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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-facmodcs 1.0
Propagated dependencies: r-zoo@1.8-14 r-xts@0.14.1 r-tseries@0.10-58 r-sn@2.1.1 r-robustbase@0.99-6 r-robstattm@1.0.11 r-performanceanalytics@2.0.8 r-lattice@0.22-7 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://github.com/robustport/facmodCS
Licenses: GPL 2
Build system: r
Synopsis: Cross-Section Factor Models
Description:

Linear cross-section factor model fitting with least-squares and robust fitting the lmrobdetMM() function from RobStatTM'; related volatility, Value at Risk and Expected Shortfall risk and performance attribution (factor-contributed vs idiosyncratic returns); tabular displays of risk and performance reports; factor model Monte Carlo. The package authors would like to thank Chicago Research on Security Prices,LLC for the cross-section of about 300 CRSP stocks data (in the data.table object stocksCRSP', and S&P GLOBAL MARKET INTELLIGENCE for contributing 14 factor scores (a.k.a "alpha factors".and "factor exposures") fundamental data on the 300 companies in the data.table object factorSPGMI'. The stocksCRSP and factorsSPGMI data are not covered by the GPL-2 license, are not provided as open source of any kind, and they are not to be redistributed in any form.

r-funsurv 1.0.0
Propagated dependencies: r-reda@0.5.6 r-mfpca@1.3-11 r-matrix@1.7-4 r-mass@7.3-65 r-ggplot2@4.0.1 r-fundata@1.3-9
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://github.com/zifangkong/FunSurv
Licenses: Expat
Build system: r
Synopsis: Modeling Time-to-Event Data with Functional Predictors
Description:

This package provides a collection of methods for modeling time-to-event data using both functional and scalar predictors. It implements functional data analysis techniques for estimation and inference, allowing researchers to assess the impact of functional covariates on survival outcomes, including time-to-single event and recurrent event outcomes.

r-finna 0.1.1
Propagated dependencies: r-xml2@1.5.0 r-tibble@3.3.0 r-readr@2.1.6 r-purrr@1.2.0 r-progress@1.2.3 r-jsonlite@2.0.0 r-httr@1.4.7 r-glue@1.8.0 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-curl@7.0.0
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://ropengov.github.io/finna/
Licenses: FreeBSD
Build system: r
Synopsis: Access the 'Finna' API
Description:

This package provides functions to access and retrieve metadata from the Finna API <https://api.finna.fi/>, which aggregates content from Finnish archives, libraries, and museums.

r-falconx 0.2
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://cran.r-project.org/package=falconx
Licenses: GPL 2+
Build system: r
Synopsis: Finding Allele-Specific Copy Number in Whole-Exome Sequencing Data
Description:

This is a method for Allele-specific DNA Copy Number profiling for whole-Exome sequencing data. Given the allele-specific coverage and site biases at the variant loci, this program segments the genome into regions of homogeneous allele-specific copy number. It requires, as input, the read counts for each variant allele in a pair of case and control samples, as well as the site biases. For detection of somatic mutations, the case and control samples can be the tumor and normal sample from the same individual. The implemented method is based on the paper: Chen, H., Jiang, Y., Maxwell, K., Nathanson, K. and Zhang, N. (under review). Allele-specific copy number estimation by whole Exome sequencing.

r-fdapace 0.6.0
Propagated dependencies: r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-pracma@2.4.6 r-numderiv@2016.8-1.1 r-matrix@1.7-4 r-mass@7.3-65 r-hmisc@5.2-4
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://github.com/functionaldata/tPACE
Licenses: Modified BSD
Build system: r
Synopsis: Functional Data Analysis and Empirical Dynamics
Description:

This package provides a versatile package that provides implementation of various methods of Functional Data Analysis (FDA) and Empirical Dynamics. The core of this package is Functional Principal Component Analysis (FPCA), a key technique for functional data analysis, for sparsely or densely sampled random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm. This core algorithm yields covariance and mean functions, eigenfunctions and principal component (scores), for both functional data and derivatives, for both dense (functional) and sparse (longitudinal) sampling designs. For sparse designs, it provides fitted continuous trajectories with confidence bands, even for subjects with very few longitudinal observations. PACE is a viable and flexible alternative to random effects modeling of longitudinal data. There is also a Matlab version (PACE) that contains some methods not available on fdapace and vice versa. Updates to fdapace were supported by grants from NIH Echo and NSF DMS-1712864 and DMS-2014626. Please cite our package if you use it (You may run the command citation("fdapace") to get the citation format and bibtex entry). References: Wang, J.L., Chiou, J., Müller, H.G. (2016) <doi:10.1146/annurev-statistics-041715-033624>; Chen, K., Zhang, X., Petersen, A., Müller, H.G. (2017) <doi:10.1007/s12561-015-9137-5>.

r-fdesigns 1.2
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-mvtnorm@1.3-3 r-mvquad@1.0-8 r-matrix@1.7-4
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://cran.r-project.org/package=fdesigns
Licenses: GPL 2
Build system: r
Synopsis: Optimal Experimental Designs for Dynamic/Functional Models
Description:

Optimal experimental designs for functional linear and functional generalised linear models, for scalar responses and profile/dynamic factors. The designs are optimised using the coordinate exchange algorithm. The methods are discussed by Michaelides (2023) <https://eprints.soton.ac.uk/474982/1/Thesis_DamianosMichaelides_Final_pdfa_1_.pdf>.

r-fordm 1.0.1
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-rlang@1.1.6 r-plotly@4.11.0 r-magrittr@2.0.4 r-ggplot2@4.0.1 r-emoa@0.5-3 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://cran.r-project.org/package=FoRDM
Licenses: Expat
Build system: r
Synopsis: Forest Many-Objective Robust Decision Making ('FoRDM')
Description:

Forest Many-Objective Robust Decision Making ('FoRDM') is a R toolkit for supporting robust forest management under deep uncertainty. It provides a forestry-focused application of Many-Objective Robust Decision Making ('MORDM') to forest simulation outputs, enabling users to evaluate robustness using regret- and satisficing'-based measures. FoRDM identifies robust solutions, generates Pareto fronts, and offers interactive 2D, 3D, and parallel-coordinate visualizations.

r-fgeo-x 1.1.4
Propagated dependencies: r-memoise@2.0.1
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://github.com/forestgeo/fgeo.x
Licenses: GPL 3
Build system: r
Synopsis: Access Small ForestGEO Datasets For Examples
Description:

Access small example datasets from Luquillo, a ForestGEO site in Puerto Rico (<https://forestgeo.si.edu/sites/north-america/luquillo>).

r-fdacluster 0.4.2
Propagated dependencies: r-tibble@3.3.0 r-rlang@1.1.6 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-progressr@0.18.0 r-nloptr@2.2.1 r-lpsolve@5.6.23 r-ggplot2@4.0.1 r-future-apply@1.20.0 r-fdasrvf@2.4.2 r-dbscan@1.2.3 r-cluster@2.1.8.1 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://astamm.github.io/fdacluster/
Licenses: GPL 3+
Build system: r
Synopsis: Joint Clustering and Alignment of Functional Data
Description:

Implementations of the k-means, hierarchical agglomerative and DBSCAN clustering methods for functional data which allows for jointly aligning and clustering curves. It supports functional data defined on one-dimensional domains but possibly evaluating in multivariate codomains. It supports functional data defined in arrays but also via the fd and funData classes for functional data defined in the fda and funData packages respectively. It currently supports shift, dilation and affine warping functions for functional data defined on the real line and uses the SRVF framework to handle boundary-preserving warping for functional data defined on a specific interval. Main reference for the k-means algorithm: Sangalli L.M., Secchi P., Vantini S., Vitelli V. (2010) "k-mean alignment for curve clustering" <doi:10.1016/j.csda.2009.12.008>. Main reference for the SRVF framework: Tucker, J. D., Wu, W., & Srivastava, A. (2013) "Generative models for functional data using phase and amplitude separation" <doi:10.1016/j.csda.2012.12.001>.

r-finnet 0.2.1
Propagated dependencies: r-rcpp@1.1.0 r-matrix@1.7-4
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://fatelarico.github.io/FinNet.html
Licenses: GPL 3+
Build system: r
Synopsis: Quickly Build and Manipulate Financial Networks
Description:

Providing classes, methods, and functions to deal with financial networks. Users can easily store information about both physical and legal persons by using pre-made classes that are studied for integration with scraping packages such as rvest and RSelenium'. Moreover, the package assists in creating various types of financial networks depending on the type of relation between its units depending on the relation under scrutiny (ownership, board interlocks, etc.), the desired tie type (valued or binary), and renders them in the most common formats (adjacency matrix, incidence matrix, edge list, igraph', network'). There are also ad-hoc functions for the Fiedler value, global network efficiency, and cascade-failure analysis.

r-functansnp 0.1.0
Propagated dependencies: r-mass@7.3-65 r-lava@1.8.2 r-glmnet@4.1-10 r-fundata@1.3-9 r-fda@6.3.0 r-caret@7.0-1
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://cran.r-project.org/package=FunctanSNP
Licenses: GPL 2+
Build system: r
Synopsis: Functional Analysis (with Interactions) for Dense SNP Data
Description:

An implementation of revised functional regression models for multiple genetic variation data, such as single nucleotide polymorphism (SNP) data, which provides revised functional linear regression models, partially functional interaction regression analysis with penalty-based techniques and corresponding drawing functions, etc.(Ruzong Fan, Yifan Wang, James L. Mills, Alexander F. Wilson, Joan E. Bailey-Wilson, and Momiao Xiong (2013) <doi:10.1002/gepi.21757>).

r-flexsurvcure 1.3.3
Propagated dependencies: r-survival@3.8-3 r-flexsurv@2.3.2 r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://github.com/jrdnmdhl/flexsurvcure
Licenses: GPL 2+
Build system: r
Synopsis: Flexible Parametric Cure Models
Description:

Flexible parametric mixture and non-mixture cure models for time-to-event data.

r-factor-switching 1.4
Propagated dependencies: r-mcmcpack@1.7-1 r-lpsolve@5.6.23 r-hdinterval@0.2.4 r-coda@0.19-4.1
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://cran.r-project.org/package=factor.switching
Licenses: GPL 2
Build system: r
Synopsis: Post-Processing MCMC Outputs of Bayesian Factor Analytic Models
Description:

This package provides a well known identifiability issue in factor analytic models is the invariance with respect to orthogonal transformations. This problem burdens the inference under a Bayesian setup, where Markov chain Monte Carlo (MCMC) methods are used to generate samples from the posterior distribution. The package applies a series of rotation, sign and permutation transformations (Papastamoulis and Ntzoufras (2022) <DOI:10.1007/s11222-022-10084-4>) into raw MCMC samples of factor loadings, which are provided by the user. The post-processed output is identifiable and can be used for MCMC inference on any parametric function of factor loadings. Comparison of multiple MCMC chains is also possible.

r-fbglm 1.5.0
Propagated dependencies: r-pscl@1.5.9 r-frbinom@1.0.0 r-bbmle@1.0.25.1 r-agridat@1.26
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://cran.r-project.org/package=fbglm
Licenses: Expat
Build system: r
Synopsis: Fractional Binomial Regression Model
Description:

Fit a fractional binomial regression model and extended zero-inflated negative binomial regression model to count data with excess zeros using maximum likelihood estimation. Compare zero-inflated regression models via Vuong closeness test.

r-fbar 0.6.0
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-roi-plugin-ecos@1.0-2 r-roi@1.0-1 r-rlang@1.1.6 r-purrr@1.2.0 r-matrix@1.7-4 r-magrittr@2.0.4 r-dplyr@1.1.4 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: http://maxconway.github.io/fbar/
Licenses: GPL 3
Build system: r
Synopsis: An Extensible Approach to Flux Balance Analysis
Description:

This package provides a toolkit for Flux Balance Analysis and related metabolic modeling techniques. Functions are provided for: parsing models in tabular format, converting parsed metabolic models to input formats for common linear programming solvers, and evaluating and applying gene-protein-reaction mappings. In addition, there are wrappers to parse a model, select a solver, find the metabolic fluxes, and return the results applied to the original model. Compared to other packages in this field, this package puts a much heavier focus on providing reusable components that can be used in the design of new implementation of new techniques, in particular those that involve large parameter sweeps. For a background on the theory, see What is Flux Balance Analysis <doi:10.1038/nbt.1614>.

r-fastlogitme 0.1.0
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://cran.r-project.org/package=fastlogitME
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Basic Marginal Effects for Logit Models
Description:

Calculates marginal effects based on logistic model objects such as glm or speedglm at the average (default) or at given values using finite differences. It also returns confidence intervals for said marginal effects and the p-values, which can easily be used as input in stargazer. The function only returns the essentials and is therefore much faster but not as detailed as other functions available to calculate marginal effects. As a result, it is highly suitable for large datasets for which other packages may require too much time or calculating power.

r-facerec 0.1.0
Propagated dependencies: r-stringr@1.6.0 r-snakecase@0.11.1 r-rlang@1.1.6 r-magrittr@2.0.4 r-knitr@1.50 r-jsonlite@2.0.0 r-httr@1.4.7 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://github.com/methodds/facerec
Licenses: Expat
Build system: r
Synopsis: An Interface for Face Recognition
Description:

This package provides an interface to the Kairos Face Recognition API <https://kairos.com/face-recognition-api>. The API detects faces in images and returns estimates for demographics like gender, ethnicity and age.

r-forecastlsw 1.1.1
Propagated dependencies: r-wavethresh@4.7.3 r-lpacf@1.0.2 r-locits@1.7.8 r-forecast@8.24.0
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://cran.r-project.org/package=forecastLSW
Licenses: GPL 2
Build system: r
Synopsis: Forecasting Routines for Locally Stationary Wavelet Processes
Description:

Implementation to perform forecasting of locally stationary wavelet processes by examining the local second order structure of the time series.

r-fardeep 1.0.1
Propagated dependencies: r-preprocesscore@1.72.0 r-nnls@1.6
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://cran.r-project.org/package=FARDEEP
Licenses: Expat
Build system: r
Synopsis: Fast and Robust Deconvolution of Tumor Infiltrating Lymphocyte from Expression Profiles using Least Trimmed Squares
Description:

Using the idea of least trimmed square, it could automatically detects and removes outliers from data before estimating the coefficients. It is a robust machine learning tool which can be applied to gene-expression deconvolution technique. Yuning Hao, Ming Yan, Blake R. Heath, Yu L. Lei and Yuying Xie (2019) <doi:10.1101/358366>.

r-first 2.1
Propagated dependencies: r-twinning@1.1 r-fnn@1.1.4.1
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://cran.r-project.org/package=first
Licenses: GPL 2+
Build system: r
Synopsis: Factor Importance Ranking and Selection using Total Indices
Description:

This package provides a model-independent factor importance ranking and selection procedure based on total Sobol indices. Please see Huang and Joseph (2025) <doi:10.1080/00401706.2025.2483531>. This research is supported by U.S. National Science Foundation grants DMS-2310637 and DMREF-1921873.

r-fints 0.4-9
Propagated dependencies: r-zoo@1.8-14
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://geobosh.github.io/FinTSDoc/
Licenses: GPL 2+
Build system: r
Synopsis: Companion to Tsay (2005) Analysis of Financial Time Series
Description:

R companion to Tsay (2005) Analysis of Financial Time Series, second edition (Wiley). Includes data sets, functions and script files required to work some of the examples. Version 0.3-x includes R objects for all data files used in the text and script files to recreate most of the analyses in chapters 1-3 and 9 plus parts of chapters 4 and 11.

r-fuzzynumbers 0.4-7
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://github.com/gagolews/FuzzyNumbers/
Licenses: LGPL 3+
Build system: r
Synopsis: Tools to Deal with Fuzzy Numbers
Description:

S4 classes and methods to deal with fuzzy numbers. They allow for computing any arithmetic operations (e.g., by using the Zadeh extension principle), performing approximation of arbitrary fuzzy numbers by trapezoidal and piecewise linear ones, preparing plots for publications, computing possibility and necessity values for comparisons, etc.

r-forlion 0.3.0
Propagated dependencies: r-psych@2.5.6 r-cubature@2.1.4-1
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://cran.r-project.org/package=ForLion
Licenses: Expat
Build system: r
Synopsis: 'ForLion' Algorithm to Find D-Optimal Designs for Experiments
Description:

Designing experimental plans that involve both discrete and continuous factors with general parametric statistical models using the ForLion algorithm and EW ForLion algorithm. The algorithms will search for locally optimal designs and EW optimal designs under the D-criterion. Reference: Huang, Y., Li, K., Mandal, A., & Yang, J., (2024)<doi:10.1007/s11222-024-10465-x>.

r-fuser 1.0.1
Propagated dependencies: r-rspectra@0.16-2 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-matrix@1.7-4 r-irlba@2.3.5.1 r-glmnet@4.1-10
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://cran.r-project.org/package=fuser
Licenses: GPL 3
Build system: r
Synopsis: Fused Lasso for High-Dimensional Regression over Groups
Description:

Enables high-dimensional penalized regression across heterogeneous subgroups. Fusion penalties are used to share information about the linear parameters across subgroups. The underlying model is described in detail in Dondelinger and Mukherjee (2017) <arXiv:1611.00953>.

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