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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-simfastboin 1.3.2
Propagated dependencies: r-knitr@1.50 r-kableextra@1.4.0 r-iso@0.0-21
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/gosukehommaEX/simFastBOIN
Licenses: Expat
Build system: r
Synopsis: Fast Bayesian Optimal Interval Design for Phase I Dose-Finding Trials
Description:

Conducting Bayesian Optimal Interval (BOIN) design for phase I dose-finding trials. simFastBOIN provides functions for pre-computing decision tables, conducting trial simulations, and evaluating operating characteristics. The package uses vectorized operations and the Iso::pava() function for isotonic regression to achieve efficient performance while maintaining full compatibility with BOIN methodology. Version 1.3.2 adds p_saf and p_tox parameters for customizable safety and toxicity thresholds. Version 1.3.1 fixes Date field. Version 1.2.1 adds comprehensive roxygen2 documentation and enhanced print formatting with flexible table output options. Version 1.2.0 integrated C-based PAVA for isotonic regression. Version 1.1.0 introduced conservative MTD selection (boundMTD) and flexible early stopping rules (n_earlystop_rule). Methods are described in Liu and Yuan (2015) <doi:10.1111/rssc.12089>.

r-spantest 1.1-3
Propagated dependencies: r-rdpack@2.6.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/ArdiaD/spantest
Licenses: GPL 3
Build system: r
Synopsis: Mean-Variance Spanning Tests
Description:

This package provides a comprehensive suite of portfolio spanning tests for asset pricing, such as Huberman and Kandel (1987) <doi:10.1111/j.1540-6261.1987.tb03917.x>, Gibbons et al. (1989) <doi:10.2307/1913625>, Kempf and Memmel (2006) <doi:10.1007/BF03396737>, Pesaran and Yamagata (2024) <doi:10.1093/jjfinec/nbad002>, and Gungor and Luger (2016) <doi:10.1080/07350015.2015.1019510>.

r-string2adjmatrix 0.1.0
Propagated dependencies: r-stringr@1.6.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=String2AdjMatrix
Licenses: GPL 3
Build system: r
Synopsis: Creates an Adjacency Matrix from a List of Strings
Description:

Takes a list of character strings and forms an adjacency matrix for the times the specified characters appear together in the strings provided. For use in social network analysis and data wrangling. Simple package, comprised of three functions.

r-sociome 3.0.0
Propagated dependencies: r-tidyr@1.3.1 r-tidycensus@1.7.3 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-same 0.1.0
Propagated dependencies: r-survival@3.8-3 r-rjags@4-17 r-ggplot2@4.0.1 r-extradistr@1.10.0 r-expint@0.1-9 r-coda@0.19-4.1 r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SAME
Licenses: GPL 2
Build system: r
Synopsis: Seamless Adaptive Multi-Arm Multi-Stage Enrichment
Description:

Design a Bayesian seamless multi-arm biomarker-enriched phase II/III design with the survival endpoint with allowing sample size re-estimation. James M S Wason, Jean E Abraham, Richard D Baird, Ioannis Gournaris, Anne-Laure Vallier, James D Brenton, Helena M Earl, Adrian P Mander (2015) <doi:10.1038/bjc.2015.278>. Guosheng Yin, Nan Chen, J. Jack Lee (2018) <doi:10.1007/s12561-017-9199-7>. Ying Yuan, Beibei Guo, Mark Munsell, Karen Lu, Amir Jazaeri (2016) <doi:10.1002/sim.6971>.

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-scifigure 0.2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=scifigure
Licenses: Expat
Build system: r
Synopsis: Visualize 'Reproducibility' and 'Replicability' in a Comparison of Scientific Studies
Description:

Users may specify what fundamental qualities of a new study have or have not changed in an attempt to reproduce or replicate an original study. A comparison of the differences is visualized. Visualization approach follows Patil', Peng', and Leek (2016) <doi:10.1101/066803>.

r-sensitivityfull 1.5.6
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=sensitivityfull
Licenses: GPL 2
Build system: r
Synopsis: Sensitivity Analysis for Full Matching in Observational Studies
Description:

Sensitivity to unmeasured biases in an observational study that is a full match. Function senfm() performs tests and function senfmCI() creates confidence intervals. The method uses Huber's M-statistics, including least squares, and is described in Rosenbaum (2007, Biometrics) <DOI:10.1111/j.1541-0420.2006.00717.x>.

r-steppedpower 0.3.5
Propagated dependencies: r-rfast@2.1.5.2 r-plotly@4.11.0 r-matrix@1.7-4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SteppedPower
Licenses: Expat
Build system: r
Synopsis: Power Calculation for Stepped Wedge Designs
Description:

This package provides tools for power and sample size calculation as well as design diagnostics for longitudinal mixed model settings, with a focus on stepped wedge designs. All calculations are oracle estimates i.e. assume random effect variances to be known (or guessed) in advance. The method is introduced in Hussey and Hughes (2007) <doi:10.1016/j.cct.2006.05.007>, extensions are discussed in Li et al. (2020) <doi:10.1177/0962280220932962>.

r-semiartificial 2.4.1
Propagated dependencies: r-timedate@4051.111 r-statmatch@1.4.3 r-rsnns@0.4-18 r-robustbase@0.99-6 r-nnet@7.3-20 r-mcclust@1.0.1 r-mass@7.3-65 r-logspline@2.1.22 r-ks@1.15.1 r-fpc@2.2-13 r-flexclust@1.5.0 r-corelearn@1.57.3.1 r-cluster@2.1.8.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: http://lkm.fri.uni-lj.si/rmarko/software/
Licenses: GPL 3
Build system: r
Synopsis: Generator of Semi-Artificial Data
Description:

This package contains methods to generate and evaluate semi-artificial data sets. Based on a given data set different methods learn data properties using machine learning algorithms and generate new data with the same properties. The package currently includes the following data generators: i) a RBF network based generator using rbfDDA() from package RSNNS', ii) a Random Forest based generator for both classification and regression problems iii) a density forest based generator for unsupervised data Data evaluation support tools include: a) single attribute based statistical evaluation: mean, median, standard deviation, skewness, kurtosis, medcouple, L/RMC, KS test, Hellinger distance b) evaluation based on clustering using Adjusted Rand Index (ARI) and FM c) evaluation based on classification performance with various learning models, e.g., random forests.

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-sra 0.1.4.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/lerouzic/sra
Licenses: GPL 2
Build system: r
Synopsis: Selection Response Analysis
Description:

Artificial selection through selective breeding is an efficient way to induce changes in traits of interest in experimental populations. This package (sra) provides a set of tools to analyse artificial-selection response datasets. The data typically feature for several generations the average value of a trait in a population, the variance of the trait, the population size and the average value of the parents that were chosen to breed. Sra implements two families of models aiming at describing the dynamics of the genetic architecture of the trait during the selection response. The first family relies on purely descriptive (phenomenological) models, based on an autoregressive framework. The second family provides different mechanistic models, accounting e.g. for inbreeding, mutations, genetic and environmental canalization, or epistasis. The parameters underlying the dynamics of the time series are estimated by maximum likelihood. The sra package thus provides (i) a wrapper for the R functions mle() and optim() aiming at fitting in a convenient way a predetermined set of models, and (ii) some functions to plot and analyze the output of the models.

r-spectralclmixed 1.0.2
Propagated dependencies: r-rspectra@0.16-2 r-ggplot2@4.0.1 r-ggally@2.4.0 r-cluster@2.1.8.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SpectralClMixed
Licenses: GPL 2+
Build system: r
Synopsis: Spectral Clustering for Mixed Type Data
Description:

This package performs cluster analysis of mixed-type data using Spectral Clustering, see F. Mbuga and, C. Tortora (2022) <doi:10.3390/stats5010001>.

r-seqmade 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=SeqMADE
Licenses: GPL 2+
Build system: r
Synopsis: Network Module-Based Model in the Differential Expression Analysis for RNA-Seq
Description:

This package provides a network module-based generalized linear model for differential expression analysis with the count-based sequence data from RNA-Seq.

r-samplex 0.3.0
Propagated dependencies: r-shiny@1.11.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=samplex
Licenses: Expat
Build system: r
Synopsis: Shiny Tool for Sample Size Calculation
Description:

An interactive shiny application to assist in determining sample sizes for common survey designs such as simple random sampling', stratified sampling', and cluster sampling'. It includes formulas, helper calculators, and illustrative examples.

r-slgp 1.0.1
Propagated dependencies: 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-mvnfast@0.2.8 r-gofkernel@2.1-3 r-dicedesign@1.10 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SLGP
Licenses: GPL 3+
Build system: r
Synopsis: Spatial Logistic Gaussian Process for Field Density Estimation
Description:

This package provides tools for conditional and spatially dependent density estimation using Spatial Logistic Gaussian Processes (SLGPs). The approach represents probability densities through finite-rank Gaussian process priors transformed via a spatial logistic density transformation, enabling flexible non-parametric modeling of heterogeneous data. Functionality includes density prediction, quantile and moment estimation, sampling methods, and preprocessing routines for basis functions. Applications arise in spatial statistics, machine learning, and uncertainty quantification. The methodology builds on the framework of Leonard (1978) <doi:10.1111/j.2517-6161.1978.tb01655.x>, Lenk (1988) <doi:10.1080/01621459.1988.10478625>, Tokdar (2007) <doi:10.1198/106186007X210206>, Tokdar (2010) <doi:10.1214/10-BA605>, and is further aligned with recent developments in Bayesian non-parametric modelling: see Gautier (2023) <https://boristheses.unibe.ch/4377/>, and Gautier (2025) <doi:10.48550/arXiv.2110.02876>).

r-sms 2.3.1
Propagated dependencies: r-iterators@1.0.14 r-foreach@1.5.2 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=sms
Licenses: GPL 3
Build system: r
Synopsis: Spatial Microsimulation
Description:

Produce small area population estimates by fitting census data to survey data.

r-swarm 0.6.0
Propagated dependencies: r-splancs@2.01-45 r-mass@7.3-65 r-lubridate@1.9.4 r-geosphere@1.5-20
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://swarm-lab.github.io/swaRm/
Licenses: GPL 3
Build system: r
Synopsis: Processing Collective Movement Data
Description:

Function library for processing collective movement data (e.g. fish schools, ungulate herds, baboon troops) collected from GPS trackers or computer vision tracking software.

r-sdprism2d 0.1.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SDPrism2D
Licenses: GPL 3
Build system: r
Synopsis: Visualizing the Standard Deviation as the Size of a Prism
Description:

We visualize the standard deviation of a data set as the size of a prism whose volume equals the total volume of several prisms made from the Empirical Cumulative Distribution Function.

r-scquantum 1.0.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=scquantum
Licenses: GPL 2+
Build system: r
Synopsis: Estimate Ploidy and Absolute Copy Number from Single Cell Sequencing
Description:

Given bincount data from single-cell copy number profiling (segmented or unsegmented), estimates ploidy, and uses the ploidy estimate to scale the data to absolute copy numbers. Uses the modular quantogram proposed by Kendall (1986) <doi:10.1002/0471667196.ess2129.pub2>, modified by weighting segments according to confidence, and quantifying confidence in the estimate using a theoretical quantogram. Includes optional fused-lasso segmentation with the algorithm in Johnson (2013) <doi:10.1080/10618600.2012.681238>, using the implementation from glmgen by Arnold, Sadhanala, and Tibshirani.

r-sparsegfm 0.1.0
Propagated dependencies: r-mass@7.3-65 r-irlba@2.3.5.1 r-gfm@1.2.2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/zjwang1013/sparseGFM
Licenses: GPL 3+
Build system: r
Synopsis: Sparse Generalized Factor Models with Multiple Penalty Functions
Description:

This package implements sparse generalized factor models (sparseGFM) for dimension reduction and variable selection in high-dimensional data with automatic adaptation to weak factor scenarios. The package supports multiple data types (continuous, count, binary) through generalized linear model frameworks and handles missing values automatically. It provides 12 different penalty functions including Least Absolute Shrinkage and Selection Operator (Lasso), adaptive Lasso, Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Penalty (MCP), group Lasso, and their adaptive versions for inducing row-wise sparsity in factor loadings. Key features include cross-validation for regularization parameter selection using Sparsity Information Criterion (SIC), automatic determination of the number of factors via multiple information criteria, and specialized algorithms for row-sparse loading structures. The methodology employs alternating minimization with Singular Value Decomposition (SVD)-based identifiability constraints and is particularly effective for high-dimensional applications in genomics, economics, and social sciences where interpretable sparse dimension reduction is crucial. For penalty functions, see Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>, Fan and Li (2001) <doi:10.1198/016214501753382273>, and Zhang (2010) <doi:10.1214/09-AOS729>.

r-shiva 1.0.2
Propagated dependencies: r-psych@2.5.6 r-phylolm@2.6.5 r-mass@7.3-65 r-igraph@2.2.1 r-glmnet@4.1-10 r-ape@5.8-1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=ShiVa
Licenses: GPL 3+
Build system: r
Synopsis: Detection of Evolutionary Shifts in Both Optimal Value and Variance
Description:

This package implements statistical methods for detecting evolutionary shifts in both the optimal trait value (mean) and evolutionary diffusion variance. The method uses an L1-penalized optimization framework to identify branches where shifts occur, and the shift magnitudes. It also supports the inclusion of measurement error. For more details, see Zhang, Ho, and Kenney (2023) <doi:10.48550/arXiv.2312.17480>.

r-spef 1.0.9
Propagated dependencies: r-survival@3.8-3 r-squarem@2021.1 r-sm@2.2-6.0 r-plyr@1.8.9 r-nleqslv@3.3.5 r-ggplot2@4.0.1 r-bb@2019.10-1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: http://github.com/stc04003/spef
Licenses: GPL 3+
Build system: r
Synopsis: Semiparametric Estimating Functions
Description:

This package provides functions for fitting semiparametric regression models for panel count survival data. An overview of the package can be found in Wang and Yan (2011) <doi:10.1016/j.cmpb.2010.10.005> and Chiou et al. (2018) <doi:10.1111/insr.12271>.

r-shinysir 0.1.2
Propagated dependencies: r-tidyr@1.3.1 r-shiny@1.11.1 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-desolve@1.40
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=shinySIR
Licenses: Expat
Build system: r
Synopsis: Interactive Plotting for Mathematical Models of Infectious Disease Spread
Description:

This package provides interactive plotting for mathematical models of infectious disease spread. Users can choose from a variety of common built-in ordinary differential equation (ODE) models (such as the SIR, SIRS, and SIS models), or create their own. This latter flexibility allows shinySIR to be applied to simple ODEs from any discipline. The package is a useful teaching tool as students can visualize how changing different parameters can impact model dynamics, with minimal knowledge of coding in R. The built-in models are inspired by those featured in Keeling and Rohani (2008) <doi:10.2307/j.ctvcm4gk0> and Bjornstad (2018) <doi:10.1007/978-3-319-97487-3>.

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