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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-fawr 1.2.0
Propagated dependencies: r-mass@7.3-65 r-lattice@0.22-7
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://cran.r-project.org/package=FAwR
Licenses: GPL 3
Build system: r
Synopsis: Functions and Datasets for "Forest Analytics with R"
Description:

This package provides functions and datasets from the book "Forest Analytics with R".

r-forestmangr 0.9.9
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-systemfit@1.1-30 r-shiny@1.11.1 r-scales@1.4.0 r-rlang@1.1.6 r-purrr@1.2.0 r-plyr@1.8.9 r-minpack-lm@1.2-4 r-miniui@0.1.2 r-magrittr@2.0.4 r-gridextra@2.3 r-ggthemes@5.1.0 r-ggpmisc@0.6.2 r-ggplot2@4.0.1 r-ggdendro@0.2.0 r-fincal@0.6.3 r-dplyr@1.1.4 r-car@3.1-3 r-broom@1.0.10
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://github.com/sollano/forestmangr#readme
Licenses: Expat
Build system: r
Synopsis: Forest Mensuration and Management
Description:

Processing forest inventory data with methods such as simple random sampling, stratified random sampling and systematic sampling. There are also functions for yield and growth predictions and model fitting, linear and nonlinear grouped data fitting, and statistical tests. References: Kershaw Jr., Ducey, Beers and Husch (2016). <doi:10.1002/9781118902028>.

r-foresterror 1.1.0
Propagated dependencies: r-purrr@1.2.0 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://cran.r-project.org/package=forestError
Licenses: GPL 3
Build system: r
Synopsis: Unified Framework for Random Forest Prediction Error Estimation
Description:

Estimates the conditional error distributions of random forest predictions and common parameters of those distributions, including conditional misclassification rates, conditional mean squared prediction errors, conditional biases, and conditional quantiles, by out-of-bag weighting of out-of-bag prediction errors as proposed by Lu and Hardin (2021). This package is compatible with several existing packages that implement random forests in R.

r-fastvoter 0.0.1
Propagated dependencies: r-rcpp@1.1.0 r-data-table@1.17.8 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://bblodfon.github.io/fastVoteR/
Licenses: LGPL 3+
Build system: r
Synopsis: Efficient Voting Methods for Committee Selection
Description:

This package provides a fast Rcpp'-based implementation of polynomially-computable voting theory methods for committee ranking and scoring. The package includes methods such as Approval Voting (AV), Satisfaction Approval Voting (SAV), sequential Proportional Approval Voting (PAV), and sequential Phragmen's Rule. Weighted variants of these methods are also provided, allowing for differential voter influence.

r-flowregenvcost 0.1.1
Propagated dependencies: r-zoo@1.8-14
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://github.com/garciadejalon/FlowRegEnvCost
Licenses: Expat
Build system: r
Synopsis: The Environmental Costs of Flow Regulation
Description:

An application to calculate the daily environmental costs of river flow regulation by dams based on Garcà a de Jalon et al. 2017 <doi:10.1007/s11269-017-1663-0>.

r-featureflag 0.2.0
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://github.com/szymanskir/featureflag
Licenses: Expat
Build system: r
Synopsis: Turn Features On and Off using Feature Flags
Description:

Feature flags allow developers to turn features of their software on and off in form of configuration. This package provides functions for creating feature flags in code. It exposes an interface for defining own feature flags which are enabled based on custom criteria.

r-fraction 1.1.1
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://cran.r-project.org/package=FRACTION
Licenses: GPL 2
Build system: r
Synopsis: Numeric Number into Fraction
Description:

Turn numeric,data.frame,matrix into fraction form.

r-finepop 1.5.2
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://cran.r-project.org/package=FinePop
Licenses: GPL 2+
Build system: r
Synopsis: Fine-Scale Population Analysis
Description:

Statistical tool set for population genetics. The package provides following functions: 1) empirical Bayes estimator of Fst and other measures of genetic differentiation, 2) regression analysis of environmental effects on genetic differentiation using bootstrap method, 3) interfaces to read and manipulate GENEPOP format data files and allele/haplotype frequency format files.

r-faulttree 1.0.1
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: http://www.openreliability.org/fault-tree-analysis-on-r/
Licenses: GPL 3+
Build system: r
Synopsis: Fault Trees for Risk and Reliability Analysis
Description:

Construction, calculation and display of fault trees. Methods derived from Clifton A. Ericson II (2005, ISBN: 9780471739425) <DOI:10.1002/0471739421>, Antoine Rauzy (1993) <DOI:10.1016/0951-8320(93)90060-C>, Tim Bedford and Roger Cooke (2012, ISBN: 9780511813597) <DOI:10.1017/CBO9780511813597>, Nikolaos Limnios, (2007, ISBN: 9780470612484) <DOI: 10.1002/9780470612484>.

r-finnts 0.6.0
Propagated dependencies: r-workflows@1.3.0 r-vroom@1.6.6 r-tune@2.0.1 r-timetk@2.9.1 r-tidyselect@1.2.1 r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-snakecase@0.11.1 r-rules@1.0.3 r-rsample@1.3.1 r-rlang@1.1.6 r-recipes@1.3.1 r-purrr@1.2.0 r-plyr@1.8.9 r-parsnip@1.3.3 r-modeltime@1.3.5 r-magrittr@2.0.4 r-lubridate@1.9.4 r-kernlab@0.9-33 r-hts@6.0.3 r-gtools@3.9.5 r-glue@1.8.0 r-glmnet@4.1-10 r-generics@0.1.4 r-fs@1.6.6 r-foreach@1.5.2 r-feasts@0.5.0 r-earth@5.3.4 r-dplyr@1.1.4 r-doparallel@1.0.17 r-digest@0.6.39 r-dials@1.4.2 r-cubist@0.5.1 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://microsoft.github.io/finnts/
Licenses: Expat
Build system: r
Synopsis: Microsoft Finance Time Series Forecasting Framework
Description:

Automated time series forecasting developed by Microsoft Finance. The Microsoft Finance Time Series Forecasting Framework, aka Finn, can be used to forecast any component of the income statement, balance sheet, or any other area of interest by finance. Any numerical quantity over time, Finn can be used to forecast it. While it can be applied outside of the finance domain, Finn was built to meet the needs of financial analysts to better forecast their businesses within a company, and has a lot of built in features that are specific to the needs of financial forecasters. Happy forecasting!

r-fastonlinecpt 1.0
Propagated dependencies: r-rdpack@2.6.4
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://cran.r-project.org/package=fastOnlineCpt
Licenses: GPL 2+
Build system: r
Synopsis: Online Multivariate Changepoint Detection
Description:

Implementation of a simple algorithm designed for online multivariate changepoint detection of a mean in sparse changepoint settings. The algorithm is based on a modified cusum statistic and guarantees control of the type I error on any false discoveries, while featuring O(1) time and O(1) memory updates per series as well as a proven detection delay.

r-frequentdirections 0.1.0
Propagated dependencies: r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://github.com/shinichi-takayanagi/frequentdirections
Licenses: Expat
Build system: r
Synopsis: Implementation of Frequent-Directions Algorithm for Efficient Matrix Sketching
Description:

Implement frequent-directions algorithm for efficient matrix sketching. (Edo Liberty (2013) <doi:10.1145/2487575.2487623>).

r-fproc 0.1.0
Dependencies: tbb@2021.6.0
Propagated dependencies: r-terra@1.8-86 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://luismurao.github.io/fpROC/
Licenses: GPL 3
Build system: r
Synopsis: Fast Partial Receiver Operating Characteristic (ROC) Test for Ecological Niche Modeling
Description:

This package provides optimized C++ code for computing the partial Receiver Operating Characteristic (ROC) test used in niche and species distribution modeling. The implementation follows Peterson et al. (2008) <doi:10.1016/j.ecolmodel.2007.11.008>. Parallelization via OpenMP was implemented with assistance from the DeepSeek Artificial Intelligence Assistant (<https://www.deepseek.com/>).

r-fluxfixer 1.0.0
Propagated dependencies: r-zoo@1.8-14 r-xts@0.14.1 r-tidyselect@1.2.1 r-tidyr@1.3.1 r-rlang@1.1.6 r-ranger@0.17.0 r-magrittr@2.0.4 r-lubridate@1.9.4 r-gsignal@0.3-7 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://github.com/yhata86/fluxfixer
Licenses: Expat
Build system: r
Synopsis: Advanced Framework for Sap Flow Data Post-Process
Description:

This package provides a flexible framework for post-processing thermal dissipation sap flow data using statistical methods and machine learning. This framework includes anomaly correction, outlier removal, gap-filling, trend removal, signal damping correction, and sap flux density calculation. The functions in this package can also apply to other time series with various artifacts.

r-fattailsr 2.0.0
Propagated dependencies: r-timeseries@4041.111 r-minpack-lm@1.2-4
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://www.inmodelia.com/fattailsr-en.html
Licenses: GPL 2
Build system: r
Synopsis: Kiener Distributions and Fat Tails in Finance and Neuroscience
Description:

Kiener distributions K1, K2, K3, K4 and K7 to characterize distributions with left and right, symmetric or asymmetric fat tails in finance, neuroscience and other disciplines. Two algorithms to estimate the distribution parameters, quantiles, value-at-risk and expected shortfall. IMPORTANT: Standardization has been changed in versions >= 2.0.0 to get sd = 1 when kappa = Inf rather than 2*pi/sqrt(3) in versions <= 1.8.6. This affects parameter g (other parameters stay unchanged). Do not update if you need consistent comparisons with previous results for the g parameter.

r-fgdir 0.1.0
Propagated dependencies: r-tidyr@1.3.1 r-refund@0.1-38 r-matrix@1.7-4 r-magrittr@2.0.4 r-ggplot2@4.0.1 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=fgdiR
Licenses: GPL 3
Build system: r
Synopsis: Functional Gait Deviation Index
Description:

This package provides a typical gait analysis requires the examination of the motion of nine joint angles on the left-hand side and six joint angles on the right-hand side across multiple subjects. Due to the quantity and complexity of the data, it is useful to calculate the amount by which a subjectâ s gait deviates from an average normal profile and to represent this deviation as a single number. Such a measure can quantify the overall severity of a condition affecting walking, monitor progress, or evaluate the outcome of an intervention prescribed to improve the gait pattern. This R package provides tools for computing the Functional Gait Deviation Index, a novel index for quantifying gait pathology using multivariate functional principal component analysis. The package supports analysis at the level of both legs combined, individual legs, and individual joints/planes. It includes functions for functional data preprocessing, multivariate functional principal component decomposition, FGDI computation, and visualisation of gait abnormality scores. Further details can be found in Minhas, S. K., Sangeux, M., Polak, J., & Carey, M. (2025). The Functional Gait Deviation Index. Journal of Applied Statistics <doi:10.1080/02664763.2025.2514150>.

r-fitlandr 0.1.1
Propagated dependencies: r-tidyr@1.3.1 r-sparsevfc@0.1.2 r-simlandr@0.4.0 r-rootsolve@1.8.2.4 r-rlang@1.1.6 r-rfast@2.1.5.2 r-r-utils@2.13.0 r-purrr@1.2.0 r-plotly@4.11.0 r-numderiv@2016.8-1.1 r-mass@7.3-65 r-magrittr@2.0.4 r-glue@1.8.0 r-ggplot2@4.0.1 r-future-apply@1.20.0 r-furrr@0.3.1 r-dplyr@1.1.4 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://sciurus365.github.io/fitlandr/
Licenses: GPL 3+
Build system: r
Synopsis: Fit Vector Fields and Potential Landscapes from Intensive Longitudinal Data
Description:

This package provides a toolbox for estimating vector fields from intensive longitudinal data, and construct potential landscapes thereafter. The vector fields can be estimated with two nonparametric methods: the Multivariate Vector Field Kernel Estimator (MVKE) by Bandi & Moloche (2018) <doi:10.1017/S0266466617000305> and the Sparse Vector Field Consensus (SparseVFC) algorithm by Ma et al. (2013) <doi:10.1016/j.patcog.2013.05.017>. The potential landscapes can be constructed with a simulation-based approach with the simlandr package (Cui et al., 2021) <doi:10.31234/osf.io/pzva3>, or the Bhattacharya et al. (2011) method for path integration <doi:10.1186/1752-0509-5-85>.

r-fica 1.1-3
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-jade@2.0-4
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://cran.r-project.org/package=fICA
Licenses: GPL 2+
Build system: r
Synopsis: Classical, Reloaded and Adaptive FastICA Algorithms
Description:

Algorithms for classical symmetric and deflation-based FastICA, reloaded deflation-based FastICA algorithm and an algorithm for adaptive deflation-based FastICA using multiple nonlinearities. For details, see Miettinen et al. (2014) <doi:10.1109/TSP.2014.2356442> and Miettinen et al. (2017) <doi:10.1016/j.sigpro.2016.08.028>. The package is described in Miettinen, Nordhausen and Taskinen (2018) <doi:10.32614/RJ-2018-046>.

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-fect 2.1.0
Propagated dependencies: r-scales@1.4.0 r-rlang@1.1.6 r-reshape2@1.4.5 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-parallelly@1.45.1 r-mvtnorm@1.3-3 r-mass@7.3-65 r-gridextra@2.3 r-ggplot2@4.0.1 r-ggally@2.4.0 r-future-apply@1.20.0 r-future@1.68.0 r-foreach@1.5.2 r-fixest@0.13.2 r-dplyr@1.1.4 r-dorng@1.8.6.2 r-doparallel@1.0.17 r-dofuture@1.1.2 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://yiqingxu.org/packages/fect/
Licenses: Expat
Build system: r
Synopsis: Fixed Effects Counterfactual Estimators
Description:

This package provides tools for estimating causal effects in panel data using counterfactual methods, as well as other modern DID estimators. It is designed for causal panel analysis with binary treatments under the parallel trends assumption. The package supports scenarios where treatments can switch on and off and allows for limited carryover effects. It includes several imputation estimators, such as Gsynth (Xu 2017), linear factor models, and the matrix completion method. Detailed methodology is described in Liu, Wang, and Xu (2024) <doi:10.48550/arXiv.2107.00856> and Chiu et al. (2025) <doi:10.48550/arXiv.2309.15983>. Optionally integrates with the "HonestDiDFEct" package for sensitivity analyses compatible with imputation estimators. "HonestDiDFEct" is not on CRAN but can be obtained from <https://github.com/lzy318/HonestDiDFEct>.

r-fitheavytail 0.2.0
Propagated dependencies: r-numderiv@2016.8-1.1 r-mvtnorm@1.3-3 r-icsnp@1.1-2 r-ghyp@1.6.5
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://CRAN.R-project.org/package=fitHeavyTail
Licenses: GPL 3
Build system: r
Synopsis: Mean and Covariance Matrix Estimation under Heavy Tails
Description:

Robust estimation methods for the mean vector, scatter matrix, and covariance matrix (if it exists) from data (possibly containing NAs) under multivariate heavy-tailed distributions such as angular Gaussian (via Tyler's method), Cauchy, and Student's t distributions. Additionally, a factor model structure can be specified for the covariance matrix. The latest revision also includes the multivariate skewed t distribution. The package is based on the papers: Sun, Babu, and Palomar (2014); Sun, Babu, and Palomar (2015); Liu and Rubin (1995); Zhou, Liu, Kumar, and Palomar (2019); Pascal, Ollila, and Palomar (2021).

r-fuzzyimputationtest 0.5.2
Propagated dependencies: r-vim@6.2.6 r-missforest@1.6.1 r-miceranger@1.5.0 r-mice@3.18.0 r-fuzzysimres@0.4.8 r-fuzzyresampling@0.6.4 r-fuzzynumbers@0.4-7
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://cran.r-project.org/package=FuzzyImputationTest
Licenses: GPL 3
Build system: r
Synopsis: Imputation Procedures and Quality Tests for Fuzzy Data
Description:

Special procedures for the imputation of missing fuzzy numbers are still underdeveloped. The goal of the package is to provide the new d-imputation method (DIMP for short, Romaniuk, M. and Grzegorzewski, P. (2023) "Fuzzy Data Imputation with DIMP and FGAIN" RB/23/2023) and covert some classical ones applied in R packages ('missForest','miceRanger','knn') for use with fuzzy datasets. Additionally, specially tailored benchmarking tests are provided to check and compare these imputation procedures with fuzzy datasets.

r-fxtwapls 0.1.3
Propagated dependencies: r-progressr@0.18.0 r-mass@7.3-65 r-jops@0.2.0 r-ggplot2@4.0.1 r-geosphere@1.5-20 r-future@1.68.0 r-foreach@1.5.2 r-dofuture@1.1.2
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://github.com/special-uor/fxTWAPLS/
Licenses: GPL 3
Build system: r
Synopsis: An Improved Version of WA-PLS
Description:

The goal of this package is to provide an improved version of WA-PLS (Weighted Averaging Partial Least Squares) by including the tolerances of taxa and the frequency of the sampled climate variable. This package also provides a way of leave-out cross-validation that removes both the test site and sites that are both geographically close and climatically close for each cycle, to avoid the risk of pseudo-replication.

r-frapo 0.4-2
Propagated dependencies: r-timeseries@4041.111 r-rglpk@0.6-5.1 r-cccp@0.3-3
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://cran.r-project.org/package=FRAPO
Licenses: GPL 3+
Build system: r
Synopsis: Financial Risk Modelling and Portfolio Optimisation with R
Description:

Accompanying package of the book Financial Risk Modelling and Portfolio Optimisation with R', second edition. The data sets used in the book are contained in this package.

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