_            _    _        _         _
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      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
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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-stockanalyst 1.0.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=stockAnalyst
Licenses: GPL 3
Build system: r
Synopsis: Equity Valuation using Methods of Fundamental Analysis
Description:

This package provides methods of Fundamental Analysis for Valuation of Equity included here serve as a quick reference for undergraduate courses on Stock Valuation and Chartered Financial Analyst Levels 1 and 2 Readings on Equity Valuation. Jerald E. Pinto (â Equity Asset Valuation (4th Edition)â , 2020, ISBN: 9781119628194). Chartered Financial Analyst Institute ("Chartered Financial Analyst Program Curriculum 2020 Level I Volumes 1-6. (Vol. 4, pp. 445-491)", 2019, ISBN: 9781119593577). Chartered Financial Analyst Institute ("Chartered Financial Analyst Program Curriculum 2020 Level II Volumes 1-6. (Vol. 4, pp. 197-447)", 2019, ISBN: 9781119593614).

r-smoothic 1.2.1
Propagated dependencies: r-toordinal@1.4-0.0 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-numderiv@2016.8-1.1 r-mass@7.3-65 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://meadhbh-oneill.github.io/smoothic/
Licenses: GPL 3
Build system: r
Synopsis: Variable Selection Using a Smooth Information Criterion
Description:

Implementation of the SIC epsilon-telescope method, either using single or distributional (multiparameter) regression. Includes classical regression with normally distributed errors and robust regression, where the errors are from the Laplace distribution. The "smooth generalized normal distribution" is used, where the estimation of an additional shape parameter allows the user to move smoothly between both types of regression. See O'Neill and Burke (2022) "Robust Distributional Regression with Automatic Variable Selection" for more details. <doi:10.48550/arXiv.2212.07317>. This package also contains the data analyses from O'Neill and Burke (2023). "Variable selection using a smooth information criterion for distributional regression models". <doi:10.1007/s11222-023-10204-8>.

r-shiny-semantic 0.5.1
Propagated dependencies: r-shiny@1.11.1 r-semantic-assets@1.1.0 r-r6@2.6.1 r-purrr@1.2.0 r-magrittr@2.0.4 r-jsonlite@2.0.0 r-htmlwidgets@1.6.4 r-htmltools@0.5.8.1 r-glue@1.8.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://appsilon.github.io/shiny.semantic/
Licenses: Expat
Build system: r
Synopsis: Semantic UI Support for Shiny
Description:

Creating a great user interface for your Shiny apps can be a hassle, especially if you want to work purely in R and don't want to use, for instance HTML templates. This package adds support for a powerful UI library Fomantic UI - <https://fomantic-ui.com/> (before Semantic). It also supports universal UI input binding that works with various DOM elements.

r-seqhmm 2.1.0
Propagated dependencies: r-traminer@2.2-13 r-rlang@1.1.6 r-rcpphungarian@0.3 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-progressr@0.18.0 r-patchwork@1.3.2 r-numderiv@2016.8-1.1 r-nloptr@2.2.1 r-matrix@1.7-4 r-lhs@1.2.0 r-igraph@2.2.1 r-gridbase@0.4-7 r-ggseqplot@0.8.9 r-ggplot2@4.0.1 r-future-apply@1.20.0 r-data-table@1.17.8 r-collapse@2.1.5 r-cli@3.6.5 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=seqHMM
Licenses: GPL 2+
Build system: r
Synopsis: Mixture Hidden Markov Models for Social Sequence Data and Other Multivariate, Multichannel Categorical Time Series
Description:

Designed for estimating variants of hidden (latent) Markov models (HMMs), mixture HMMs, and non-homogeneous HMMs (NHMMs) for social sequence data and other categorical time series. Special cases include feedback-augmented NHMMs, Markov models without latent layer, mixture Markov models, and latent class models. The package supports models for one or multiple subjects with one or multiple parallel sequences (channels). External covariates can be added to explain cluster membership in mixture models as well as initial, transition and emission probabilities in NHMMs. The package provides functions for evaluating and comparing models, as well as functions for visualizing of multichannel sequence data and HMMs. For NHMMs, methods for computing average causal effects and marginal state and emission probabilities are available. Models are estimated using maximum likelihood via the EM algorithm or direct numerical maximization with analytical gradients. Documentation is available via several vignettes, and Helske and Helske (2019, <doi:10.18637/jss.v088.i03>). For methodology behind the NHMMs, see Helske (2025, <doi:10.48550/arXiv.2503.16014>).

r-sipetool 0.1.0
Propagated dependencies: r-convolutioner@0.1.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SIPETool
Licenses: GPL 3
Build system: r
Synopsis: SIFT-MS and CPET Data Processor
Description:

Processor for selected ion flow tube mass spectrometer (SIFT-MS) output file from breath analysis. It allows the filtering of the SIFT output file (i.e., variation over time of the target analyte concentration) and the following analysis for the determination of: maximum, average, and standard deviation value of target concentration measured at each exhalation, and the respiratory rate over the measurement. Additionally, it is possible to align the SIFT-MS data with other on-line techniques such as cardio pulmonary exercise test (CPET) for a comprehensive characterization of breath samples.

r-simts 0.2.3
Propagated dependencies: r-tidyr@1.3.1 r-scales@1.4.0 r-robcor@0.1-6.1 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-purrr@1.2.0 r-magrittr@2.0.4 r-dplyr@1.1.4 r-broom@1.0.10
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/SMAC-Group/simts
Licenses: AGPL 3 FSDG-compatible
Build system: r
Synopsis: Time Series Analysis Tools
Description:

This package provides a system contains easy-to-use tools as a support for time series analysis courses. In particular, it incorporates a technique called Generalized Method of Wavelet Moments (GMWM) as well as its robust implementation for fast and robust parameter estimation of time series models which is described, for example, in Guerrier et al. (2013) <doi: 10.1080/01621459.2013.799920>. More details can also be found in the paper linked to via the URL below.

r-supercells 1.0.0
Propagated dependencies: r-terra@1.8-86 r-sf@1.0-23 r-philentropy@0.10.0 r-future-apply@1.20.0 r-cpp11@0.5.2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://jakubnowosad.com/supercells/
Licenses: GPL 3+
Build system: r
Synopsis: Superpixels of Spatial Data
Description:

This package creates superpixels based on input spatial data. This package works on spatial data with one variable (e.g., continuous raster), many variables (e.g., RGB rasters), and spatial patterns (e.g., areas in categorical rasters). It is based on the SLIC algorithm (Achanta et al. (2012) <doi:10.1109/TPAMI.2012.120>), and readapts it to work with arbitrary dissimilarity measures.

r-statebins 1.4.0
Propagated dependencies: r-scales@1.4.0 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://gitlab.com/hrbrmstr/statebins
Licenses: Expat
Build system: r
Synopsis: Create United States Uniform Cartogram Heatmaps
Description:

The cartogram heatmaps generated by the included methods are an alternative to choropleth maps for the United States and are based on work by the Washington Post graphics department in their report on "The states most threatened by trade" (<http://www.washingtonpost.com/wp-srv/special/business/states-most-threatened-by-trade/>). "State bins" preserve as much of the geographic placement of the states as possible but have the look and feel of a traditional heatmap. Functions are provided that allow for use of a binned, discrete scale, a continuous scale or manually specified colors depending on what is needed for the underlying data.

r-spidr 1.0.2
Propagated dependencies: r-rworldxtra@1.01 r-rworldmap@1.3-8 r-rgbif@3.8.4 r-jsonlite@2.0.0 r-httr@1.4.7
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=spidR
Licenses: GPL 3
Build system: r
Synopsis: Spider Knowledge Online
Description:

Allows the user to connect with the World Spider Catalogue (WSC; <https://wsc.nmbe.ch/>) and the World Spider Trait (WST; <https://spidertraits.sci.muni.cz/>) databases. Also performs several basic functions such as checking names validity, retrieving coordinate data from the Global Biodiversity Information Facility (GBIF; <https://www.gbif.org/>), and mapping.

r-smirnov 1.0-1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=smirnov
Licenses: GPL 2+
Build system: r
Synopsis: Provides two taxonomic coefficients from E. S. Smirnov "Taxonomic analysis" (1969) book
Description:

This tiny package contains one function smirnov() which calculates two scaled taxonomic coefficients, Txy (coefficient of similarity) and Txx (coefficient of originality). These two characteristics may be used for the analysis of similarities between any number of taxonomic groups, and also for assessing uniqueness of giving taxon. It is possible to use smirnov() output as a distance measure: convert it to distance by "as.dist(1 - smirnov(x))".

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-sunburstr 2.1.8
Propagated dependencies: r-htmlwidgets@1.6.4 r-htmltools@0.5.8.1 r-dplyr@1.1.4 r-d3r@1.1.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/timelyportfolio/sunburstR
Licenses: Expat
Build system: r
Synopsis: Sunburst 'Htmlwidget'
Description:

Make interactive d3.js sequence sunburst diagrams in R with the convenience and infrastructure of an htmlwidget'.

r-sicegar 0.3.0
Propagated dependencies: r-minpack-lm@1.2-4 r-ggplot2@4.0.1 r-fbasics@4041.97 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/wilkelab/sicegar/
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: Analysis of Single-Cell Viral Growth Curves
Description:

Aims to quantify time intensity data by using sigmoidal and double sigmoidal curves. It fits straight lines, sigmoidal, and double sigmoidal curves on to time vs intensity data. Then all the fits are used to make decision on which model best describes the data. This method was first developed in the context of single-cell viral growth analysis (for details, see Caglar et al. (2018) <doi:10.7717/peerj.4251>), and the package name stands for "SIngle CEll Growth Analysis in R".

r-sads 0.6.5
Propagated dependencies: r-vgam@1.1-13 r-powerlaw@1.0.0 r-poilog@0.4.2.1 r-mass@7.3-65 r-guilds@1.4.7 r-bbmle@1.0.25.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/piLaboratory/sads
Licenses: GPL 2
Build system: r
Synopsis: Maximum Likelihood Models for Species Abundance Distributions
Description:

Maximum likelihood tools to fit and compare models of species abundance distributions and of species rank-abundance distributions.

r-sopie 1.6
Propagated dependencies: r-circular@0.5-2 r-adgoftest@0.3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SOPIE
Licenses: GPL 3
Build system: r
Synopsis: Non-Parametric Estimation of the Off-Pulse Interval of a Pulsar
Description:

This package provides functions to non-parametrically estimate the off-pulse interval of a source function originating from a pulsar. The technique is based on a sequential application of P-values obtained from goodness-of-fit tests for the uniform distribution, such as the Kolmogorov-Smirnov, Cramer-von Mises, Anderson-Darling and Rayleigh goodness-of-fit tests.

r-stdmod 0.2.12
Propagated dependencies: r-rlang@1.1.6 r-manymome@0.3.3 r-lavaan@0.6-20 r-ggplot2@4.0.1 r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://sfcheung.github.io/stdmod/
Licenses: GPL 3
Build system: r
Synopsis: Standardized Moderation Effect and Its Confidence Interval
Description:

This package provides functions for computing a standardized moderation effect in moderated regression and forming its confidence interval by nonparametric bootstrapping as proposed in Cheung, Cheung, Lau, Hui, and Vong (2022) <doi:10.1037/hea0001188>. Also includes simple-to-use functions for computing conditional effects (unstandardized or standardized) and plotting moderation effects.

r-serieslcb 0.4.0
Propagated dependencies: r-shiny@1.11.1 r-gplots@3.2.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=serieslcb
Licenses: GPL 3
Build system: r
Synopsis: Lower Confidence Bounds for Binomial Series System
Description:

Calculate and compare lower confidence bounds for binomial series system reliability. The R shiny application, launched by the function launch_app(), weaves together a workflow of customized simulations and delta coverage calculations to output recommended lower confidence bound methods.

r-stablespec 0.3.0
Propagated dependencies: r-sem@3.1-16 r-rgraphviz@2.54.0 r-polycor@0.8-1 r-nsga2r@1.1 r-matrixcalc@1.0-6 r-graph@1.88.0 r-ggm@2.5.2 r-foreach@1.5.2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/rahmarid/stablespec
Licenses: Expat
Build system: r
Synopsis: Stable Specification Search in Structural Equation Models
Description:

An exploratory and heuristic approach for specification search in Structural Equation Modeling. The basic idea is to subsample the original data and then search for optimal models on each subset. Optimality is defined through two objectives: model fit and parsimony. As these objectives are conflicting, we apply a multi-objective optimization methods, specifically NSGA-II, to obtain optimal models for the whole range of model complexities. From these optimal models, we consider only the relevant model specifications (structures), i.e., those that are both stable (occur frequently) and parsimonious and use those to infer a causal model.

r-sdpdmod 0.0.7
Propagated dependencies: r-spdep@1.4-1 r-sp@2.2-0 r-sf@1.0-23 r-rspectra@0.16-2 r-plm@2.6-7 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=SDPDmod
Licenses: GPL 3+
Build system: r
Synopsis: Spatial Dynamic Panel Data Modeling
Description:

Spatial model calculation for static and dynamic panel data models, weights matrix creation and Bayesian model comparison. Bayesian model comparison methods were described by LeSage (2014) <doi:10.1016/j.spasta.2014.02.002>. The Lee'-'Yu transformation approach is described in Yu', De Jong and Lee (2008) <doi:10.1016/j.jeconom.2008.08.002>, Lee and Yu (2010) <doi:10.1016/j.jeconom.2009.08.001> and Lee and Yu (2010) <doi:10.1017/S0266466609100099>.

r-simcross 0.6
Propagated dependencies: r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://kbroman.org/simcross/
Licenses: GPL 3
Build system: r
Synopsis: Simulate Experimental Crosses
Description:

Simulate and plot general experimental crosses. The focus is on simulating genotypes with an aim towards flexibility rather than speed. Meiosis is simulated following the Stahl model, in which chiasma locations are the superposition of two processes: a proportion p coming from a process exhibiting no interference, and the remainder coming from a process following the chi-square model.

r-spdates 1.1
Propagated dependencies: r-viridislite@0.4.2 r-sp@2.2-0 r-smatr@3.4-8 r-rlang@1.1.6 r-rcarbon@1.5.2 r-raster@3.6-32 r-magrittr@2.0.4 r-gstat@2.1-4 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=spDates
Licenses: Expat
Build system: r
Synopsis: Analysis of Spatial Gradients in Radiocarbon Dates
Description:

Inspired by space-time regressions often performed to assess the expansion of the Neolithic from the Near East to Europe (Pinhasi et al. 2005 <doi:10.1371/journal.pbio.0030410>). Test for significant correlations between the (earliest) radiocarbon dates of archaeological sites and their respective distances from a hypothetical center of origin. Both ordinary least squares (OLS) and reduced major axis (RMA) methods are supported (Russell et al. 2014 <doi:10.1371/journal.pone.0087854>). It is also possible to iterate over many sites to identify the most likely origin.

r-symbolicda 0.7-3
Propagated dependencies: r-xml@3.99-0.20 r-shapes@1.2.8 r-rsda@3.2.5 r-e1071@1.7-16 r-clustersim@0.51-6 r-cluster@2.1.8.1 r-ade4@1.7-23
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=symbolicDA
Licenses: GPL 2+
Build system: r
Synopsis: Analysis of Symbolic Data
Description:

Symbolic data analysis methods: importing/exporting data from ASSO XML Files, distance calculation for symbolic data (Ichino-Yaguchi, de Carvalho measure), zoom star plot, 3d interval plot, multidimensional scaling for symbolic interval data, dynamic clustering based on distance matrix, HINoV method for symbolic data, Ichino's feature selection method, principal component analysis for symbolic interval data, decision trees for symbolic data based on optimal split with bagging, boosting and random forest approach (+visualization), kernel discriminant analysis for symbolic data, Kohonen's self-organizing maps for symbolic data, replication and profiling, artificial symbolic data generation. (Milligan, G.W., Cooper, M.C. (1985) <doi:10.1007/BF02294245>, Breiman, L. (1996), <doi:10.1007/BF00058655>, Hubert, L., Arabie, P. (1985), <doi:10.1007%2FBF01908075>, Ichino, M., & Yaguchi, H. (1994), <doi:10.1109/21.286391>, Rand, W.M. (1971) <doi:10.1080/01621459.1971.10482356>, Breckenridge, J.N. (2000) <doi:10.1207/S15327906MBR3502_5>, Groenen, P.J.F, Winsberg, S., Rodriguez, O., Diday, E. (2006) <doi:10.1016/j.csda.2006.04.003>, Dudek, A. (2007), <doi:10.1007/978-3-540-70981-7_4>).

r-scoper 1.4.0
Propagated dependencies: r-tidyr@1.3.1 r-stringi@1.8.7 r-shazam@1.3.1 r-scales@1.4.0 r-rlang@1.1.6 r-rcpp@1.1.0 r-ggplot2@4.0.1 r-foreach@1.5.2 r-fastcluster@1.3.0 r-dplyr@1.1.4 r-doparallel@1.0.17 r-data-table@1.17.8 r-alakazam@1.4.2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://scoper.readthedocs.io
Licenses: AGPL 3
Build system: r
Synopsis: Spectral Clustering-Based Method for Identifying B Cell Clones
Description:

This package provides a computational framework for identification of B cell clones from Adaptive Immune Receptor Repertoire sequencing (AIRR-Seq) data. Three main functions are included (identicalClones, hierarchicalClones, and spectralClones) that perform clustering among sequences of BCRs/IGs (B cell receptors/immunoglobulins) which share the same V gene, J gene and junction length. Nouri N and Kleinstein SH (2018) <doi: 10.1093/bioinformatics/bty235>. Nouri N and Kleinstein SH (2019) <doi: 10.1101/788620>. Gupta NT, et al. (2017) <doi: 10.4049/jimmunol.1601850>.

r-swdpwr 1.11
Propagated dependencies: r-spatstat-random@3.4-3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=swdpwr
Licenses: GPL 3
Build system: r
Synopsis: Power Calculation for Stepped Wedge Cluster Randomized Trials
Description:

To meet the needs of statistical power calculation for stepped wedge cluster randomized trials, we developed this software. Different parameters can be specified by users for different scenarios, including: cross-sectional and cohort designs, binary and continuous outcomes, marginal (GEE) and conditional models (mixed effects model), three link functions (identity, log, logit links), with and without time effects (the default specification assumes no-time-effect) under exchangeable, nested exchangeable and block exchangeable correlation structures. Unequal numbers of clusters per sequence are also allowed. The methods included in this package: Zhou et al. (2020) <doi:10.1093/biostatistics/kxy031>, Li et al. (2018) <doi:10.1111/biom.12918>. Supplementary documents can be found at: <https://ysph.yale.edu/cmips/research/software/study-design-power-calculation/swdpwr/>. The Shiny app for swdpwr can be accessed at: <https://jiachenchen322.shinyapps.io/swdpwr_shinyapp/>. The package also includes functions that perform calculations for the intra-cluster correlation coefficients based on the random effects variances as input variables for continuous and binary outcomes, respectively.

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