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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-scbiclust 1.0.2
Propagated dependencies: r-sparcl@1.0.4 r-sigclust@1.1.0.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SCBiclust
Licenses: Expat
Build system: r
Synopsis: Identifies Mean, Variance, and Hierarchically Clustered Biclusters
Description:

Identifies a bicluster, a submatrix of the data such that the features and observations within the submatrix differ from those not contained in submatrix, using a two-step method. In the first step, observations in the bicluster are identified to maximize the sum of weighted between cluster feature differences. The method is described in Helgeson et al. (2020) <doi:10.1111/biom.13136>. SCBiclust can be used to identify biclusters which differ based on feature means, feature variances, or more general differences.

r-sociome 3.0.0
Propagated dependencies: r-tidyr@1.3.1 r-tidycensus@1.7.5 r-tibble@3.3.0 r-stringr@1.6.0 r-rlang@1.1.6 r-purrr@1.2.0 r-psych@2.5.6 r-mice@3.18.0 r-magrittr@2.0.4 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=sociome
Licenses: Expat
Build system: r
Synopsis: Operationalizing Social Determinants of Health Data for Researchers
Description:

Accesses raw data via API and calculates social determinants of health measures for user-specified locations in the US, returning them in tidyverse- and sf-compatible data frames.

r-spfa 1.0
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=spfa
Licenses: Expat
Build system: r
Synopsis: Semi-Parametric Factor Analysis
Description:

Estimation, scoring, and plotting functions for the semi-parametric factor model proposed by Liu & Wang (2022) <doi:10.1007/s11336-021-09832-8> and Liu & Wang (2023) <arXiv:2303.10079>. Both the conditional densities of observed responses given the latent factors and the joint density of latent factors are estimated non-parametrically. Functional parameters are approximated by smoothing splines, whose coefficients are estimated by penalized maximum likelihood using an expectation-maximization (EM) algorithm. E- and M-steps can be parallelized on multi-thread computing platforms that support OpenMP'. Both continuous and unordered categorical response variables are supported.

r-speff2trial 1.0.5
Propagated dependencies: r-survival@3.8-3 r-leaps@3.2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/mjuraska/speff2trial
Licenses: GPL 2
Build system: r
Synopsis: Semiparametric Efficient Estimation for a Two-Sample Treatment Effect
Description:

This package performs estimation and testing of the treatment effect in a 2-group randomized clinical trial with a quantitative, dichotomous, or right-censored time-to-event endpoint. The method improves efficiency by leveraging baseline predictors of the endpoint. The inverse probability weighting technique of Robins, Rotnitzky, and Zhao (JASA, 1994) is used to provide unbiased estimation when the endpoint is missing at random.

r-sim-ba 0.1.0
Propagated dependencies: r-survival@3.8-3 r-scales@1.4.0 r-rlang@1.1.6 r-pbapply@1.7-4 r-ggplot2@4.0.1 r-cobalt@4.6.2 r-chk@0.10.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=sim.BA
Licenses: GPL 2+
Build system: r
Synopsis: Simulation-Based Bias Analysis for Observational Studies
Description:

Allows user to conduct a simulation based quantitative bias analysis using covariate structures generated with individual-level data to characterize the bias arising from unmeasured confounding. Users can specify their desired data generating mechanisms to simulate data and quantitatively summarize findings in an end-to-end application using this package.

r-sure 0.2.0
Propagated dependencies: r-gridextra@2.3 r-goftest@1.2-3 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/AFIT-R/sure
Licenses: GPL 2+
Build system: r
Synopsis: Surrogate Residuals for Ordinal and General Regression Models
Description:

An implementation of the surrogate approach to residuals and diagnostics for ordinal and general regression models; for details, see Liu and Zhang (2017) <doi:10.1080/01621459.2017.1292915>. These residuals can be used to construct standard residual plots for model diagnostics (e.g., residual-vs-fitted value plots, residual-vs-covariate plots, Q-Q plots, etc.). The package also provides an autoplot function for producing standard diagnostic plots using ggplot2 graphics. The package currently supports cumulative link models from packages MASS', ordinal', rms', and VGAM'. Support for binary regression models using the standard glm function is also available.

r-stormr 0.2.1
Propagated dependencies: r-zoo@1.8-14 r-terra@1.8-86 r-stringr@1.6.0 r-sf@1.0-23 r-rworldmap@1.3-8 r-ncdf4@1.24 r-maps@3.4.3 r-leaflet@2.2.3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://umr-amap.github.io/StormR/
Licenses: GPL 3+
Build system: r
Synopsis: Analyzing the Behaviour of Wind Generated by Tropical Storms and Cyclones
Description:

Set of functions to quantify and map the behaviour of winds generated by tropical storms and cyclones in space and time. It includes functions to compute and analyze fields such as the maximum sustained wind field, power dissipation index and duration of exposure to winds above a given threshold. It also includes functions to map the trajectories as well as characteristics of the storms.

r-survpresmooth 1.1-12
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=survPresmooth
Licenses: GPL 2+
Build system: r
Synopsis: Presmoothed Estimation in Survival Analysis
Description:

Presmoothed estimators of survival, density, cumulative and non-cumulative hazard functions with right-censored survival data. For details, see Lopez-de-Ullibarri and Jacome (2013) <doi:10.18637/jss.v054.i11>.

r-svgviewr 1.4.3
Propagated dependencies: r-rjson@0.2.23 r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://aaronolsen.github.io/tutorials/visualization3d.html
Licenses: GPL 2+
Build system: r
Synopsis: 3D Animated Interactive Visualizations Using SVG and WebGL
Description:

This package creates 3D animated, interactive visualizations that can be viewed in a web browser.

r-scouter 1.0.0
Propagated dependencies: r-ggpubr@0.6.2 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SCOUTer
Licenses: GPL 3
Build system: r
Synopsis: Simulate Controlled Outliers
Description:

Using principal component analysis as a base model, SCOUTer offers a new approach to simulate outliers in a simple and precise way. The user can generate new observations defining them by a pair of well-known statistics: the Squared Prediction Error (SPE) and the Hotelling's T^2 (T^2) statistics. Just by introducing the target values of the SPE and T^2, SCOUTer returns a new set of observations with the desired target properties. Authors: Alba González, Abel Folch-Fortuny, Francisco Arteaga and Alberto Ferrer (2020).

r-soql 0.1.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=soql
Licenses: Expat
Build system: r
Synopsis: Helps Make Socrata Open Data API Calls
Description:

Used to construct the URLs and parameters of Socrata Open Data API <https://dev.socrata.com> calls, using the API's SoQL parameter format. Has method-chained and sensical syntax. Plays well with pipes.

r-ssabss 0.1.1
Propagated dependencies: r-zoo@1.8-14 r-xts@0.14.1 r-tsbss@1.0.0 r-jade@2.0-4 r-ictest@0.3-6 r-ggplot2@4.0.1 r-bssprep@0.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=ssaBSS
Licenses: GPL 2+
Build system: r
Synopsis: Stationary Subspace Analysis
Description:

Stationary subspace analysis (SSA) is a blind source separation (BSS) variant where stationary components are separated from non-stationary components. Several SSA methods for multivariate time series are provided here (Flumian et al. (2021); Hara et al. (2010) <doi:10.1007/978-3-642-17537-4_52>) along with functions to simulate time series with time-varying variance and autocovariance (Patilea and Raissi(2014) <doi:10.1080/01621459.2014.884504>).

r-steadyica 1.0.1
Propagated dependencies: r-rcpp@1.1.0 r-mass@7.3-65 r-combinat@0.0-8 r-clue@0.3-66
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=steadyICA
Licenses: GPL 2+
Build system: r
Synopsis: ICA and Tests of Independence via Multivariate Distance Covariance
Description:

This package provides functions related to multivariate measures of independence and ICA: -estimate independent components by minimizing distance covariance; -conduct a test of mutual independence based on distance covariance; -estimate independent components via infomax (a popular method but generally performs poorer than mdcovica, ProDenICA, and/or fastICA, but is useful for comparisons); -order indepedent components by skewness; -match independent components from multiple estimates; -other functions useful in ICA.

r-sieveph 1.1
Propagated dependencies: r-survival@3.8-3 r-scales@1.4.0 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-plyr@1.8.9 r-np@0.60-18 r-ggpubr@0.6.2 r-ggplot2@4.0.1
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-serofoi 1.0.3
Propagated dependencies: 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-purrr@1.2.0 r-loo@2.8.0 r-glue@1.8.0 r-ggplot2@4.0.1 r-expm@1.0-0 r-dplyr@1.1.4 r-cowplot@1.2.0 r-config@0.3.2 r-checkmate@2.3.3 r-bh@1.87.0-1 r-bayesplot@1.14.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/epiverse-trace/serofoi
Licenses: Expat
Build system: r
Synopsis: Bayesian Estimation of the Force of Infection from Serological Data
Description:

Estimating the force of infection from time varying, age varying, or constant serocatalytic models from population based seroprevalence studies using a Bayesian framework, including data simulation functions enabling the generation of serological surveys based on this models. This tool also provides a flexible prior specification syntax for the force of infection and the seroreversion rate, as well as methods to assess model convergence and comparison criteria along with useful visualisation functions.

r-stgam 1.2.0
Propagated dependencies: r-stringr@1.6.0 r-purrr@1.2.0 r-mgcv@1.9-4 r-magrittr@2.0.4 r-glue@1.8.0 r-foreach@1.5.2 r-dplyr@1.1.4 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/lexcomber/stgam
Licenses: Expat
Build system: r
Synopsis: Spatially and Temporally Varying Coefficient Models Using Generalized Additive Models
Description:

This package provides a framework for undertaking space and time varying coefficient models (varying parameter models) using a Generalized Additive Model (GAM) with smooths approach. The framework suggests the need to investigate for the presence and nature of any space-time dependencies in the data. It proposes a workflow that creates and refines an initial space-time GAM and includes tools to create and evaluate multiple model forms. The workflow sequence is to: i) Prepare the data by lengthening it to have a single location and time variables for each observation. ii) Create all possible space and/or time models in which each predictor is specified in different ways in smooths. iii) Evaluate each model via their AIC value and pick the best one. iv) Create the final model. v) Calculate the varying coefficient estimates to quantify how the relationships between the target and predictor variables vary over space, time or space-time. vi) Create maps, time series plots etc. The number of knots used in each smooth can be specified directly or iteratively increased. This is illustrated with a climate point dataset of the dry rain forest in South America. This builds on work in Comber et al (2024) <doi:10.1080/13658816.2023.2270285> and Comber et al (2004) <doi:10.3390/ijgi13120459>.

r-sensortowerr 0.9.4
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-openssl@2.3.4 r-lubridate@1.9.4 r-jsonlite@2.0.0 r-httr2@1.2.1 r-httr@1.4.7 r-glue@1.8.0 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=sensortowerR
Licenses: Expat
Build system: r
Synopsis: Interface to 'Sensor Tower' Mobile App Intelligence API
Description:

Interface to the Sensor Tower API <https://app.sensortower.com/api/docs/app_analysis> for mobile app analytics and market intelligence. Provides functions to retrieve app metadata, publisher information, download and revenue estimates, active user metrics, category rankings, and market trends. The package includes data processing utilities to clean and aggregate metrics across platforms, automatic app name resolution, and tools for generating professional analytics dashboards. Supports both iOS and Android app ecosystems with unified data structures for cross-platform analysis.

r-swissair 1.1.6
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SwissAir
Licenses: GPL 3+
Build system: r
Synopsis: Air Quality Data of Switzerland for One Year in 30 Min Resolution
Description:

Ozone, NOx (= Sum of nitrogen monoxide and nitrogen dioxide), nitrogen monoxide, ambient temperature, dew point, wind speed and wind direction at 3 sites around lake of Lucerne in Central Switzerland in 30 min time resolution for year 2004.

r-snreg 1.2.0
Propagated dependencies: r-npsf@0.8.0 r-formula@1.2-5
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://olegbadunenko.github.io/snreg/
Licenses: GPL 3
Build system: r
Synopsis: Regression with Skew-Normally Distributed Error Term
Description:

Models with skewâ normally distributed and thus asymmetric error terms, implementing the methods developed in Badunenko and Henderson (2023) "Production analysis with asymmetric noise" <doi:10.1007/s11123-023-00680-5>. The package provides tools to estimate regression models with skewâ normal error terms, allowing both the variance and skewness parameters to be heteroskedastic. It also includes a stochastic frontier framework that accommodates both i.i.d. and heteroskedastic inefficiency terms.

r-scplot 0.6.0
Propagated dependencies: r-scan@0.67.0 r-mblm@0.12.1 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=scplot
Licenses: GPL 3+
Build system: r
Synopsis: Plot Function for Single-Case Data Frames
Description:

Add-on for the scan package that creates plots from single-case data frames ('scdf'). It includes functions for styling single-case plots, adding phase-based lines to indicate various statistical parameters, and predefined themes for presentations and publications. More information and in depth examples can be found in the online book "Analyzing Single-Case Data with R and scan" Jürgen Wilbert (2025) <https://jazznbass.github.io/scan-Book/>.

r-smvr 0.2.2
Propagated dependencies: r-vctrs@0.6.5 r-rlang@1.1.6 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://eitsupi.github.io/smvr/
Licenses: Expat
Build system: r
Synopsis: Simple Implementation of Semantic Versioning (SemVer)
Description:

Simple implementation of Semantic Versioning 2.0.0 ('SemVer') on the vctrs package. This package provides a simple way to create, compare, and manipulate semantic versions in R. It is designed to be lightweight and easy to use.

r-snplinkage 1.2.0
Propagated dependencies: r-snprelate@1.44.0 r-reshape2@1.4.5 r-magrittr@2.0.4 r-knitr@1.50 r-gwastools@1.56.0 r-gtable@0.3.6 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-gdsfmt@1.46.0 r-data-table@1.17.8 r-cowplot@1.2.0 r-biomart@2.66.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://gitlab.com/thomaschln/snplinkage
Licenses: GPL 3
Build system: r
Synopsis: Single Nucleotide Polymorphisms Linkage Disequilibrium Visualizations
Description:

Linkage disequilibrium visualizations of up to several hundreds of single nucleotide polymorphisms (SNPs), annotated with chromosomic positions and gene names. Two types of plots are available for small numbers of SNPs (<40) and for large numbers (tested up to 500). Both can be extended by combining other ggplots, e.g. association studies results, and functions enable to directly visualize the effect of SNP selection methods, as minor allele frequency filtering and TagSNP selection, with a second correlation heatmap. The SNPs correlations are computed on Genotype Data objects from the GWASTools package using the SNPRelate package, and the plots are customizable ggplot2 and gtable objects and are annotated using the biomaRt package. Usage is detailed in the vignette with example data and results from up to 500 SNPs of 1,200 scans are in Charlon T. (2019) <doi:10.13097/archive-ouverte/unige:161795>.

r-spcalda 1.0
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=SPCALDA
Licenses: GPL 2
Build system: r
Synopsis: New Reduced-Rank Linear Discriminant Analysis Method
Description:

This package provides a new reduced-rank LDA method which works for high dimensional multi-class data.

r-s2net 1.0.7
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/jlaria/s2net
Licenses: GPL 2+
Build system: r
Synopsis: The Generalized Semi-Supervised Elastic-Net
Description:

This package implements the generalized semi-supervised elastic-net. This method extends the supervised elastic-net problem, and thus it is a practical solution to the problem of feature selection in semi-supervised contexts. Its mathematical formulation is presented from a general perspective, covering a wide range of models. We focus on linear and logistic responses, but the implementation could be easily extended to other losses in generalized linear models. We develop a flexible and fast implementation, written in C++ using RcppArmadillo and integrated into R via Rcpp modules. See Culp, M. 2013 <doi:10.1080/10618600.2012.657139> for references on the Joint Trained Elastic-Net.

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