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     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
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fd 10.2.0
Dependencies: jemalloc@5.3.0
Channel: guix
Location: gnu/packages/rust-apps.scm (gnu packages rust-apps)
Home page: https://github.com/sharkdp/fd
Licenses: Expat ASL 2.0
Synopsis: Simple, fast and user-friendly alternative to find
Description:

fd is a simple, fast and user-friendly alternative to find. While it does not seek to mirror all of find's powerful functionality, it provides defaults for 80% of the use cases.

fdm 2.2
Dependencies: tdb@1.4.7 openssl@3.0.8 zlib@1.3
Channel: guix
Location: gnu/packages/mail.scm (gnu packages mail)
Home page: https://github.com/nicm/fdm
Licenses: non-copyleft
Synopsis: @acronym{MRA, Mail Retrieval Agent} and @acronym{MDA, Mail Delivery Agent}
Description:

fdm fetches and delivers mail in various ways.

Mail may be fetched from IMAP or POP3 servers, from local maildirs, or read from standard input. It is then filtered based on regular expressions, its size or age, or the output of a (shell) command. It can be rewritten by an external process, dropped, left on the server or delivered into maildirs, mboxes, to a file or pipe, or any combination.

fdm is primarily designed for use by a single user, but can use privilege separation to safely deliver mail in multi-user setups.

r-fd 1.0-12.3
Propagated dependencies: r-vegan@2.6-10 r-geometry@0.5.2 r-ape@5.8-1 r-ade4@1.7-23
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://cran.r-project.org/package=FD
Licenses: GPL 2
Synopsis: Measuring Functional Diversity (FD) from Multiple Traits, and Other Tools for Functional Ecology
Description:

Computes different multidimensional FD indices. Implements a distance-based framework to measure FD that allows any number and type of functional traits, and can also consider species relative abundances. Also contains other useful tools for functional ecology.

fdisk 2.0.0a1
Dependencies: gettext-minimal@0.21 guile@1.8.8 libxcrypt@4.4.36 util-linux@2.37.4 parted@3.6
Channel: guix
Location: gnu/packages/disk.scm (gnu packages disk)
Home page: https://www.gnu.org/software/fdisk/
Licenses: GPL 3+
Synopsis: Low-level disk partitioning and formatting
Description:

GNU fdisk provides a GNU version of the common disk partitioning tool fdisk. fdisk is used for the creation and manipulation of disk partition tables, and it understands a variety of different formats.

r-fda 6.2.0
Propagated dependencies: r-desolve@1.40 r-fds@1.8
Channel: guix
Location: gnu/packages/cran.scm (gnu packages cran)
Home page: https://www.functionaldata.org
Licenses: GPL 2+
Synopsis: Functional data analysis
Description:

These functions were developed to support functional data analysis as described in Ramsay, J. O. and Silverman, B. W. (2005) Functional Data Analysis. The package includes data sets and script files working many examples.

r-fdq 0.12
Propagated dependencies: r-sqldf@0.4-11 r-randomcolor@1.1.0.1 r-ggplot2@3.5.2 r-fgmutils@0.9.5 r-data-table@1.17.2
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://cran.r-project.org/package=fdq
Licenses: GPL 3
Synopsis: Forest Data Quality
Description:

Forest data quality is a package containing nine methods of analysis for forest databases, from databases containing inventory data and growth models, the focus of the analyzes is related to the quality of the data present in the database with a focus on consistency , punctuality and completeness of data.

r-fds 1.8
Propagated dependencies: r-rainbow@3.8 r-rcurl@1.98-1.17
Channel: guix
Location: gnu/packages/cran.scm (gnu packages cran)
Home page: https://cran.r-project.org/web/packages/fds/
Licenses: GPL 2+
Synopsis: Functional data sets
Description:

This package contains a list of functional time series, sliced functional time series, and functional data sets. Functional time series is a special type of functional data observed over time. Sliced functional time series is a special type of functional time series with a time variable observed over time.

r-fdx 2.0.2
Propagated dependencies: r-rcpparmadillo@14.4.2-1 r-rcpp@1.0.14 r-pracma@2.4.4 r-poissonbinomial@1.2.7 r-lifecycle@1.0.4 r-discretefdr@2.1.0 r-checkmate@2.3.2
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://github.com/DISOhda/FDX
Licenses: GPL 3
Synopsis: False Discovery Exceedance Controlling Multiple Testing Procedures
Description:

Multiple testing procedures for heterogeneous and discrete tests as described in Döhler and Roquain (2020) <doi:10.1214/20-EJS1771>. The main algorithms of the paper are available as continuous, discrete and weighted versions. They take as input the results of a test procedure from package DiscreteTests', or a set of observed p-values and their discrete support under their nulls. A shortcut function to obtain such p-values and supports is also provided, along with wrappers allowing to apply discrete procedures directly to data.

fdkaac 1.0.6
Dependencies: libfdk@2.0.1
Channel: guix
Location: gnu/packages/audio.scm (gnu packages audio)
Home page: https://github.com/nu774/fdkaac
Licenses: Zlib
Synopsis: Command-line AAC encoder
Description:

This package provides a command-line AAC-encoder.

fdupes 2.3.2
Dependencies: ncurses@6.2.20210619 pcre2@10.42 sqlite@3.39.3
Channel: guix
Location: gnu/packages/admin.scm (gnu packages admin)
Home page: https://github.com/adrianlopezroche/fdupes
Licenses: Expat
Synopsis: Identify duplicate files
Description:

fdupes is a program for identifying duplicate files residing within specified directories.

r-fdth 1.3-0
Propagated dependencies: r-xtable@1.8-4
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://github.com/jcfaria/fdth
Licenses: GPL 2+
Synopsis: Frequency Distribution Tables, Histograms and Polygons
Description:

Perform frequency distribution tables, associated histograms and polygons from vector, data.frame and matrix objects for numerical and categorical variables.

r-fdma 2.2.8
Propagated dependencies: r-zoo@1.8-14 r-xts@0.14.1 r-tseries@0.10-58 r-rcpparmadillo@14.4.2-1 r-rcpp@1.0.14 r-psych@2.5.3 r-png@0.1-8 r-itertools@0.1-3 r-iterators@1.0.14 r-gplots@3.2.0 r-forecast@8.24.0 r-foreach@1.5.2 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://CRAN.R-project.org/package=fDMA
Licenses: GPL 3
Synopsis: Dynamic Model Averaging and Dynamic Model Selection for Continuous Outcomes
Description:

Allows to estimate dynamic model averaging, dynamic model selection and median probability model. The original methods are implemented, as well as, selected further modifications of these methods. In particular the user might choose between recursive moment estimation and exponentially moving average for variance updating. Inclusion probabilities might be modified in a way using Google Trends'. The code is written in a way which minimises the computational burden (which is quite an obstacle for dynamic model averaging if many variables are used). For example, this package allows for parallel computations and Occam's window approach. The package is designed in a way that is hoped to be especially useful in economics and finance. Main reference: Raftery, A.E., Karny, M., Ettler, P. (2010) <doi:10.1198/TECH.2009.08104>.

r-fddm 1.0-2
Propagated dependencies: r-rcppeigen@0.3.4.0.2 r-rcpp@1.0.14 r-formula@1.2-5
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://github.com/rtdists/fddm
Licenses: GPL 2+
Synopsis: Fast Implementation of the Diffusion Decision Model
Description:

This package provides the probability density function (PDF), cumulative distribution function (CDF), the first-order and second-order partial derivatives of the PDF, and a fitting function for the diffusion decision model (DDM; e.g., Ratcliff & McKoon, 2008, <doi:10.1162/neco.2008.12-06-420>) with across-trial variability in the drift rate. Because the PDF, its partial derivatives, and the CDF of the DDM both contain an infinite sum, they need to be approximated. fddm implements all published approximations (Navarro & Fuss, 2009, <doi:10.1016/j.jmp.2009.02.003>; Gondan, Blurton, & Kesselmeier, 2014, <doi:10.1016/j.jmp.2014.05.002>; Blurton, Kesselmeier, & Gondan, 2017, <doi:10.1016/j.jmp.2016.11.003>; Hartmann & Klauer, 2021, <doi:10.1016/j.jmp.2021.102550>) plus new approximations. All approximations are implemented purely in C++ providing faster speed than existing packages.

r-fdrci 2.4
Propagated dependencies: r-ggplot2@3.5.2
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://cran.r-project.org/package=fdrci
Licenses: Artistic License 2.0
Synopsis: Permutation-Based FDR Point and Confidence Interval Estimation
Description:

FDR functions for permutation-based estimators, including pi0 as well as FDR confidence intervals. The confidence intervals account for dependencies between tests by the incorporation of an overdispersion parameter, which is estimated from the permuted data. Also included are options for an analog parametric approach.

r-fdasp 1.1.1
Propagated dependencies: r-rdpack@2.6.4 r-rcpparmadillo@14.4.2-1 r-rcpp@1.0.14 r-pracma@2.4.4 r-ks@1.15.1 r-foreach@1.5.2 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://cran.r-project.org/package=fdaSP
Licenses: GPL 3+
Synopsis: Sparse Functional Data Analysis Methods
Description:

This package provides algorithms to fit linear regression models under several popular penalization techniques and functional linear regression models based on Majorizing-Minimizing (MM) and Alternating Direction Method of Multipliers (ADMM) techniques. See Boyd et al (2010) <doi:10.1561/2200000016> for complete introduction to the method.

emacs-fd 0.1.0
Channel: guix
Location: gnu/packages/emacs-xyz.scm (gnu packages emacs-xyz)
Home page: https://github.com/yqrashawn/fd-dired
Licenses: GPL 3+
Synopsis: Dired-mode interface for fd result
Description:

This package provides a dired-mode interface for fd's result.

fdroidcl 0.7.0
Dependencies: go-github-com-kr-pretty@0.3.1 go-github-com-schollz-progressbar-v3@3.13.1
Channel: guix
Location: gnu/packages/android.scm (gnu packages android)
Home page: https://github.com/mvdan/fdroidcl
Licenses: Modified BSD
Synopsis: F-Droid desktop client
Description:

While the Android client integrates with the system with regular update checks and notifications, this is a simple command line client that talks to connected devices via ADB.

r-fdaacf 1.0.0
Propagated dependencies: r-vars@1.6-1 r-pracma@2.4.4 r-fda@6.2.0 r-compquadform@1.4.3
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://github.com/GMestreM/fdaACF
Licenses: GPL 2+
Synopsis: Autocorrelation Function for Functional Time Series
Description:

Quantify the serial correlation across lags of a given functional time series using the autocorrelation function and a partial autocorrelation function for functional time series proposed in Mestre et al. (2021) <doi:10.1016/j.csda.2020.107108>. The autocorrelation functions are based on the L2 norm of the lagged covariance operators of the series. Functions are available for estimating the distribution of the autocorrelation functions under the assumption of strong functional white noise.

r-fdm2id 0.9.9
Propagated dependencies: r-pls@2.8-5 r-nnet@7.3-20 r-mclust@6.1.1 r-factominer@2.11 r-arulesviz@1.5.3 r-arules@1.7-10
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://cran.r-project.org/package=fdm2id
Licenses: GPL 3
Synopsis: Data Mining and R Programming for Beginners
Description:

This package contains functions to simplify the use of data mining methods (classification, regression, clustering, etc.), for students and beginners in R programming. Various R packages are used and wrappers are built around the main functions, to standardize the use of data mining methods (input/output): it brings a certain loss of flexibility, but also a gain of simplicity. The package name came from the French "Fouille de Données en Master 2 Informatique Décisionnelle".

r-fdarep 0.1.1
Propagated dependencies: r-rcppeigen@0.3.4.0.2 r-rcpp@1.0.14 r-pracma@2.4.4 r-numderiv@2016.8-1.1 r-matrix@1.7-3 r-mass@7.3-65 r-hmisc@5.2-3 r-fdapace@0.6.0
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://github.com/functionaldata/tFDArep
Licenses: Modified BSD
Synopsis: Two-Dimensional FPCA, Marginal FPCA, and Product FPCA for Repeated Functional Data
Description:

This package provides an implementation of two-dimensional functional principal component analysis (FPCA), Marginal FPCA, and Product FPCA for repeated functional data. Marginal and Product FPCA implementations are done for both dense and sparsely observed functional data. References: Chen, K., Delicado, P., & Müller, H. G. (2017) <doi:10.1111/rssb.12160>. Chen, K., & Müller, H. G. (2012) <doi:10.1080/01621459.2012.734196>. Hall, P., Müller, H.G. and Wang, J.L. (2006) <doi:10.1214/009053606000000272>. Yao, F., Müller, H. G., & Wang, J. L. (2005) <doi:10.1198/016214504000001745>.

r-fdapde 1.1-21
Propagated dependencies: r-rgl@1.3.18 r-rcppeigen@0.3.4.0.2 r-rcpp@1.0.14 r-plot3d@1.4.1 r-matrix@1.7-3
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://cran.r-project.org/package=fdaPDE
Licenses: GPL 3
Synopsis: Physics-Informed Spatial and Functional Data Analysis
Description:

An implementation of regression models with partial differential regularizations, making use of the Finite Element Method. The models efficiently handle data distributed over irregularly shaped domains and can comply with various conditions at the boundaries of the domain. A priori information about the spatial structure of the phenomenon under study can be incorporated in the model via the differential regularization. See Sangalli, L. M. (2021) <doi:10.1111/insr.12444> "Spatial Regression With Partial Differential Equation Regularisation" for an overview. The release 1.1-9 requires R (>= 4.2.0) to be installed on windows machines.

r-fda-usc 2.2.0
Propagated dependencies: r-nlme@3.1-168 r-mgcv@1.9-3 r-mass@7.3-65 r-ksamples@1.2-10 r-knitr@1.50 r-iterators@1.0.14 r-foreach@1.5.2 r-fda@6.2.0 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://github.com/moviedo5/fda.usc
Licenses: GPL 2
Synopsis: Functional Data Analysis and Utilities for Statistical Computing
Description:

Routines for exploratory and descriptive analysis of functional data such as depth measurements, atypical curves detection, regression models, supervised classification, unsupervised classification and functional analysis of variance.

r-fdboost 1.1-2
Propagated dependencies: r-zoo@1.8-14 r-stabs@0.6-4 r-mgcv@1.9-3 r-mboost@2.9-11 r-matrix@1.7-3 r-mass@7.3-65 r-gamboostlss@2.1-0
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://github.com/boost-R/FDboost
Licenses: GPL 2
Synopsis: Boosting Functional Regression Models
Description:

Regression models for functional data, i.e., scalar-on-function, function-on-scalar and function-on-function regression models, are fitted by a component-wise gradient boosting algorithm. For a manual on how to use FDboost', see Brockhaus, Ruegamer, Greven (2017) <doi:10.18637/jss.v094.i10>.

r-fdasrvf 2.4.0
Propagated dependencies: r-viridislite@0.4.2 r-tolerance@3.0.0 r-rlang@1.1.6 r-rcpparmadillo@14.4.2-1 r-rcpp@1.0.14 r-mvtnorm@1.3-3 r-minpack-lm@1.2-4 r-matrix@1.7-3 r-lpsolve@5.6.23 r-foreach@1.5.2 r-fields@16.3.1 r-doparallel@1.0.17 r-coda@0.19-4.1 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/f.scm (guix-cran packages f)
Home page: https://github.com/jdtuck/fdasrvf_R
Licenses: GPL 3
Synopsis: Elastic Functional Data Analysis
Description:

This package performs alignment, PCA, and modeling of multidimensional and unidimensional functions using the square-root velocity framework (Srivastava et al., 2011 <doi:10.48550/arXiv.1103.3817> and Tucker et al., 2014 <DOI:10.1016/j.csda.2012.12.001>). This framework allows for elastic analysis of functional data through phase and amplitude separation.

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