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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-sapfluxnetr 0.1.5
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-magrittr@2.0.4 r-lubridate@1.9.4 r-glue@1.8.0 r-ggplot2@4.0.1 r-furrr@0.3.1 r-dplyr@1.1.4 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/sapfluxnet/sapfluxnetr
Licenses: Expat
Build system: r
Synopsis: Working with 'Sapfluxnet' Project Data
Description:

Access, modify, aggregate and plot data from the Sapfluxnet project, the first global database of sap flow measurements.

r-smartsizer 1.0.3
Propagated dependencies: r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=smartsizer
Licenses: GPL 3
Build system: r
Synopsis: Power Analysis for a SMART Design
Description:

This package provides a set of tools for determining the necessary sample size in order to identify the optimal dynamic treatment regime in a sequential, multiple assignment, randomized trial (SMART). Utilizes multiple comparisons with the best methodology to adjust for multiple comparisons. Designed for an arbitrary SMART design. Please see Artman (2018) <doi:10.1093/biostatistics/kxy064> for more details.

r-stmgp 1.0.4.2
Propagated dependencies: r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=stmgp
Licenses: GPL 2+
Build system: r
Synopsis: Rapid and Accurate Genetic Prediction Modeling for Genome-Wide Association or Whole-Genome Sequencing Study Data
Description:

Rapidly build accurate genetic prediction models for genome-wide association or whole-genome sequencing study data by smooth-threshold multivariate genetic prediction (STMGP) method. Variable selection is performed using marginal association test p-values with an optimal p-value cutoff selected by Cp-type criterion. Quantitative and binary traits are modeled respectively via linear and logistic regression models. A function that works through PLINK software (Purcell et al. 2007 <DOI:10.1086/519795>, Chang et al. 2015 <DOI:10.1186/s13742-015-0047-8>) <https://www.cog-genomics.org/plink2> is provided. Covariates can be included in regression model.

r-silicate 0.7.1
Propagated dependencies: r-unjoin@0.1.0 r-tibble@3.3.0 r-rlang@1.1.6 r-purrr@1.2.0 r-magrittr@2.0.4 r-gridbase@0.4-7 r-gibble@0.4.0 r-dplyr@1.1.4 r-decido@0.4.0 r-crsmeta@0.3.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/hypertidy/silicate
Licenses: GPL 3
Build system: r
Synopsis: Common Forms for Complex Hierarchical and Relational Data Structures
Description:

Generate common data forms for complex data suitable for conversions and transmission by decomposition as paths or primitives. Paths are sequentially-linked records, primitives are basic atomic elements and both can model many forms and be grouped into hierarchical structures. The universal models SC0 (structural) and SC (labelled, relational) are composed of edges and can represent any hierarchical form. Specialist models PATH', ARC and TRI provide the most common intermediate forms used for converting from one form to another. The methods are inspired by the simplicial complex <https://en.wikipedia.org/wiki/Simplicial_complex> and provide intermediate forms that relate spatial data structures to this mathematical construct.

r-spiga 1.0.0
Propagated dependencies: r-ga@3.2.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SPIGA
Licenses: GPL 2
Build system: r
Synopsis: Compute SPI Index using the Methods Genetic Algorithm and Maximum Likelihood
Description:

Calculate the Standardized Precipitation Index (SPI) for monitoring drought, using Artificial Intelligence techniques (SPIGA) and traditional numerical technique Maximum Likelihood (SPIML). For more information see: http://drought.unl.edu/monitoringtools/downloadablespiprogram.aspx.

r-saros-base 1.2.0
Propagated dependencies: r-zip@2.3.3 r-yaml@2.3.10 r-vctrs@0.6.5 r-tidyselect@1.2.1 r-tidyr@1.3.1 r-stringi@1.8.7 r-rstudioapi@0.17.1 r-rlang@1.1.6 r-glue@1.8.0 r-fs@1.6.6 r-forcats@1.0.1 r-dplyr@1.1.4 r-cli@3.6.5 r-bcrypt@1.2.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://nifu-no.github.io/saros.base/
Licenses: Expat
Build system: r
Synopsis: Base Tools for Semi-Automatic Reporting of Ordinary Surveys
Description:

Scaffold an entire web-based report using template chunks, based on a small chapter overview and a dataset. Highly adaptable with prefixes, suffixes, translations, etc. Also contains tools for password-protecting, e.g. for each organization's report on a website. Developed for the common case of a survey across multiple organizations/sites where each organization wants to obtain results for their organization compared with everyone else. See saros (<https://CRAN.R-project.org/package=saros>) for tools used for authors in the drafted reports.

r-samplesizesinglearmsurvival 0.1.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SampleSizeSingleArmSurvival
Licenses: Expat
Build system: r
Synopsis: Calculate Sample Size for Single-Arm Survival Studies
Description:

This package provides methods to calculate sample size for single-arm survival studies using the arcsine transformation, incorporating uniform accrual and exponential survival assumptions. Includes functionality for detailed numerical integration and simulation. This method is based on Nagashima et al. (2021) <doi:10.1002/pst.2090>.

r-sportminer 0.1.0
Propagated dependencies: r-widyr@0.1.5 r-topicmodels@0.2-17 r-tidytext@0.4.3 r-tidyr@1.3.1 r-textminer@3.0.6 r-stm@1.3.8 r-snowballc@0.7.1 r-slam@0.1-55 r-scales@1.4.0 r-rscopus@0.9.0 r-rlang@1.1.6 r-matrix@1.7-4 r-magrittr@2.0.4 r-igraph@2.2.1 r-ggraph@2.2.2 r-ggplot2@4.0.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/praveenmaths89/SportMiner
Licenses: Expat
Build system: r
Synopsis: Text Mining and Topic Modeling for Sport Science Literature
Description:

This package provides a comprehensive toolkit for mining, analyzing, and visualizing scientific literature in sport science domains. Provides functions for retrieving abstracts from Scopus', preprocessing text data, performing advanced topic modeling using Latent Dirichlet Allocation ('LDA'), Structural Topic Models ('STM'), and Correlated Topic Models ('CTM'), and creating publication-ready visualizations including keyword co-occurrence networks and topic trends. For methodological details see Blei et al. (2003) <doi:10.1162/jmlr.2003.3.4-5.993> for LDA', Roberts et al. (2014) <doi:10.1111/ajps.12103> for STM', and Blei and Lafferty (2007) <doi:10.1214/07-AOAS114> for CTM'.

r-splitwise 1.0.2
Propagated dependencies: r-rpart@4.1.24
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SplitWise
Licenses: GPL 3+
Build system: r
Synopsis: Hybrid Stepwise Regression with Single-Split Dummy Encoding
Description:

This package implements SplitWise', a hybrid regression approach that transforms numeric variables into either single-split (0/1) dummy variables or retains them as continuous predictors. The transformation is followed by stepwise selection to identify the most relevant variables. The default iterative mode adaptively explores partial synergies among variables to enhance model performance, while an alternative univariate mode applies simpler transformations independently to each predictor. For details, see Kurbucz et al. (2025) <doi:10.48550/arXiv.2505.15423>.

r-samplingr 1.0.1
Propagated dependencies: r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=samplingR
Licenses: GPL 2
Build system: r
Synopsis: Sampling and Estimation Methods
Description:

This package provides functions to take samples of data, sample size estimation and getting useful estimators such as total, mean, proportion about its population using simple random, stratified, systematic and cluster sampling.

r-ssw 0.2.1
Dependencies: python@3.11.14
Propagated dependencies: r-reticulate@1.44.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://nanx.me/ssw-r/
Licenses: Expat
Build system: r
Synopsis: Striped Smith-Waterman Algorithm for Sequence Alignment using SIMD
Description:

This package provides an R interface for SSW (Striped Smith-Waterman) via its Python binding ssw-py'. SSW is a fast C and C++ implementation of the Smith-Waterman algorithm for pairwise sequence alignment using Single-Instruction-Multiple-Data (SIMD) instructions. SSW enhances the standard algorithm by efficiently returning alignment information and suboptimal alignment scores. The core SSW library offers performance improvements for various bioinformatics tasks, including protein database searches, short-read alignments, primary and split-read mapping, structural variant detection, and read-overlap graph generation. These features make SSW particularly useful for genomic applications. Zhao et al. (2013) <doi:10.1371/journal.pone.0082138> developed the original C and C++ implementation.

r-simsem 0.5-17
Propagated dependencies: r-lavaan@0.6-20
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://simsem.org/
Licenses: GPL 2+
Build system: r
Synopsis: SIMulated Structural Equation Modeling
Description:

This package provides an easy framework for Monte Carlo simulation in structural equation modeling, which can be used for various purposes, such as such as model fit evaluation, power analysis, or missing data handling and planning.

r-support-bws3 0.2-1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=support.BWS3
Licenses: GPL 2+
Build system: r
Synopsis: Tools for Case 3 Best-Worst Scaling
Description:

This package provides basic functions that support an implementation of multi-profile case (Case 3) best-worst scaling (BWS). Case 3 BWS is a question-based survey method to elicit people's preferences for attribute levels. Case 3 BWS constructs various combinations of attribute levels (profiles) and then asks respondents to select the best and worst profiles in each choice set. A main function creates a dataset for the analysis from the choice sets and the responses to the questions. For details on Case 3 BWS, refer to Louviere et al. (2015) <doi:10.1017/CBO9781107337855>.

r-stanmomo 1.2.0
Propagated dependencies: r-tidyverse@2.0.0 r-tidyselect@1.2.1 r-tidyr@1.3.1 r-tibble@3.3.0 r-stanheaders@2.32.10 r-rstantools@2.5.0 r-rstan@2.32.7 r-rcppparallel@5.1.11-1 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-loo@2.8.0 r-latex2exp@0.9.6 r-httr@1.4.7 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-bridgesampling@1.2-1 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/kabarigou/StanMoMo
Licenses: GPL 3
Build system: r
Synopsis: Bayesian Mortality Modelling with 'Stan'
Description:

Implementation of popular mortality models using the rstan package, which provides the R interface to the Stan C++ library for Bayesian estimation. The package supports well-known models proposed in the actuarial and demographic literature including the Lee-Carter (1992) <doi:10.1080/01621459.1992.10475265> and the Cairns-Blake-Dowd (2006) <doi:10.1111/j.1539-6975.2006.00195.x> models. By a simple call, the user inputs deaths and exposures and the package outputs the MCMC simulations for each parameter, the log likelihoods and predictions. Moreover, the package includes tools for model selection and Bayesian model averaging by leave future-out validation.

r-shinyml 1.0.1
Propagated dependencies: r-tidyr@1.3.1 r-sparklyr@1.9.3 r-shinywidgets@0.9.0 r-shinyjs@2.1.0 r-shinycssloaders@1.1.0 r-shiny@1.11.1 r-plotly@4.11.0 r-lubridate@1.9.4 r-h2o@3.44.0.3 r-ggplot2@4.0.1 r-dygraphs@1.1.1.6 r-dt@0.34.0 r-dplyr@1.1.4 r-data-table@1.17.8 r-argonr@0.2.0 r-argondash@0.2.5
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://jeanbertinr.github.io/shinyMLpackage/
Licenses: GPL 3
Build system: r
Synopsis: Compare Supervised Machine Learning Models Using Shiny App
Description:

Implementation of a shiny app to easily compare supervised machine learning model performances. You provide the data and configure each model parameter directly on the shiny app. Different supervised learning algorithms can be tested either on Spark or H2O frameworks to suit your regression and classification tasks. Implementation of available machine learning models on R has been done by Lantz (2013, ISBN:9781782162148).

r-survivalvignettes 0.1.6
Propagated dependencies: r-survival@3.8-3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/bethatkinson/survivalVignettes
Licenses: LGPL 2.0+
Build system: r
Synopsis: Survival Analysis Vignettes and Optional Datasets
Description:

Vignettes for the survival package. Split from the survival package since the vignettes were getting large. Also, since survival is a recommended package it cannot make use of other packages outside of base+recommended (e.g. rmarkdown').

r-spcdanalyze 0.1.0
Propagated dependencies: r-plyr@1.8.9 r-nlme@3.1-168 r-lme4@1.1-37
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SPCDAnalyze
Licenses: FSDG-compatible
Build system: r
Synopsis: Design and Analyze Studies using the Sequential Parallel Comparison Design
Description:

Programs to find the sample size or power of studies using the Sequential Parallel Comparison Design (SPCD) and programs to analyze such studies. This is a clinical trial design where patients initially on placebo who did not respond are re-randomized between placebo and active drug in a second phase and the results of the two phases are pooled. The method of analyzing binary data with this design is described in Fava,Evins, Dorer and Schoenfeld(2003) <doi:10.1159/000069738>, and the method of analyzing continuous data is described in Chen, Yang, Hung and Wang (2011) <doi:10.1016/j.cct.2011.04.006>.

r-spatialprobit 1.0.4
Propagated dependencies: r-tmvtnorm@1.7 r-spdep@1.4-1 r-spatialreg@1.4-2 r-mvtnorm@1.3-3 r-matrix@1.7-4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://www.r-project.org
Licenses: GPL 2+
Build system: r
Synopsis: Spatial Probit Models
Description:

This package provides a collection of methods for the Bayesian estimation of Spatial Probit, Spatial Ordered Probit and Spatial Tobit Models. Original implementations from the works of LeSage and Pace (2009, ISBN: 1420064258) were ported and adjusted for R, as described in Wilhelm and de Matos (2013) <doi:10.32614/RJ-2013-013>.

r-sdefsr 0.7.22
Propagated dependencies: r-shiny@1.11.1 r-ggplot2@4.0.1 r-foreign@0.8-90
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/SIMIDAT/SDEFSR
Licenses: LGPL 3+
Build system: r
Synopsis: Subgroup Discovery with Evolutionary Fuzzy Systems
Description:

Implementation of evolutionary fuzzy systems for the data mining task called "subgroup discovery". In particular, the algorithms presented in this package are: M. J. del Jesus, P. Gonzalez, F. Herrera, M. Mesonero (2007) <doi:10.1109/TFUZZ.2006.890662> M. J. del Jesus, P. Gonzalez, F. Herrera (2007) <doi:10.1109/MCDM.2007.369416> C. J. Carmona, P. Gonzalez, M. J. del Jesus, F. Herrera (2010) <doi:10.1109/TFUZZ.2010.2060200> C. J. Carmona, V. Ruiz-Rodado, M. J. del Jesus, A. Weber, M. Grootveld, P. González, D. Elizondo (2015) <doi:10.1016/j.ins.2014.11.030> It also provide a Shiny App to ease the analysis. The algorithms work with data sets provided in KEEL, ARFF and CSV format and also with data.frame objects.

r-sisir 0.2.3
Propagated dependencies: r-tidyselect@1.2.1 r-tidyr@1.3.1 r-rspectra@0.16-2 r-rlang@1.1.6 r-reshape2@1.4.5 r-rcolorbrewer@1.1-3 r-ranger@0.17.0 r-purrr@1.2.0 r-mixomics@6.34.0 r-matrix@1.7-4 r-magrittr@2.0.4 r-glmnet@4.1-10 r-ggplot2@4.0.1 r-foreach@1.5.2 r-expm@1.0-0 r-dplyr@1.1.4 r-doparallel@1.0.17 r-dendextend@1.19.1 r-corelearn@1.57.3.1 r-boruta@9.0.0 r-aricode@1.0.3 r-adjclust@0.6.11
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://forgemia.inra.fr/sfcb/sisir
Licenses: GPL 2+
Build system: r
Synopsis: Select Intervals Suited for Functional Regression
Description:

Interval fusion and selection procedures for regression with functional inputs. Methods include a semiparametric approach based on Sliced Inverse Regression (SIR), as described in <doi:10.1007/s11222-018-9806-6> (standard ridge and sparse SIR are also included in the package) and a random forest based approach, as described in <doi:10.1002/sam.11705>.

r-sigr 1.1.5
Propagated dependencies: r-wrapr@2.1.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/WinVector/sigr/
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: Succinct and Correct Statistical Summaries for Reports
Description:

Succinctly and correctly format statistical summaries of various models and tests (F-test, Chi-Sq-test, Fisher-test, T-test, and rank-significance). This package also includes empirical tests, such as Monte Carlo and bootstrap distribution estimates.

r-spatialposition 2.1.3
Propagated dependencies: r-sp@2.2-0 r-sf@1.0-23 r-raster@3.6-32 r-isoband@0.2.7
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/riatelab/SpatialPosition
Licenses: GPL 3
Build system: r
Synopsis: Spatial Position Models
Description:

Computes spatial position models: the potential model as defined by Stewart (1941) <doi:10.1126/science.93.2404.89> and catchment areas as defined by Reilly (1931) or Huff (1964) <doi:10.2307/1249154>.

r-setariaviridis 0.1.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/NONONOexe/setariaviridis
Licenses: Expat
Build system: r
Synopsis: Field-Collected Data of Green Foxtail
Description:

Setaria viridis (green foxtail) is a common weed. This package contains measurements from individual branches of a wild Setaria viridis plant collected near the author's home. The data is intended for use in data analysis practice.

r-ssmrcd 2.0.1
Propagated dependencies: r-scales@1.4.0 r-rrcov@1.7-7 r-rootsolve@1.8.2.4 r-robustbase@0.99-6 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-matrix@1.7-4 r-ggplot2@4.0.1 r-expm@1.0-0 r-ellipse@0.5.0 r-desctools@0.99.60 r-dbscan@1.2.3 r-cellwise@2.5.5
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=ssMRCD
Licenses: GPL 3
Build system: r
Synopsis: Robust Estimators for Multi-Group and Spatial Data
Description:

Estimation of robust estimators for multi-group and spatial data including the casewise robust Spatially Smoothed Minimum Regularized Determinant (ssMRCD) estimator and its usage for local outlier detection as described in Puchhammer and Filzmoser (2023) <doi:10.1080/10618600.2023.2277875> as well as for sparse robust PCA for multi-source data described in Puchhammer, Wilms and Filzmoser (2024) <doi:10.48550/arXiv.2407.16299>. Moreover, a cellwise robust multi-group Gaussian mixture model (MG-GMM) is implemented as described in Puchhammer, Wilms and Filzmoser (2024) <doi:10.48550/arXiv.2504.02547>. Included are also complementary visualization and parameter tuning tools.

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