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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-chatai4r 1.3.1
Propagated dependencies: r-rstudioapi@0.17.1 r-jsonlite@2.0.0 r-igraph@2.2.1 r-httr@1.4.7 r-glue@1.8.0 r-future@1.68.0 r-deeprstudio@0.0.9 r-curl@7.0.0 r-crayon@1.5.3 r-clipr@0.8.0 r-base64enc@0.1-3 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://kumes.github.io/chatAI4R/
Licenses: Artistic License 2.0
Build system: r
Synopsis: Chat-Based Interactive Artificial Intelligence for R
Description:

The Large Language Model (LLM) represents a groundbreaking advancement in data science and programming, and also allows us to extend the world of R. A seamless interface for integrating the OpenAI Web APIs into R is provided in this package. This package leverages LLM-based AI techniques, enabling efficient knowledge discovery and data analysis. The previous functions such as seamless translation and image generation have been moved to other packages deepRstudio and stableDiffusion4R'.

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-cmahalanobis 1.0.0
Propagated dependencies: r-reshape2@1.4.5 r-matrixstats@1.5.0 r-gridextra@2.3 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=cmahalanobis
Licenses: GPL 3
Build system: r
Synopsis: Calculate Distance Measures for DataFrames
Description:

It provides functions that calculate Mahalanobis distance, Euclidean distance, Manhattan distance, Chebyshev distance, Hamming distance, Canberra distance, Minkowski dissimilarity (distance defined for p >= 1), Cosine dissimilarity, Bhattacharyya dissimilarity, Jaccard distance, Hellinger distance, Bray-Curtis dissimilarity, Sorensen-Dice dissimilarity between each pair of species in a list of data frames. These statistics are fundamental in various fields, such as cluster analysis, classification, and other applications of machine learning and data mining, where assessing similarity or dissimilarity between data is crucial. The package is designed to be flexible and easily integrated into data analysis workflows, providing reliable tools for evaluating distances in multidimensional contexts.

r-chernoffdist 0.1.0
Propagated dependencies: r-gsl@2.1-9
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=ChernoffDist
Licenses: GPL 3
Build system: r
Synopsis: Chernoff's Distribution
Description:

Computes Chernoff's distribution based on the method in Piet Groeneboom & Jon A Wellner (2001) Computing Chernoff's Distribution, Journal of Computational and Graphical Statistics, 10:2, 388-400, <doi:10.1198/10618600152627997>. Chernoff's distribution is defined as the distribution of the maximizer of the two-sided Brownian motion minus quadratic drift. That is, Z = argmax (B(t)-t^2).

r-cases 0.2.0
Propagated dependencies: r-mvtnorm@1.3-3 r-multcomp@1.4-29 r-matrix@1.7-4 r-magrittr@2.0.4 r-ggplot2@4.0.1 r-extradistr@1.10.0 r-dplyr@1.1.4 r-corrplot@0.95 r-copula@1.1-7 r-boot@1.3-32 r-bindata@0.9-24
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/maxwestphal/cases
Licenses: Expat
Build system: r
Synopsis: Stratified Evaluation of Subgroup Classification Accuracy
Description:

Enables simultaneous statistical inference for the accuracy of multiple classifiers in multiple subgroups (strata). For instance, allows to perform multiple comparisons in diagnostic accuracy studies with co-primary endpoints sensitivity and specificity (Westphal M, Zapf A. Statistical inference for diagnostic test accuracy studies with multiple comparisons. Statistical Methods in Medical Research. 2024;0(0). <doi:10.1177/09622802241236933>).

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-clordr 1.7.0
Propagated dependencies: r-tmvmixnorm@1.1.1 r-rootsolve@1.8.2.4 r-pbivnorm@0.6.0 r-mass@7.3-65 r-foreach@1.5.2 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=clordr
Licenses: GPL 2
Build system: r
Synopsis: Composite Likelihood Inference and Diagnostics for Replicated Spatial Ordinal Data
Description:

Composite likelihood parameter estimate and asymptotic covariance matrix are calculated for the spatial ordinal data with replications, where spatial ordinal response with covariate and both spatial exponential covariance within subject and independent and identically distributed measurement error. Parameter estimation can be performed by either solving the gradient function or maximizing composite log-likelihood. Parametric bootstrapping is used to estimate the Godambe information matrix and hence the asymptotic standard error and covariance matrix with parallel processing option. Moreover, the proposed surrogate residual, which extends the results of Liu and Zhang (2017) <doi: 10.1080/01621459.2017.1292915>, can act as a useful tool for model diagnostics.

r-coxphw 4.0.3
Propagated dependencies: r-survival@3.8-3
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/biometrician/coxphw
Licenses: GPL 3
Build system: r
Synopsis: Weighted Estimation in Cox Regression
Description:

This package implements weighted estimation in Cox regression as proposed by Schemper, Wakounig and Heinze (Statistics in Medicine, 2009, <doi:10.1002/sim.3623>) and as described in Dunkler, Ploner, Schemper and Heinze (Journal of Statistical Software, 2018, <doi:10.18637/jss.v084.i02>). Weighted Cox regression provides unbiased average hazard ratio estimates also in case of non-proportional hazards. Approximated generalized concordance probability an effect size measure for clear-cut decisions can be obtained. The package provides options to estimate time-dependent effects conveniently by including interactions of covariates with arbitrary functions of time, with or without making use of the weighting option.

r-camea 0.1.1
Propagated dependencies: r-tibble@3.3.0 r-purrr@1.2.0 r-metafor@4.8-0 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=CaMeA
Licenses: AGPL 3+
Build system: r
Synopsis: Causal Meta-Analysis for Aggregated Data
Description:

This package provides a tool for causal meta-analysis. This package implements the aggregation formulas and inference methods proposed in Berenfeld et al. (2025) <doi:10.48550/arXiv.2505.20168>. Users can input aggregated data across multiple studies and compute causally meaningful aggregated effects of their choice (risk difference, risk ratio, odds ratio, etc) under user-specified population weighting. The built-in function camea() allows to obtain precise variance estimates for these effects and to compare the latter to a classical meta-analysis aggregate, the random effect model, as implemented in the metafor package <https://CRAN.R-project.org/package=metafor>.

r-card-pro 2.3.0
Propagated dependencies: r-shiny@1.11.1 r-quickcode@1.0.8 r-htmltools@0.5.8.1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cardpro.rpkg.net
Licenses: Expat
Build system: r
Synopsis: Lightweight Modern & Responsive Card Component for 'shiny'
Description:

Responsive and modern HTML card essentials for shiny applications and dashboards. This novel card component in Bootstrap provides a flexible and extensible content container with multiple variants and options for building robust R based apps e.g for graph build or machine learning projects. The features rely on a combination of JQuery <https://jquery.com> and CSS styles to improve the card functionality.

r-coni 0.1.0
Dependencies: python@3.11.14
Propagated dependencies: r-tidyselect@1.2.1 r-tidyr@1.3.1 r-tibble@3.3.0 r-rlang@1.1.6 r-ppcor@1.1 r-plyr@1.8.9 r-igraph@2.2.1 r-hmisc@5.2-4 r-gridextra@2.3 r-gplots@3.2.0 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-genefilter@1.92.0 r-foreach@1.5.2 r-forcats@1.0.1 r-dplyr@1.1.4 r-doparallel@1.0.17 r-data-table@1.17.8 r-cocor@1.1-4
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=CoNI
Licenses: GPL 3
Build system: r
Synopsis: Correlation Guided Network Integration (CoNI)
Description:

Integrates two numerical omics data sets from the same samples using partial correlations. The output can be represented as a network, bipartite graph or a hypergraph structure. The method used in the package refers to Klaus et al (2021) <doi:10.1016/j.molmet.2021.101295>.

r-cliff 0.1.2
Propagated dependencies: r-rlang@1.1.6 r-processx@3.8.6 r-ellipsis@0.3.2
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/RTagBot/cliff
Licenses: Expat
Build system: r
Synopsis: Execute Command Line Programs Interactively
Description:

Execute command line programs and format results for interactive use. It is based on the package processx so it does not use shell to start up the process like system() and system2(). It also provides a simpler and cleaner interface than processx::run().

r-corehunter 3.2.3
Dependencies: openjdk@25
Propagated dependencies: r-rjava@1.0-11 r-naturalsort@0.1.3
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=corehunter
Licenses: Expat
Build system: r
Synopsis: Multi-Purpose Core Subset Selection
Description:

Core Hunter is a tool to sample diverse, representative subsets from large germplasm collections, with minimum redundancy. Such so-called core collections have applications in plant breeding and genetic resource management in general. Core Hunter can construct cores based on genetic marker data, phenotypic traits or precomputed distance matrices, optimizing one of many provided evaluation measures depending on the precise purpose of the core (e.g. high diversity, representativeness, or allelic richness). In addition, multiple measures can be simultaneously optimized as part of a weighted index to bring the different perspectives closer together. The Core Hunter library is implemented in Java 8 as an open source project (see <http://www.corehunter.org>).

r-correctr 0.3.1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://hendersontrent.github.io/correctR/
Licenses: Expat
Build system: r
Synopsis: Corrected Test Statistics for Comparing Machine Learning Models on Correlated Samples
Description:

Calculate a set of corrected test statistics for cases when samples are not independent, such as when classification accuracy values are obtained over resamples or through k-fold cross-validation, as proposed by Nadeau and Bengio (2003) <doi:10.1023/A:1024068626366> and presented in Bouckaert and Frank (2004) <doi:10.1007/978-3-540-24775-3_3>.

r-cgal4h 0.1.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://gitlab.com/dickoa/cgal4h
Licenses: GPL 3 FSDG-compatible
Build system: r
Synopsis: 'CGAL' Version 4 C++ Header Files
Description:

CGAL is a C++ library that aims to provide easy access to efficient and reliable algorithms in computational geometry. Since its version 4, CGAL can be used as standalone header-only library and is available under a double GPL-3|LGPL license. <https://www.cgal.org/>.

r-clustimpute 0.2.4
Propagated dependencies: r-tidyr@1.3.1 r-rlang@1.1.6 r-magrittr@2.0.4 r-knitr@1.50 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-copula@1.1-7 r-clusterr@1.3.5
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=ClustImpute
Licenses: GPL 3
Build system: r
Synopsis: K-Means Clustering with Build-in Missing Data Imputation
Description:

This k-means algorithm is able to cluster data with missing values and as a by-product completes the data set. The implementation can deal with missing values in multiple variables and is computationally efficient since it iteratively uses the current cluster assignment to define a plausible distribution for missing value imputation. Weights are used to shrink early random draws for missing values (i.e., draws based on the cluster assignments after few iterations) towards the global mean of each feature. This shrinkage slowly fades out after a fixed number of iterations to reflect the increasing credibility of cluster assignments. See the vignette for details.

r-concordance 2.0.0
Propagated dependencies: 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-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=concordance
Licenses: GPL 2
Build system: r
Synopsis: Product Concordance
Description:

This package provides a set of utilities for matching products in different classification codes used in international trade research. It supports concordance between the Harmonized System (HS0, HS1, HS2, HS3, HS4, HS5, HS combined), the Standard International Trade Classification (SITC1, SITC2, SITC3, SITC4), the North American Industry Classification System (NAICS combined), as well as the Broad Economic Categories (BEC), the International Standard of Industrial Classification (ISIC), and the Standard Industrial Classification (SIC). It also provides code nomenclature/descriptions look-up, Rauch classification look-up (via concordance to SITC2), and trade elasticity look-up (via concordance to HS0 or SITC3 codes).

r-causalqual 1.0.0
Propagated dependencies: r-stringr@1.6.0 r-sandwich@3.1-1 r-rdrobust@3.0.0 r-ocf@1.0.3 r-magrittr@2.0.4 r-lmtest@0.9-40 r-grf@2.6.1 r-ggsci@4.1.0 r-ggplot2@4.0.1 r-cli@3.6.5 r-caret@7.0-1 r-aer@1.2-15
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://riccardo-df.github.io/causalQual/
Licenses: Expat
Build system: r
Synopsis: Causal Inference for Qualitative Outcomes
Description:

This package implements the framework introduced in Di Francesco and Mellace (2025) <doi:10.48550/arXiv.2502.11691>, shifting the focus to well-defined and interpretable estimands that quantify how treatment affects the probability distribution over outcome categories. It supports selection-on-observables, instrumental variables, regression discontinuity, and difference-in-differences designs.

r-certara-darwinreporter 2.0.1
Propagated dependencies: r-xpose@0.4.23 r-tidyr@1.3.1 r-sortable@0.6.0 r-shinywidgets@0.9.0 r-shinytree@0.3.1 r-shinymeta@0.2.1 r-shinyjs@2.1.0 r-shinyjqui@0.4.1 r-shinyace@0.4.4 r-shiny@1.11.1 r-scales@1.4.0 r-plotly@4.11.0 r-jsonlite@2.0.0 r-ggplot2@4.0.1 r-flextable@0.9.10 r-dt@0.34.0 r-dplyr@1.1.4 r-colourpicker@1.3.0 r-certara-xpose-nlme@2.0.2 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-DarwinReporter/
Licenses: LGPL 3
Build system: r
Synopsis: Data Visualization Utilities for 'pyDarwin' Machine Learning Pharmacometric Model Development
Description:

Utilize the shiny interface for visualizing results from a pyDarwin (<https://certara.github.io/pyDarwin/>) machine learning pharmacometric model search. It generates Goodness-of-Fit plots and summary tables for selected models, allowing users to customize diagnostic outputs within the interface. The underlying R code for generating plots and tables can be extracted for use outside the interactive session. Model diagnostics can also be incorporated into an R Markdown document and rendered in various output formats.

r-consrq 1.0
Propagated dependencies: r-rfast@2.1.5.2 r-quantreg@6.1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=consrq
Licenses: GPL 2+
Build system: r
Synopsis: Constrained Quantile Regression
Description:

Constrained quantile regression is performed. One constraint is that all beta coefficients (including the constant) cannot be negative, they can be either 0 or strictly positive. Another constraint is that the beta coefficients lie within an interval. References: Koenker R. (2005) Quantile Regression, Cambridge University Press. <doi:10.1017/CBO9780511754098>.

r-cumulcalib 0.0.1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/resplab/cumulcalib
Licenses: Expat
Build system: r
Synopsis: Cumulative Calibration Assessment for Prediction Models
Description:

This package provides tools for visualization of, and inference on, the calibration of prediction models on the cumulative domain. This provides a method for evaluating calibration of risk prediction models without having to group the data or use tuning parameters (e.g., loess bandwidth). This package implements the methodology described in Sadatsafavi and Patkau (2024) <doi:10.1002/sim.10138>. The core of the package is cumulcalib(), which takes in vectors of binary responses and predicted risks. The plot() and summary() methods are implemented for the results returned by cumulcalib().

r-cocron 1.0-1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: http://comparingcronbachalphas.org
Licenses: GPL 3+
Build system: r
Synopsis: Statistical Comparisons of Two or more Alpha Coefficients
Description:

Statistical tests for the comparison between two or more alpha coefficients based on either dependent or independent groups of individuals. A web interface is available at http://comparingcronbachalphas.org. A plugin for the R GUI and IDE RKWard is included. Please install RKWard from https:// rkward.kde.org to use this feature. The respective R package rkward cannot be installed directly from a repository, as it is a part of RKWard.

r-cmprskqr 0.9.3
Propagated dependencies: r-survival@3.8-3 r-quantreg@6.1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://bitbucket.org/sdlugosz/cmprskqr
Licenses: GPL 2+
Build system: r
Synopsis: Analysis of Competing Risks Using Quantile Regressions
Description:

Estimation, testing and regression modeling of subdistribution functions in competing risks using quantile regressions, as described in Peng and Fine (2009) <DOI:10.1198/jasa.2009.tm08228>.

r-chemospec 6.3.1
Propagated dependencies: r-reshape2@1.4.5 r-readjdx@0.6.4 r-plotly@4.11.0 r-patchwork@1.3.2 r-magrittr@2.0.4 r-lattice@0.22-7 r-ggplot2@4.0.1 r-chemospecutils@1.0.5
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://bryanhanson.github.io/ChemoSpec/
Licenses: GPL 3
Build system: r
Synopsis: Exploratory Chemometrics for Spectroscopy
Description:

This package provides a collection of functions for top-down exploratory data analysis of spectral data including nuclear magnetic resonance (NMR), infrared (IR), Raman, X-ray fluorescence (XRF) and other similar types of spectroscopy. Includes functions for plotting and inspecting spectra, peak alignment, hierarchical cluster analysis (HCA), principal components analysis (PCA) and model-based clustering. Robust methods appropriate for this type of high-dimensional data are available. ChemoSpec is designed for structured experiments, such as metabolomics investigations, where the samples fall into treatment and control groups. Graphical output is formatted consistently for publication quality plots. ChemoSpec is intended to be very user friendly and to help you get usable results quickly. A vignette covering typical operations is available.

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