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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-sasmarkdown 0.8.7
Propagated dependencies: r-xfun@0.54 r-knitr@1.50
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://www.ssc.wisc.edu/~hemken/SASworkshops/sas.html#writing-sas-documentation
Licenses: Expat
Build system: r
Synopsis: 'SAS' Markdown
Description:

Settings and functions to extend the knitr SAS engine.

r-samplevadir 1.0.0
Propagated dependencies: r-splitstackshape@1.4.8 r-lubridate@1.9.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/tswanson222/sampleVADIR
Licenses: GPL 3+
Build system: r
Synopsis: Draw Stratified Samples from the VADIR Database
Description:

Affords researchers the ability to draw stratified samples from the U.S. Department of Veteran's Affairs/Department of Defense Identity Repository (VADIR) database according to a variety of population characteristics. The VADIR database contains information for all veterans who were separated from the military after 1980. The central utility of the present package is to integrate data cleaning and formatting for the VADIR database with the stratification methods described by Mahto (2019) <https://CRAN.R-project.org/package=splitstackshape>. Data from VADIR are not provided as part of this package.

r-snbdata 0.2.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: http://enricoschumann.net/R/packages/SNBdata/
Licenses: GPL 3
Build system: r
Synopsis: Download Data from the Swiss National Bank (SNB)
Description:

Download data (tables and datasets) from the Swiss National Bank (SNB; <https://www.snb.ch/en>), the Swiss central bank. The package is lightweight and comes with few dependencies; suggested packages are used only if data is to be transformed into particular data structures, for instance into zoo objects. Downloaded data can optionally be cached, to avoid repeated downloads of the same files.

r-sox 1.2.2
Propagated dependencies: r-survival@3.8-3 r-rcpp@1.1.0 r-glmnet@4.1-10
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=sox
Licenses: GPL 3+
Build system: r
Synopsis: Structured Learning in Time-Dependent Cox Models
Description:

Efficient procedures for fitting and cross-validating the structurally-regularized time-dependent Cox models.

r-spfilter 2.2.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/sjuhl/spfilteR
Licenses: GPL 3
Build system: r
Synopsis: Semiparametric Spatial Filtering with Eigenvectors in (Generalized) Linear Models
Description:

This package provides tools to decompose (transformed) spatial connectivity matrices and perform supervised or unsupervised semiparametric spatial filtering in a regression framework. The package supports unsupervised spatial filtering in standard linear as well as some generalized linear regression models.

r-subgrpid 0.12
Propagated dependencies: r-survival@3.8-3 r-rpart@4.1.24 r-matrix@1.7-4 r-mass@7.3-65 r-glmnet@4.1-10 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=SubgrpID
Licenses: GPL 2+
Build system: r
Synopsis: Patient Subgroup Identification for Clinical Drug Development
Description:

Implementation of Sequential BATTing (bootstrapping and aggregating of thresholds from trees) for developing threshold-based multivariate (prognostic/predictive) biomarker signatures. Variable selection is automatically built-in. Final signatures are returned with interaction plots for predictive signatures. Cross-validation performance evaluation and testing dataset results are also output. Detail algorithms are described in Huang et al (2017) <doi:10.1002/sim.7236>.

r-stratpal 0.7.1
Propagated dependencies: r-paleots@0.6.2 r-admtools@0.6.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://mindthegap-erc.github.io/StratPal/
Licenses: FSDG-compatible
Build system: r
Synopsis: Stratigraphic Paleobiology Modeling Pipelines
Description:

The fossil record is a joint expression of ecological, taphonomic, evolutionary, and stratigraphic processes (Holland and Patzkowsky, 2012, ISBN:978-0226649382). This package allowing to simulate biological processes in the time domain (e.g., trait evolution, fossil abundance, phylogenetic trees), and examine how their expression in the rock record (stratigraphic domain) is influenced based on age-depth models, ecological niche models, and taphonomic effects. Functions simulating common processes used in modeling trait evolution, biostratigraphy or event type data such as first/last occurrences are provided and can be used standalone or as part of a pipeline. The package comes with example data sets and tutorials in several vignettes, which can be used as a template to set up one's own simulation.

r-spark-sas7bdat 1.4
Propagated dependencies: r-sparklyr@1.9.3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/bnosac/spark.sas7bdat
Licenses: GPL 3
Build system: r
Synopsis: Read in 'SAS' Data ('.sas7bdat' Files) into 'Apache Spark'
Description:

Read in SAS Data ('.sas7bdat Files) into Apache Spark from R. Apache Spark is an open source cluster computing framework available at <http://spark.apache.org>. This R package uses the spark-sas7bdat Spark package (<https://spark-packages.org/package/saurfang/spark-sas7bdat>) to import and process SAS data in parallel using Spark'. Hereby allowing to execute dplyr statements in parallel on top of SAS data.

r-ssnbler 1.1.1
Propagated dependencies: r-withr@3.0.2 r-ssn2@0.4.0 r-sf@1.0-23 r-rsqlite@2.4.4 r-pdist@1.2.1 r-igraph@2.2.1 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/pet221/SSNbler
Licenses: GPL 3+
Build system: r
Synopsis: Assemble 'SSN' Objects
Description:

Import, create and assemble data needed to fit spatial-statistical stream-network models using the SSN2 package for R'. Streams, observations, and prediction locations are represented as simple features and specific tools provided to define topological relationships between features; calculate the hydrologic distances (with flow-direction preserved) and the spatial additive function used to weight converging stream segments; and export the topological, spatial, and attribute information to an `SSN` (spatial stream network) object, which can be efficiently stored, accessed and analysed in R'. A detailed description of methods used to calculate and format the spatial data can be found in Peterson, E.E. and Ver Hoef, J.M., (2014) <doi:10.18637/jss.v056.i02>.

r-solar 0.47
Propagated dependencies: r-zoo@1.8-14 r-rcolorbrewer@1.1-3 r-latticeextra@0.6-31 r-lattice@0.22-7
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://oscarperpinan.codeberg.page/solar/
Licenses: GPL 3
Build system: r
Synopsis: Radiation and Photovoltaic Systems
Description:

Calculation methods of solar radiation and performance of photovoltaic systems from daily and intradaily irradiation data sources.

r-stagedtrees 2.3.0
Propagated dependencies: r-rlang@1.1.6 r-matrixstats@1.5.0 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/stagedtrees/stagedtrees
Licenses: Expat
Build system: r
Synopsis: Staged Event Trees
Description:

This package creates and fits staged event tree probability models, which are probabilistic graphical models capable of representing asymmetric conditional independence statements for categorical variables. Includes functions to create, plot and fit staged event trees from data, as well as many efficient structure learning algorithms. References: Carli F, Leonelli M, Riccomagno E, Varando G (2022). <doi: 10.18637/jss.v102.i06>. Collazo R. A., Görgen C. and Smith J. Q. (2018, ISBN:9781498729604). Görgen C., Bigatti A., Riccomagno E. and Smith J. Q. (2018) <arXiv:1705.09457>. Thwaites P. A., Smith, J. Q. (2017) <arXiv:1510.00186>. Barclay L. M., Hutton J. L. and Smith J. Q. (2013) <doi:10.1016/j.ijar.2013.05.006>. Smith J. Q. and Anderson P. E. (2008) <doi:10.1016/j.artint.2007.05.004>.

r-snowboot 1.0.2
Propagated dependencies: r-rdpack@2.6.4 r-rcpp@1.1.0 r-igraph@2.2.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=snowboot
Licenses: GPL 3
Build system: r
Synopsis: Bootstrap Methods for Network Inference
Description:

This package provides functions for analysis of network objects, which are imported or simulated by the package. The non-parametric methods of analysis center on snowball and bootstrap sampling for estimating functions of network degree distribution. For other parameters of interest, see, e.g., bootnet package.

r-spfda 0.9.2
Propagated dependencies: r-mathjaxr@1.8-0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/dipterix/spfda
Licenses: Expat
Build system: r
Synopsis: Function-on-Scalar Regression with Group-Bridge Penalty
Description:

This package implements a group-bridge penalized function-on-scalar regression model proposed by Wang et al. (2023) <doi:10.1111/biom.13684>, to simultaneously estimate functional coefficient and recover the local sparsity.

r-slideview 0.2.1
Propagated dependencies: r-viridislite@0.4.2 r-terra@1.8-86 r-raster@3.6-32 r-lattice@0.22-7 r-htmlwidgets@1.6.4 r-htmltools@0.5.8.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://r-spatial.github.io/slideview/
Licenses: Expat
Build system: r
Synopsis: Compare Raster Images Side by Side with a Slider
Description:

Create a side-by-side view of raster(image)s with an interactive slider to switch between regions of the images. This can be especially useful for image comparison of the same region at different time stamps.

r-sparsechol 0.3.2
Propagated dependencies: r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-matrix@1.7-4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/samuel-watson/SparseChol
Licenses: GPL 2+
Build system: r
Synopsis: Sparse Matrix C++ Classes Including Sparse Cholesky LDL Decomposition of Symmetric Matrices
Description:

C++ classes for sparse matrix methods including implementation of sparse LDL decomposition of symmetric matrices and solvers described by Timothy A. Davis (2016) <https://fossies.org/linux/SuiteSparse/LDL/Doc/ldl_userguide.pdf>. Provides a set of C++ classes for basic sparse matrix specification and linear algebra, and a class to implement sparse LDL decomposition and solvers. See <https://github.com/samuel-watson/SparseChol> for details.

r-spnetwork 0.4.4.7
Propagated dependencies: r-spdep@1.4-1 r-sfheaders@0.4.5 r-sf@1.0-23 r-rdpack@2.6.4 r-rcppprogress@0.4.2 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-progressr@0.18.0 r-igraph@2.2.1 r-ggplot2@4.0.1 r-future-apply@1.20.0 r-dbscan@1.2.3 r-data-table@1.17.8 r-cubature@2.1.4-1 r-cpprouting@3.2 r-bh@1.87.0-1 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://jeremygelb.github.io/spNetwork/
Licenses: GPL 2
Build system: r
Synopsis: Spatial Analysis on Network
Description:

Perform spatial analysis on network. Implement several methods for spatial analysis on network: Network Kernel Density estimation, building of spatial matrices based on network distance ('listw objects from spdep package), K functions estimation for point pattern analysis on network, k nearest neighbours on network, reachable area calculation, and graph generation References: Okabe et al (2019) <doi:10.1080/13658810802475491>; Okabe et al (2012, ISBN:978-0470770818);Baddeley et al (2015, ISBN:9781482210200).

r-serp 0.2.5
Propagated dependencies: r-ordinal@2023.12-4.1 r-crayon@1.5.3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/ejikeugba/serp
Licenses: GPL 2
Build system: r
Synopsis: Smooth Effects on Response Penalty for CLM
Description:

This package implements a regularization method for cumulative link models using the Smooth-Effect-on-Response Penalty (SERP). This method allows flexible modeling of ordinal data by enabling a smooth transition from a general cumulative link model to a simplified version of the same model. As the tuning parameter increases from zero to infinity, the subject-specific effects for each variable converge to a single global effect. The approach addresses common issues in cumulative link models, such as parameter unidentifiability and numerical instability, by maximizing a penalized log-likelihood instead of the standard non-penalized version. Fitting is performed using a modified Newton's method. Additionally, the package includes various model performance metrics and descriptive tools. For details on the implemented penalty method, see Ugba (2021) <doi:10.21105/joss.03705> and Ugba et al. (2021) <doi:10.3390/stats4030037>.

r-ssanv 1.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=ssanv
Licenses: GPL 3
Build system: r
Synopsis: Sample Size Adjusted for Nonadherence or Variability of Input Parameters
Description:

This package provides a set of functions to calculate sample size for two-sample difference in means tests. Does adjustments for either nonadherence or variability that comes from using data to estimate parameters.

r-statgensta 1.0.15
Propagated dependencies: r-xtable@1.8-4 r-spats@1.0-19 r-scales@1.4.0 r-rlang@1.1.6 r-qtl@1.72 r-maps@3.4.3 r-mapproj@1.2.12 r-lme4@1.1-37 r-knitr@1.50 r-gridextra@2.3 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-emmeans@2.0.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://biometris.github.io/statgenSTA/index.html
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Single Trial Analysis (STA) of Field Trials
Description:

Phenotypic analysis of field trials using mixed models with and without spatial components. One of a series of statistical genetic packages for streamlining the analysis of typical plant breeding experiments developed by Biometris. Some functions have been created to be used in conjunction with the R package asreml for the ASReml software, which can be obtained upon purchase from VSN international (<https://vsni.co.uk/software/asreml-r/>).

r-splinecox 0.0.8
Propagated dependencies: r-joint-cox@3.16
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=splineCox
Licenses: GPL 3+
Build system: r
Synopsis: Two-Stage Estimation Approach to Cox Regression Using M-Spline Function
Description:

This package implements a two-stage estimation approach for Cox regression using five-parameter M-spline functions to model the baseline hazard. It allows for flexible hazard shapes and model selection based on log-likelihood criteria as described in Teranishi et al.(2025). In addition, the package provides functions for constructing and evaluating B-spline copulas based on five M-spline or I-spline basis functions, allowing users to flexibly model and compute bivariate dependence structures. Both the copula function and its density can be evaluated. Furthermore, the package supports computation of dependence measures such as Kendall's tau and Spearman's rho, derived analytically from the copula parameters.

r-stepwisetest 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=StepwiseTest
Licenses: GPL 2+
Build system: r
Synopsis: Multiple Testing Method to Control Generalized Family-Wise Error Rate and False Discovery Proportion
Description:

Collection of stepwise procedures to conduct multiple hypotheses testing. The details of the stepwise algorithm can be found in Romano and Wolf (2007) <DOI:10.1214/009053606000001622> and Hsu, Kuan, and Yen (2014) <DOI:10.1093/jjfinec/nbu014>.

r-spemd 0.1-1
Propagated dependencies: r-spdep@1.4-1 r-sp@2.2-0 r-mba@0.1-2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/pierreroudier/spemd
Licenses: GPL 3
Build system: r
Synopsis: Bi-Dimensional Implementation of the Empirical Mode Decomposition for Spatial Data
Description:

This implementation of the Empirical Mode Decomposition (EMD) works in 2 dimensions simultaneously, and can be applied on spatial data. It can handle both gridded or un-gridded datasets.

r-spm2 1.1.3
Propagated dependencies: r-spm@1.2.3 r-sp@2.2-0 r-randomforest@4.7-1.2 r-nlme@3.1-168 r-gstat@2.1-4 r-glmnet@4.1-10 r-gbm@2.2.2 r-fields@17.1 r-e1071@1.7-16
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=spm2
Licenses: GPL 2+
Build system: r
Synopsis: Spatial Predictive Modeling
Description:

An updated and extended version of spm package, by introducing some further novel functions for modern statistical methods (i.e., generalised linear models, glmnet, generalised least squares), thin plate splines, support vector machine, kriging methods (i.e., simple kriging, universal kriging, block kriging, kriging with an external drift), and novel hybrid methods (228 hybrids plus numerous variants) of modern statistical methods or machine learning methods with mathematical and/or univariate geostatistical methods for spatial predictive modelling. For each method, two functions are provided, with one function for assessing the predictive errors and accuracy of the method based on cross-validation, and the other for generating spatial predictions. It also contains a couple of functions for data preparation and predictive accuracy assessment.

r-shrink 1.2.3
Propagated dependencies: r-survival@3.8-3 r-rms@8.1-0 r-mfp@1.5.5.1 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/biometrician/shrink
Licenses: GPL 3
Build system: r
Synopsis: Global, Parameterwise and Joint Shrinkage Factor Estimation
Description:

The predictive value of a statistical model can often be improved by applying shrinkage methods. This can be achieved, e.g., by regularized regression or empirical Bayes approaches. Various types of shrinkage factors can also be estimated after a maximum likelihood. While global shrinkage modifies all regression coefficients by the same factor, parameterwise shrinkage factors differ between regression coefficients. With variables which are either highly correlated or associated with regard to contents, such as several columns of a design matrix describing a nonlinear effect, parameterwise shrinkage factors are not interpretable and a compromise between global and parameterwise shrinkage, termed joint shrinkage', is a useful extension. A computational shortcut to resampling-based shrinkage factor estimation based on DFBETA residuals can be applied. Global, parameterwise and joint shrinkage for models fitted by lm(), glm(), coxph(), or mfp() is available.

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