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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 search send a patch to ~whereiseveryone/toys@lists.sr.ht adding your channel as an entry in channels.scm.


r-serial 3.1.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=serial
Licenses: GPL 2
Build system: r
Synopsis: The Serial Interface Package
Description:

Enables reading and writing binary and ASCII data to RS232/RS422/RS485 or any other virtual serial interface of the computer.

r-saeval 1.0.0
Propagated dependencies: r-lmtest@0.9-40 r-ggspatial@1.1.10 r-ggplot2@4.0.3 r-car@3.1-5
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SAEval
Licenses: FSDG-compatible
Build system: r
Synopsis: Small Area Estimation Evaluation
Description:

Allows users to produce diagnostic procedures and graphic tools for the evaluation of Small Area estimators.

r-seqmagick 0.1.8
Propagated dependencies: r-yulab-utils@0.2.4 r-magrittr@2.0.5 r-biostrings@2.80.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/YuLab-SMU/seqmagick
Licenses: Artistic License 2.0
Build system: r
Synopsis: Sequence Manipulation Utilities
Description:

This package provides tools for reading and writing biological sequences in multiple formats, including FASTA', PHYLIP', CLUSTAL', STOCKHOLM', MEGA and GenBank'. Supports interleaved and sequential layouts where applicable, converts between formats, and manipulates sequence sets (e.g., filtering by patterns and computing consensus sequences from alignments). Also includes functions to download nucleotide records from NCBI by accession.

r-sssvcqr 0.0.4
Propagated dependencies: r-matrix@1.7-5 r-igraph@2.3.1 r-fnn@1.1.4.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/Stork343/sssvcqr
Licenses: GPL 3+
Build system: r
Synopsis: Sparse-Smooth Spatially Varying Coefficient Quantile Regression
Description:

This package implements sparse-smooth spatially varying coefficient quantile regression (SS-SVCQR), combining quantile regression of Koenker and Bassett (1978) <doi:10.2307/1913643>, grouped variable selection of Yuan and Lin (2006) <doi:10.1111/j.1467-9868.2005.00532.x>, graph regularization, and the alternating direction method of multipliers of Boyd et al. (2011) <doi:10.1561/2200000016>. The package provides graph-regularized estimation, spatially blocked cross-validation, prediction, diagnostics, and simulation helpers for global-local spatial quantile regression.

r-silhouette 0.9.6
Propagated dependencies: r-lifecycle@1.0.5 r-ggpubr@0.6.3 r-ggplot2@4.0.3 r-dplyr@1.2.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://kskbhat.github.io/Silhouette/
Licenses: GPL 2
Build system: r
Synopsis: Proximity Measure Based Diagnostics for Standard, Soft, and Multi-Way Clustering
Description:

Quantifies clustering quality by measuring both cohesion within clusters and separation between clusters. Implements advanced silhouette width computations for diverse clustering structures, including: simplified silhouette (Van der Laan et al., 2003) <doi:10.1080/0094965031000136012>, Probability of Alternative Cluster normalization methods (Raymaekers & Rousseeuw, 2022) <doi:10.1080/10618600.2022.2050249>, fuzzy clustering and silhouette diagnostics using membership probabilities (Campello & Hruschka, 2006; Menardi, 2011; Bhat & Kiruthika, 2024) <doi:10.1016/j.fss.2006.07.006>, <doi:10.1007/s11222-010-9169-0>, <doi:10.1080/23737484.2024.2408534>, and multi-way clustering extensions such as block and tensor clustering (Schepers et al., 2008; Bhat & Kiruthika, 2025) <doi:10.1007/s00357-008-9005-9>, <doi:10.21203/rs.3.rs-6973596/v1>. Provides tools for computation and visualization (Rousseeuw, 1987) <doi:10.1016/0377-0427(87)90125-7> to support robust and reproducible cluster diagnostics across standard, soft, and multi-way clustering settings.

r-skillings-mack 1.10
Propagated dependencies: r-matrixcalc@1.0-6 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=Skillings.Mack
Licenses: GPL 2+
Build system: r
Synopsis: The Skillings-Mack Test Statistic for Block Designs with Missing Observations
Description:

This package provides a generalization of the statistic used in Friedman's ANOVA method and in Durbin's rank test. This nonparametric statistical test is useful for the data obtained from block designs with missing observations occurring randomly. A resulting p-value is based on the chi-squared distribution and Monte Carlo method.

r-statisticteach1 0.1.2
Propagated dependencies: r-tidyr@1.3.2 r-tibble@3.3.1 r-shinywidgets@0.9.1 r-shinyjs@2.1.1 r-shinydashboard@0.7.3 r-shinybs@0.65.0 r-shiny@1.13.0 r-rlang@1.2.0 r-readxl@1.5.0 r-rcolorbrewer@1.1-3 r-mixdist@0.5-5 r-magrittr@2.0.5 r-ggplot2@4.0.3 r-dt@0.34.0 r-dplyr@1.2.1 r-desctools@0.99.60 r-descriptr@0.6.0 r-colourpicker@1.3.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=StatisticTeach1
Licenses: GPL 3
Build system: r
Synopsis: Interactive Tool for Statistics and Probability
Description:

This package provides a Shiny application designed to support the learning of basic concepts in statistics and probability. The tool provides an interactive interface that allows students to explore and visualize different statistical concepts intuitively, including descriptive statistics for continuous and qualitative variables, and probability distributions.

r-scrutiny 0.6.1
Propagated dependencies: r-tidyselect@1.2.1 r-tidyr@1.3.2 r-tibble@3.3.1 r-stringr@1.6.0 r-rlang@1.2.0 r-purrr@1.2.2 r-magrittr@2.0.5 r-lifecycle@1.0.5 r-glue@1.8.1 r-ggrepel@0.9.8 r-ggplot2@4.0.3 r-dplyr@1.2.1 r-corrr@0.4.5 r-cli@3.6.6
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://lhdjung.github.io/scrutiny/
Licenses: Expat
Build system: r
Synopsis: Error Detection in Science
Description:

Test published summary statistics for consistency (Brown and Heathers, 2017, <doi:10.1177/1948550616673876>; Allard, 2018, <https://aurelienallard.netlify.app/post/anaytic-grimmer-possibility-standard-deviations/>; Heathers and Brown, 2019, <https://osf.io/5vb3u/>). The package also provides infrastructure for implementing new error detection techniques.

r-spatgraphs 3.4
Propagated dependencies: r-rcpp@1.1.1-1.1 r-matrix@1.7-5
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=spatgraphs
Licenses: GPL 2+
Build system: r
Synopsis: Graph Edge Computations for Spatial Point Patterns
Description:

Graphs (or networks) and graph component calculations for spatial locations in 1D, 2D, 3D etc.

r-simpleboot 1.1-8
Propagated dependencies: r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/rdpeng/simpleboot
Licenses: GPL 2+
Build system: r
Synopsis: Simple Bootstrap Routines
Description:

Simple bootstrap routines.

r-srm 0.4-26
Propagated dependencies: r-rcpparmadillo@15.2.6-1 r-rcpp@1.1.1-1.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/alexanderrobitzsch/srm
Licenses: GPL 2+
Build system: r
Synopsis: Structural Equation Modeling for the Social Relations Model
Description:

This package provides functionality for structural equation modeling for the social relations model (Kenny & La Voie, 1984; <doi:10.1016/S0065-2601(08)60144-6>; Warner, Kenny, & Soto, 1979, <doi:10.1037/0022-3514.37.10.1742>). Maximum likelihood estimation (Gill & Swartz, 2001, <doi:10.2307/3316080>; Nestler, 2018, <doi:10.3102/1076998617741106>) and least squares estimation is supported (Bond & Malloy, 2018, <doi:10.1016/B978-0-12-811967-9.00014-X>).

r-shinytoastify 2.0.0
Propagated dependencies: r-shiny@1.13.0 r-reactr@0.6.1 r-htmltools@0.5.9 r-fontawesome@0.5.3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/stla/shinyToastify
Licenses: GPL 3
Build system: r
Synopsis: Pretty Notifications for 'Shiny'
Description:

This is a wrapper of the React library React-Toastify'. It allows to show some notifications (toasts) in Shiny applications. There are options for the style, the position, the transition effect, and more.

r-samplingstrata 1.5-5
Propagated dependencies: r-samplingbigdata@1.0.0 r-pbapply@1.7-4 r-memoise@2.0.1 r-glue@1.8.1 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://barcaroli.github.io/SamplingStrata/
Licenses: GPL 2+
Build system: r
Synopsis: Optimal Stratification of Sampling Frames for Multipurpose Sampling Surveys
Description:

This package provides tools for the optimization of stratified sampling design. It determines a stratification of a sampling frame that minimizes sample cost while satisfying precision constraints in a multivariate and multidomain context. The approach relies on a genetic algorithm; each candidate partition of the frame is an individual whose fitness is evaluated via the Bethel-Chromy allocation to meet target precisions. Functions support analysis of optimization results, labeling of the frame with new strata, and drawing a sample according to the optimal allocation. Algorithmic components adapt code from the genalg package. See M. Ballin and G. Barcaroli (2020) "R package SamplingStrata: new developments and extension to Spatial Sampling" <doi:10.48550/arXiv.2004.09366>.

r-soar 1.0-1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SOAR
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: Memory Management in R by Delayed Assignments
Description:

Allows objects to be stored on disc and automatically recalled into memory, as required, by delayed assignment.

r-sdafilter 1.0.1
Propagated dependencies: r-selectiveinference@1.2.5 r-poet@2.0 r-glmnet@5.0 r-glasso@1.11
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=sdafilter
Licenses: GPL 2+
Build system: r
Synopsis: Symmetrized Data Aggregation
Description:

We develop a new class of distribution free multiple testing rules for false discovery rate (FDR) control under general dependence. A key element in our proposal is a symmetrized data aggregation (SDA) approach to incorporating the dependence structure via sample splitting, data screening and information pooling. The proposed SDA filter first constructs a sequence of ranking statistics that fulfill global symmetry properties, and then chooses a data driven threshold along the ranking to control the FDR. For more information, see the website below and the accompanying paper: Du et al. (2023), "False Discovery Rate Control Under General Dependence By Symmetrized Data Aggregation", <doi:10.1080/01621459.2021.1945459>. Some optional functionality uses the archived R packages â hugeâ and â pfaâ , which are not available from CRANâ s main repositories. Users who need this optional functionality can obtain them from the CRAN Archive as follows: â hugeâ at <https://cran.r-project.org/src/contrib/Archive/huge/>; â pfaâ at <https://cran.r-project.org/src/contrib/Archive/pfa/>.

r-starvars 1.1.11
Propagated dependencies: r-zoo@1.8-15 r-xts@0.14.2 r-vars@1.6-1 r-quantmod@0.4.28 r-optimparallel@1.0-2 r-matrixcalc@1.0-6 r-mass@7.3-65 r-lessr@4.5.5 r-ks@1.15.2 r-foreach@1.5.2 r-dosnow@1.0.20
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/andbucci/starvars
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Vector Logistic Smooth Transition Models Estimation and Prediction
Description:

Allows the user to estimate a vector logistic smooth transition autoregressive model via maximum log-likelihood or nonlinear least squares. It further permits to test for linearity in the multivariate framework against a vector logistic smooth transition autoregressive model with a single transition variable. The estimation method is discussed in Terasvirta and Yang (2014, <doi:10.1108/S0731-9053(2013)0000031008>). Also, realized covariances can be constructed from stock market prices or returns, as explained in Andersen et al. (2001, <doi:10.1016/S0304-405X(01)00055-1>).

r-sns 1.2.2
Propagated dependencies: r-numderiv@2016.8-1.1 r-mvtnorm@1.3-7 r-coda@0.19-4.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=sns
Licenses: GPL 2+
Build system: r
Synopsis: Stochastic Newton Sampler (SNS)
Description:

Stochastic Newton Sampler (SNS) is a Metropolis-Hastings-based, Markov Chain Monte Carlo sampler for twice differentiable, log-concave probability density functions (PDFs) where the proposal density function is a multivariate Gaussian resulting from a second-order Taylor-series expansion of log-density around the current point. The mean of the Gaussian proposal is the full Newton-Raphson step from the current point. A Boolean flag allows for switching from SNS to Newton-Raphson optimization (by choosing the mean of proposal function as next point). This can be used during burn-in to get close to the mode of the PDF (which is unique due to concavity). For high-dimensional densities, mixing can be improved via state space partitioning strategy, in which SNS is applied to disjoint subsets of state space, wrapped in a Gibbs cycle. Numerical differentiation is available when analytical expressions for gradient and Hessian are not available. Facilities for validation and numerical differentiation of log-density are provided. Note: Formerly available versions of the MfUSampler can be obtained from the archive <https://cran.r-project.org/src/contrib/Archive/MfUSampler/>.

r-serosv 1.3.0
Propagated dependencies: r-tidyr@1.3.2 r-stringr@1.6.0 r-stanheaders@2.32.10 r-scam@1.2-22 r-rstantools@2.6.0 r-rstan@2.32.7 r-rlang@1.2.0 r-rcppparallel@5.1.11-2 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.1-1.1 r-purrr@1.2.2 r-proc@1.19.0.1 r-patchwork@1.3.2 r-mvtnorm@1.3-7 r-mixdist@0.5-5 r-mgcv@1.9-4 r-magrittr@2.0.5 r-locfit@1.5-9.12 r-janitor@2.2.1 r-ggplot2@4.0.3 r-dplyr@1.2.1 r-boot@1.3-32 r-bh@1.90.0-1 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://oucru-modelling.github.io/serosv/
Licenses: Expat
Build system: r
Synopsis: Model Infectious Disease Parameters from Serosurveys
Description:

An easy-to-use and efficient tool to estimate infectious diseases parameters using serological data. Implemented models include SIR models (basic_sir_model(), static_sir_model(), mseir_model(), sir_subpops_model()), parametric models (polynomial_model(), fp_model()), nonparametric models (lp_model()), semiparametric models (penalized_splines_model()), hierarchical models (hierarchical_bayesian_model()). The package is based on the book "Modeling Infectious Disease Parameters Based on Serological and Social Contact Data: A Modern Statistical Perspective" (Hens, Niel & Shkedy, Ziv & Aerts, Marc & Faes, Christel & Damme, Pierre & Beutels, Philippe., 2013) <doi:10.1007/978-1-4614-4072-7>.

r-splitreg 1.0.3
Propagated dependencies: r-rcpparmadillo@15.2.6-1 r-rcpp@1.1.1-1.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SplitReg
Licenses: GPL 2+
Build system: r
Synopsis: Split Regularized Regression
Description:

This package provides functions for computing split regularized estimators defined in Christidis, Lakshmanan, Smucler and Zamar (2019) <doi:10.48550/arXiv.1712.03561>. The approach fits linear regression models that split the set of covariates into groups. The optimal split of the variables into groups and the regularized estimation of the regression coefficients are performed by minimizing an objective function that encourages sparsity within each group and diversity among them. The estimated coefficients are then pooled together to form the final fit.

r-scidesignr 1.0.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=scidesignR
Licenses: FSDG-compatible
Build system: r
Synopsis: Data Sets from Design and Analysis of Experiments and Observational Studies using R
Description:

Data used in Taback, N. (2022). Design and Analysis of Experiments and Observational Studies using R. Chapman & Hall/CRC.

r-sieveph 1.1
Propagated dependencies: r-survival@3.8-6 r-scales@1.4.0 r-rcpparmadillo@15.2.6-1 r-rcpp@1.1.1-1.1 r-plyr@1.8.9 r-np@0.70-2 r-ggpubr@0.6.3 r-ggplot2@4.0.3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/mjuraska/sievePH
Licenses: GPL 2
Build system: r
Synopsis: Sieve Analysis Methods for Proportional Hazards Models
Description:

This package implements a suite of semiparametric and nonparametric kernel-smoothed estimation and testing procedures for continuous mark-specific stratified hazard ratio (treatment/placebo) models in a randomized treatment efficacy trial with a time-to-event endpoint. Semiparametric methods, allowing multivariate marks, are described in Juraska M and Gilbert PB (2013), Mark-specific hazard ratio model with multivariate continuous marks: an application to vaccine efficacy. Biometrics 69(2):328-337 <doi:10.1111/biom.12016>, and in Juraska M and Gilbert PB (2016), Mark-specific hazard ratio model with missing multivariate marks. Lifetime Data Analysis 22(4):606-25 <doi:10.1007/s10985-015-9353-9>. Nonparametric kernel-smoothed methods, allowing univariate marks only, are described in Sun Y and Gilbert PB (2012), Estimation of stratified markâ specific proportional hazards models with missing marks. Scandinavian Journal of Statistics

r-shoredate 1.1.1
Propagated dependencies: r-terra@1.9-27 r-sf@1.1-1 r-ggspatial@1.1.10 r-ggridges@0.5.7 r-ggrepel@0.9.8 r-ggplot2@4.0.3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/isakro/shoredate
Licenses: GPL 3+
Build system: r
Synopsis: Shoreline Dating Coastal Stone Age Sites
Description:

This package provides tools for shoreline dating coastal Stone Age sites. The implemented method was developed in Roalkvam (2023) <doi:10.1016/j.quascirev.2022.107880> for the Norwegian Skagerrak coast. Although it can be extended to other areas, this also forms the core area for application of the package. Shoreline dating is based on the present-day elevation of a site, a reconstruction of past relative sea-level change, and empirically derived estimates of the likely elevation of the sites above the contemporaneous sea-level when they were in use. The geographical and temporal coverage of the method thus follows from the availability of local geological reconstructions of shoreline displacement and the degree to which the settlements to be dated have been located on or close to the shoreline when they were in use. Methods for numerical treatment and visualisation of the dates are provided, along with basic tools for visualising and evaluating the location of sites.

r-scaper 0.2.0
Propagated dependencies: r-xml2@1.5.2 r-vam@1.1.0 r-stringr@1.6.0 r-seuratobject@5.4.0 r-seurat@5.5.0 r-magrittr@2.0.5 r-dplyr@1.2.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=scaper
Licenses: GPL 2+
Build system: r
Synopsis: Single Cell Transcriptomics-Level Cytokine Activity Prediction and Estimation
Description:

Generates cell-level cytokine activity estimates using relevant information from gene sets constructed with the CytoSig and the Reactome databases and scored using the modified Variance-adjusted Mahalanobis (VAM) framework for single-cell RNA-sequencing (scRNA-seq) data. CytoSig database is described in: Jiang at al., (2021) <doi:10.1038/s41592-021-01274-5>. Reactome database is described in: Gillespie et al., (2021) <doi:10.1093/nar/gkab1028>. The VAM method is outlined in: Frost (2020) <doi:10.1093/nar/gkaa582>.

r-samur 1.1
Propagated dependencies: r-matching@4.10-15
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SAMUR
Licenses: GPL 2+
Build system: r
Synopsis: Stochastic Augmentation of Matched Data Using Restriction Methods
Description:

Augmenting a matched data set by generating multiple stochastic, matched samples from the data using a multi-dimensional histogram constructed from dropping the input matched data into a multi-dimensional grid built on the full data set. The resulting stochastic, matched sets will likely provide a collectively higher coverage of the full data set compared to the single matched set. Each stochastic match is without duplication, thus allowing downstream validation techniques such as cross-validation to be applied to each set without concern for overfitting.

Total packages: 72465