ready for analysis.

r-cagr 1.1.1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=CAGR
Licenses: GPL 3
Build system: r
Synopsis: Compound Annual Growth Rate
Description:

This package provides a time series usually does not have a uniform growth rate. Compound Annual Growth Rate measures the average annual growth over a given period. More details can be found in Bardhan et al. (2022) <DOI:10.18805/ag.D-5418>.

r-cstools 5.3.0
Propagated dependencies: r-verification@1.45 r-startr@3.0.0 r-scales@1.4.0 r-s2dv@2.2.1 r-reshape2@1.4.5 r-rcolorbrewer@1.1-3 r-rainfarmr@0.1 r-qmap@1.0-6 r-plyr@1.8.9 r-ncdf4@1.24 r-multiapply@2.1.5 r-maps@3.4.3 r-lubridate@1.9.4 r-ggplot2@4.0.1 r-easyverification@0.4.5 r-easyncdf@0.1.4 r-dplyr@1.1.4 r-data-table@1.17.8 r-climprojdiags@0.3.5 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=CSTools
Licenses: GPL 3
Build system: r
Synopsis: Assessing Skill of Climate Forecasts on Seasonal-to-Decadal Timescales
Description:

Exploits dynamical seasonal forecasts in order to provide information relevant to stakeholders at the seasonal timescale. The package contains process-based methods for forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination and multivariate verification, as well as basic and advanced tools to obtain tailored products. This package was developed in the context of the ERA4CS project MEDSCOPE and the H2020 S2S4E project and includes contributions from ArticXchange project founded by EU-PolarNet 2. Implements methods described in Pérez-Zanón et al. (2022) <doi:10.5194/gmd-15-6115-2022>, Doblas-Reyes et al. (2005) <doi:10.1111/j.1600-0870.2005.00104.x>, Mishra et al. (2018) <doi:10.1007/s00382-018-4404-z>, Sanchez-Garcia et al. (2019) <doi:10.5194/asr-16-165-2019>, Straus et al. (2007) <doi:10.1175/JCLI4070.1>, Terzago et al. (2018) <doi:10.5194/nhess-18-2825-2018>, Torralba et al. (2017) <doi:10.1175/JAMC-D-16-0204.1>, D'Onofrio et al. (2014) <doi:10.1175/JHM-D-13-096.1>, Verfaillie et al. (2017) <doi:10.5194/gmd-10-4257-2017>, Van Schaeybroeck et al. (2019) <doi:10.1016/B978-0-12-812372-0.00010-8>, Yiou et al. (2013) <doi:10.1007/s00382-012-1626-3>.

r-causalweight 1.1.4
Propagated dependencies: r-xgboost@1.7.11.1 r-superlearner@2.0-29 r-sandwich@3.1-1 r-ranger@0.17.0 r-np@0.60-18 r-mvtnorm@1.3-3 r-larf@1.4 r-hdm@0.3.2 r-grf@2.5.0 r-glmnet@4.1-10 r-fastdummies@1.7.5 r-e1071@1.7-16 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=causalweight
Licenses: Expat
Build system: r
Synopsis: Estimation Methods for Causal Inference Based on Inverse Probability Weighting and Doubly Robust Estimation
Description:

Various estimators of causal effects based on inverse probability weighting, doubly robust estimation, and double machine learning. Specifically, the package includes methods for estimating average treatment effects, direct and indirect effects in causal mediation analysis, and dynamic treatment effects. The models refer to studies of Froelich (2007) <doi:10.1016/j.jeconom.2006.06.004>, Huber (2012) <doi:10.3102/1076998611411917>, Huber (2014) <doi:10.1080/07474938.2013.806197>, Huber (2014) <doi:10.1002/jae.2341>, Froelich and Huber (2017) <doi:10.1111/rssb.12232>, Hsu, Huber, Lee, and Lettry (2020) <doi:10.1002/jae.2765>, and others.

r-cttshiny 0.1
Propagated dependencies: r-shinyace@0.4.4 r-shiny@1.11.1 r-psych@2.5.6 r-ltm@1.2-0 r-ctt@2.3.4
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=CTTShiny
Licenses: GPL 3
Build system: r
Synopsis: Classical Test Theory via Shiny
Description:

Interactive shiny application for running classical test theory (item analysis).

r-catmaply 0.9.5
Propagated dependencies: r-tidyr@1.3.1 r-rlang@1.1.6 r-plotly@4.11.0 r-magrittr@2.0.4 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/VerkehrsbetriebeZuerich/catmaply
Licenses: Expat
Build system: r
Synopsis: Heatmap for Categorical Data using 'plotly'
Description:

This package provides methods and plotting functions for displaying categorical data on an interactive heatmap using plotly'. Provides functionality for strictly categorical heatmaps, heatmaps illustrating categorized continuous data and annotated heatmaps. Also, there are various options to interact with the x-axis to prevent overlapping axis labels, e.g. via simple sliders or range sliders. Besides the viewer pane, resulting plots can be saved as a standalone HTML file, embedded in R Markdown documents or in a Shiny app.

r-clustorus 0.2.2
Propagated dependencies: r-rlang@1.1.6 r-purrr@1.2.0 r-igraph@2.2.1 r-ggplot2@4.0.1 r-cowplot@1.2.0 r-bambi@2.3.6
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/sungkyujung/ClusTorus
Licenses: GPL 3
Build system: r
Synopsis: Prediction and Clustering on the Torus by Conformal Prediction
Description:

This package provides various tools of for clustering multivariate angular data on the torus. The package provides angular adaptations of usual clustering methods such as the k-means clustering, pairwise angular distances, which can be used as an input for distance-based clustering algorithms, and implements clustering based on the conformal prediction framework. Options for the conformal scores include scores based on a kernel density estimate, multivariate von Mises mixtures, and naive k-means clusters. Moreover, the package provides some basic data handling tools for angular data.

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