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f.scm
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(define-module (guix-cran packages f)
#:use-module (guix packages)
#:use-module (guix download)
#:use-module (guix build-system r)
#:use-module ((guix licenses)
#:prefix license:)
#:use-module (gnu packages statistics)
#:use-module (gnu packages cran)
#:use-module (gnu packages python)
#:use-module (gnu packages gcc)
#:use-module (gnu packages java)
#:use-module (gnu packages maths)
#:use-module (gnu packages bioconductor)
#:use-module (gnu packages multiprecision)
#:use-module (gnu packages python-xyz)
#:use-module (gnu packages image-processing)
#:use-module (gnu packages python-science)
#:use-module (gnu packages web)
#:use-module (gnu packages haskell-xyz)
#:use-module (gnu packages pkg-config)
#:use-module (gnu packages perl)
#:use-module (gnu packages bioinformatics)
#:use-module (gnu packages version-control)
#:use-module (gnu packages base)
#:use-module (gnu packages algebra)
#:use-module (gnu packages geo)
#:use-module (guix-cran packages z)
#:use-module (guix-cran packages y)
#:use-module (guix-cran packages x)
#:use-module (guix-cran packages w)
#:use-module (guix-cran packages v)
#:use-module (guix-cran packages u)
#:use-module (guix-cran packages t)
#:use-module (guix-cran packages s)
#:use-module (guix-cran packages r)
#:use-module (guix-cran packages q)
#:use-module (guix-cran packages p)
#:use-module (guix-cran packages o)
#:use-module (guix-cran packages n)
#:use-module (guix-cran packages m)
#:use-module (guix-cran packages l)
#:use-module (guix-cran packages k)
#:use-module (guix-cran packages j)
#:use-module (guix-cran packages i)
#:use-module (guix-cran packages h)
#:use-module (guix-cran packages g)
#:use-module (guix-cran packages e)
#:use-module (guix-cran packages d)
#:use-module (guix-cran packages c)
#:use-module (guix-cran packages b)
#:use-module (guix-cran packages a))
(define-public r-fy
(package
(name "r-fy")
(version "0.4.2")
(source
(origin
(method url-fetch)
(uri (cran-uri "fy" version))
(sha256
(base32 "1gbv2hvh3y9qdld660i250snwxn4irp41qrr3pc1nakbf5b9aqnz"))))
(properties `((upstream-name . "fy")))
(build-system r-build-system)
(propagated-inputs (list r-hutils r-fastmatch r-data-table))
(home-page "https://cran.r-project.org/package=fy")
(synopsis "Utilities for Financial Years")
(description
"In Australia, a financial year (or fiscal year) is the period from 1 July to 30
June of the following calendar year. As such, many databases need to represent
and validate financial years efficiently. While the use of integer years with a
convention that they represent the year ending is common, it may lead to
ambiguity with calendar years. On the other hand, string representations may be
too inefficient and do not easily admit arithmetic operations. This package
tries to make validation of financial years quicker while retaining clarity.")
(license license:gpl2)))
(define-public r-fxtwapls
(package
(name "r-fxtwapls")
(version "0.1.2")
(source
(origin
(method url-fetch)
(uri (cran-uri "fxTWAPLS" version))
(sha256
(base32 "1bxrwkf65qw3k6mqvp27lqw6i2r7bdbilnl9406k1nva353gcxxm"))))
(properties `((upstream-name . "fxTWAPLS")))
(build-system r-build-system)
(propagated-inputs (list r-progressr
r-mass
r-jops
r-ggplot2
r-geosphere
r-future
r-foreach
r-dofuture))
(home-page "https://github.com/special-uor/fxTWAPLS/")
(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.")
(license license:gpl3)))
(define-public r-fxregime
(package
(name "r-fxregime")
(version "1.0-4")
(source
(origin
(method url-fetch)
(uri (cran-uri "fxregime" version))
(sha256
(base32 "0ml1q0xp90jfng6a60pfkp6j7wajk6pz4p4wm08rfqikgsdvp0m4"))))
(properties `((upstream-name . "fxregime")))
(build-system r-build-system)
(propagated-inputs (list r-zoo r-strucchange r-sandwich r-car))
(home-page "https://cran.r-project.org/package=fxregime")
(synopsis "Exchange Rate Regime Analysis")
(description
"Exchange rate regression and structural change tools for estimating, testing,
dating, and monitoring (de facto) exchange rate regimes.")
(license (list license:gpl2 license:gpl3))))
(define-public r-fxl
(package
(name "r-fxl")
(version "1.6.3")
(source
(origin
(method url-fetch)
(uri (cran-uri "fxl" version))
(sha256
(base32 "1c1cw1j1vz7my868ryg0r7zw655xbhk0za75pvd3mgfy0bs6600x"))))
(properties `((upstream-name . "fxl")))
(build-system r-build-system)
(propagated-inputs (list r-rlang r-grimport))
(native-inputs (list r-knitr))
(home-page "https://cran.r-project.org/package=fxl")
(synopsis "'fxl' Single Case Design Charting Package")
(description
"The fxl Charting package is used to prepare and design single case design
figures that are typically prepared in spreadsheet software. With fxl', there
is no need to leave the R environment to prepare these works.")
(license license:gpl3+)))
(define-public r-fwsim
(package
(name "r-fwsim")
(version "0.3.4")
(source
(origin
(method url-fetch)
(uri (cran-uri "fwsim" version))
(sha256
(base32 "0fy87c1x5hihfcppv1pvk3b0pwl6ygqpka40x55gbpkgssdigb1l"))))
(properties `((upstream-name . "fwsim")))
(build-system r-build-system)
(propagated-inputs (list r-rcpp))
(home-page "https://cran.r-project.org/package=fwsim")
(synopsis "Fisher-Wright Population Simulation")
(description
"Simulates a population under the Fisher-Wright model (fixed or stochastic
population size) with a one-step neutral mutation process (stepwise mutation
model, logistic mutation model and exponential mutation model supported). The
stochastic population sizes are random Poisson distributed and different kinds
of population growth are supported. For the stepwise mutation model, it is
possible to specify locus and direction specific mutation rate (in terms of
upwards and downwards mutation rate). Intermediate generations can be saved in
order to study e.g. drift.")
(license license:gpl2)))
(define-public r-fwrgb
(package
(name "r-fwrgb")
(version "0.1.0")
(source
(origin
(method url-fetch)
(uri (cran-uri "FWRGB" version))
(sha256
(base32 "0wi1vkb8mmw9f8hzgp8cm6hy9nbglz0x8mllngdamhlvm35gr57d"))))
(properties `((upstream-name . "FWRGB")))
(build-system r-build-system)
(propagated-inputs (list r-neuralnet r-imager r-e1071))
(home-page "https://cran.r-project.org/package=FWRGB")
(synopsis "Fresh Weight Determination from Visual Image of the Plant")
(description
"Fresh biomass determination is the key to evaluating crop genotypes response to
diverse input and stress conditions and forms the basis for calculating net
primary production. However, as conventional phenotyping approaches for
measuring fresh biomass is time-consuming, laborious and destructive,
image-based phenotyping methods are being widely used now. In the image-based
approach, the fresh weight of the above-ground part of the plant depends on the
projected area. For determining the projected area, the visual image of the
plant is converted into the grayscale image by simply averaging the Red(R),
Green (G) and Blue (B) pixel values. Grayscale image is then converted into a
binary image using Otsuâs thresholding method Otsu, N. (1979)
<doi:10.1109/TSMC.1979.4310076> to separate plant area from the background
(image segmentation). The segmentation process was accomplished by selecting
the pixels with values over the threshold value belonging to the plant region
and other pixels to the background region. The resulting binary image consists
of white and black pixels representing the plant and background regions.
Finally, the number of pixels inside the plant region was counted and converted
to square centimetres (cm2) using the reference object (any object whose actual
area is known previously) to get the projected area. After that, the projected
area is used as input to the machine learning model (Linear Model, Artificial
Neural Network, and Support Vector Regression) to determine the plant's fresh
weight.")
(license license:gpl3)))
(define-public r-fwlplot
(package
(name "r-fwlplot")
(version "0.2.0")
(source
(origin
(method url-fetch)
(uri (cran-uri "fwlplot" version))
(sha256
(base32 "1fi2ijfkpxfbwxfcyc5mk2c7mpj6z7lrwm8wlilk2g04y6jmw84l"))))
(properties `((upstream-name . "fwlplot")))
(build-system r-build-system)
(propagated-inputs (list r-ggplot2 r-fixest r-data-table))
(home-page "https://cran.r-project.org/package=fwlplot")
(synopsis "Scatter Plot After Residualizing Using 'fixest' Package")
(description
"This package creates a scatter plot after residualizing using a set of
covariates. The residuals are calculated using the fixest package which allows
very fast estimation that scales. Details of the (Yule-)Frisch-Waugh-Lovell
theorem is given in Basu (2023) <@code{arXiv:2307.00369>}.")
(license license:expat)))
(define-public r-fwildclusterboot
(package
(name "r-fwildclusterboot")
(version "0.13.0")
(source
(origin
(method url-fetch)
(uri (cran-uri "fwildclusterboot" version))
(sha256
(base32 "0slm28pgvfsrsdvz2i1h231gkf4iq6gdlspyzppn8nwhngvic5hd"))))
(properties `((upstream-name . "fwildclusterboot")))
(build-system r-build-system)
(propagated-inputs (list r-summclust
r-rlang
r-rcppeigen
r-rcpparmadillo
r-rcpp
r-matrix
r-mass
r-juliaconnector
r-gtools
r-generics
r-formula
r-dreamerr
r-dqrng
r-collapse))
(native-inputs (list r-knitr))
(home-page "https://s3alfisc.github.io/fwildclusterboot/")
(synopsis "Fast Wild Cluster Bootstrap Inference for Linear Models")
(description
"Implementation of fast algorithms for wild cluster bootstrap inference developed
in Roodman et al (2019, STATA Journal, <doi:10.1177/1536867X19830877>) and
@code{MacKinnon} et al (2022), which makes it feasible to quickly calculate
bootstrap test statistics based on a large number of bootstrap draws even for
large samples. Multiple bootstrap types as described in @code{MacKinnon},
Nielsen & Webb (2022) are supported. Further, multiway clustering, regression
weights, bootstrap weights, fixed effects and subcluster bootstrapping are
supported. Further, both restricted ('WCR') and unrestricted ('WCU') bootstrap
are supported. Methods are provided for a variety of fitted models, including
lm()', feols() (from package fixest') and felm() (from package lfe').
Additionally implements a heteroskedasticity-robust ('HC1') wild bootstrap.
Last, the package provides an R binding to @code{WildBootTests.jl}', which
provides additional speed gains and functionality, including the WRE bootstrap
for instrumental variable models (based on models of type ivreg() from package
ivreg') and hypotheses with q > 1.")
(license license:gpl3)))
(define-public r-fwdselect
(package
(name "r-fwdselect")
(version "2.1.0")
(source
(origin
(method url-fetch)
(uri (cran-uri "FWDselect" version))
(sha256
(base32 "0w0hkmhcz7h1lixk7p3yffbbalgxwh2lv463vqz361k80sri6wz7"))))
(properties `((upstream-name . "FWDselect")))
(build-system r-build-system)
(propagated-inputs (list r-mgcv r-cvtools))
(home-page "http://cran.r-project.org/package=FWDselect")
(synopsis "Selecting Variables in Regression Models")
(description
"This package provides a simple method to select the best model or best subset of
variables using different types of data (binary, Gaussian or Poisson) and
applying it in different contexts (parametric or non-parametric).")
(license license:expat)))
(define-public r-fwb
(package
(name "r-fwb")
(version "0.2.0")
(source
(origin
(method url-fetch)
(uri (cran-uri "fwb" version))
(sha256
(base32 "02r6p2ck56lm3ip105cp0m5q05lifynpq767bl2igf7zsvhjqpak"))))
(properties `((upstream-name . "fwb")))
(build-system r-build-system)
(propagated-inputs (list r-rlang r-pbapply r-chk))
(home-page "https://github.com/ngreifer/fwb")
(synopsis "Fractional Weighted Bootstrap")
(description
"An implementation of the fractional weighted bootstrap to be used as a drop-in
for functions in the boot package. The fractional weighted bootstrap (also
known as the Bayesian bootstrap) involves drawing weights randomly that are
applied to the data rather than resampling units from the data. See Xu et al.
(2020) <doi:10.1080/00031305.2020.1731599> for details.")
(license license:gpl2+)))
(define-public r-fuzzywuzzyr
(package
(name "r-fuzzywuzzyr")
(version "1.0.5")
(source
(origin
(method url-fetch)
(uri (cran-uri "fuzzywuzzyR" version))
(sha256
(base32 "1g73xivxyh5fvppccgnxhgar6jsl6zsr2djkg0bhh10i633l56ia"))))
(properties `((upstream-name . "fuzzywuzzyR")))
(build-system r-build-system)
(inputs (list python))
(propagated-inputs (list r-reticulate r-r6))
(native-inputs (list r-knitr))
(home-page "https://github.com/mlampros/fuzzywuzzyR")
(synopsis "Fuzzy String Matching")
(description
"Fuzzy string matching implementation of the fuzzywuzzy
<https://github.com/seatgeek/fuzzywuzzy> python package. It uses the
Levenshtein Distance <https://en.wikipedia.org/wiki/Levenshtein_distance> to
calculate the differences between sequences.")
(license license:gpl2)))
(define-public r-fuzzysts
(package
(name "r-fuzzysts")
(version "0.2")
(source
(origin
(method url-fetch)
(uri (cran-uri "FuzzySTs" version))
(sha256
(base32 "0zs1pyn9apysspxa0glqc96h6npmvvhiqvqpcm0brslz0b6xnilp"))))
(properties `((upstream-name . "FuzzySTs")))
(build-system r-build-system)
(propagated-inputs (list r-polynom r-fuzzynumbers))
(native-inputs (list r-knitr))
(home-page "https://cran.r-project.org/package=FuzzySTs")
(synopsis "Fuzzy Statistical Tools")
(description
"The main goal of this package is to present various fuzzy statistical tools. It
intends to provide an implementation of the theoretical and empirical approaches
presented in the thesis entitled \"The signed distance measure in fuzzy
statistical analysis. Some theoretical, empirical and programming advances\"
(Thesis to be published soon. For the theoretical approaches, see Berkachy R.
and Donze L. (2019) <doi:10.1007/978-3-030-03368-2_1>. For the empirical
approaches, see Berkachy R. and Donze L. (2016) <ISBN: 978-989-758-201-1>).
Important (non-exhaustive) implementation highlights of this package are as
follows: (1) a numerical procedure to estimate the fuzzy difference and the
fuzzy square. (2) two numerical methods of fuzzification. (3) a function
performing different possibilities of distances, including the signed distance
and the generalized signed distance for instance. (4) numerical estimations of
fuzzy statistical measures such as the variance, the moment, etc. (5) two
methods of estimation of the bootstrap distribution of the likelihood ratio in
the fuzzy context. (6) an estimation of a fuzzy confidence interval by the
likelihood ratio method. (7) testing fuzzy hypotheses and/or fuzzy data by fuzzy
confidence intervals in the Kwakernaak - Kruse and Meyer sense. (8) a general
method to estimate the fuzzy p-value with fuzzy hypotheses and/or fuzzy data.
(9) a method of estimation of global and individual evaluations of linguistic
questionnaires. (10) numerical estimations of multi-ways analysis of variance
models in the fuzzy context. The unbalance in the considered designs are also
foreseen.")
(license license:expat)))
(define-public r-fuzzystattraeoo
(package
(name "r-fuzzystattraeoo")
(version "1.0")
(source
(origin
(method url-fetch)
(uri (cran-uri "FuzzyStatTraEOO" version))
(sha256
(base32 "0jxjnmsznab0nf6nrjvdcy2gjmzn7fa0va993f7ylasr2pjawgsd"))))
(properties `((upstream-name . "FuzzyStatTraEOO")))
(build-system r-build-system)
(propagated-inputs (list r-r6))
(home-page
"https://bellman.ciencias.uniovi.es/smire+codire/FuzzyStatTraRpackage.html")
(synopsis
"Package 'FuzzyStatTra' in Encapsulated Object Oriented Programming")
(description
"The aim of the package is to contain the package @code{FuzzyStatTra} in
Encapsulated Object Oriented Programming using R6. @code{FuzzyStatTra} contains
Statistical Methods for Trapezoidal Fuzzy Numbers, whose aim is to provide some
basic functions for doing statistical analysis with trapezoidal fuzzy numbers.
For more details, you can visit the website of the SMIRE+@code{CoDiRE}
(Statistical Methods with Imprecise Random Elements and Comparison of
Distributions of Random Elements) Research Group
(<https://bellman.ciencias.uniovi.es/smire+codire/>). The most related paper
can be found in References. Now, those functions are organized in specific
classes and methods. This object-based approach is an important step in making
statistical computing more accessible to users.")
(license license:lgpl3+)))
(define-public r-fuzzystattra
(package
(name "r-fuzzystattra")
(version "1.0")
(source
(origin
(method url-fetch)
(uri (cran-uri "FuzzyStatTra" version))
(sha256
(base32 "1ijrlnlmq9d5ahgrpzba6kzkaq1zq59zqdgcizybsf9alsswcm00"))))
(properties `((upstream-name . "FuzzyStatTra")))
(build-system r-build-system)
(home-page "https://cran.r-project.org/package=FuzzyStatTra")
(synopsis "Statistical Methods for Trapezoidal Fuzzy Numbers")
(description
"The aim of the package is to provide some basic functions for doing statistics
with trapezoidal fuzzy numbers. In particular, the package contains several
functions for simulating trapezoidal fuzzy numbers, as well as for calculating
some central tendency measures (mean and two types of median), some scale
measures (variance, ADD, MDD, Sn, Qn, Tn and some M-estimators) and one
diversity index and one inequality index. Moreover, functions for calculating
the 1-norm distance, the mid/spr distance and the (phi,theta)-wabl/ldev/rdev
distance between fuzzy numbers are included, and a function to calculate the
value phi-wabl given a sample of trapezoidal fuzzy numbers.")
(license license:gpl2+)))
(define-public r-fuzzystatprob
(package
(name "r-fuzzystatprob")
(version "2.0.4")
(source
(origin
(method url-fetch)
(uri (cran-uri "FuzzyStatProb" version))
(sha256
(base32 "1jpqb8xczi1d4g306vrwpi02f9h59aki1pgnckvfmiclr306prpb"))))
(properties `((upstream-name . "FuzzyStatProb")))
(build-system r-build-system)
(propagated-inputs (list r-multinomialci r-fuzzynumbers r-deoptim))
(native-inputs (list r-r-rsp))
(home-page "http://decsai.ugr.es/~pjvi/r-packages.html")
(synopsis
"Fuzzy Stationary Probabilities from a Sequence of Observations of an Unknown Markov Chain")
(description
"An implementation of a method for computing fuzzy numbers representing
stationary probabilities of an unknown Markov chain, from which a sequence of
observations along time has been obtained. The algorithm is based on the
proposal presented by James Buckley in his book on Fuzzy probabilities
(Springer, 2005), chapter 6. Package @code{FuzzyNumbers} is used to represent
the output probabilities.")
(license license:lgpl3+)))
(define-public r-fuzzysimres
(package
(name "r-fuzzysimres")
(version "0.4.0")
(source
(origin
(method url-fetch)
(uri (cran-uri "FuzzySimRes" version))
(sha256
(base32 "0zhzzjr28c7byx4vkh8y8a6b6v9isa2wyhwldpzsml2mxg0f3300"))))
(properties `((upstream-name . "FuzzySimRes")))
(build-system r-build-system)
(propagated-inputs (list r-palasso r-fuzzynumbers))
(home-page "https://cran.r-project.org/package=FuzzySimRes")
(synopsis "Simulation and Resampling Methods for Epistemic Fuzzy Data")
(description
"Random simulations of fuzzy numbers are still a challenging problem. The aim of
this package is to provide the respective procedures to simulate fuzzy random
variables, especially in the case of the piecewise linear fuzzy numbers (PLFNs,
see Coroianua et al. (2013) <doi:10.1016/j.fss.2013.02.005> for the further
details). Additionally, the special resampling algorithms known as the
epistemic bootstrap are provided (see Grzegorzewski and Romaniuk (2022)
<doi:10.34768/amcs-2022-0021>, Grzegorzewski and Romaniuk (2022)
<doi:10.1007/978-3-031-08974-9_39>) together with the functions to apply
statistical tests and estimate various characteristics based on the epistemic
bootstrap. The package also includes a real-life data set of epistemic fuzzy
triangular numbers. The fuzzy numbers used in this package are consistent with
the @code{FuzzyNumbers} package.")
(license license:gpl3)))
(define-public r-fuzzysim
(package
(name "r-fuzzysim")
(version "4.10.7")
(source
(origin
(method url-fetch)
(uri (cran-uri "fuzzySim" version))
(sha256
(base32 "1xfq2il2d44a99yp2lnsmmna52spqk92sv29farmxwppfvghg942"))))
(properties `((upstream-name . "fuzzySim")))
(build-system r-build-system)
(propagated-inputs (list r-modeva))
(home-page "fuzzysim.r-forge.r-project.org")
(synopsis "Fuzzy Similarity in Species Distributions")
(description
"This package provides functions to compute fuzzy versions of species occurrence
patterns based on presence-absence data (including inverse distance
interpolation, trend surface analysis, and prevalence-independent favourability
obtained from probability of presence), as well as pair-wise fuzzy similarity
(based on fuzzy logic versions of commonly used similarity indices) among those
occurrence patterns. Includes also functions for model consensus and comparison
(overlap and fuzzy similarity, loss or gain), and for data preparation, such as
obtaining unique abbreviations of species names, cleaning and gridding
(thinning) point occurrence data onto raster maps, selecting absences under
specified criteria, converting species lists (long format) to presence-absence
tables (wide format), transposing part of a data frame, selecting relevant
variables for models, assessing the false discovery rate, or analysing and
dealing with multicollinearity. Initially described in Barbosa (2015)
<doi:10.1111/2041-210X.12372>.")
(license license:gpl3)))
(define-public r-fuzzyresampling
(package
(name "r-fuzzyresampling")
(version "0.6.3")
(source
(origin
(method url-fetch)
(uri (cran-uri "FuzzyResampling" version))
(sha256
(base32 "1gic7la5ks7v37f3h4azazz4sv650sp6pl199wk4021blkah5r56"))))
(properties `((upstream-name . "FuzzyResampling")))
(build-system r-build-system)
(home-page "https://github.com/mroman-ibs/FuzzyResampling")
(synopsis
"Resampling Methods for Triangular and Trapezoidal Fuzzy Numbers")
(description
"The classical (i.e. Efron's, see Efron and Tibshirani (1994,
ISBN:978-0412042317) \"An Introduction to the Bootstrap\") bootstrap is widely
used for both the real (i.e. \"crisp\") and fuzzy data. The main aim of the
algorithms implemented in this package is to overcome a problem with repetition
of a few distinct values and to create fuzzy numbers, which are \"similar\" (but
not the same) to values from the initial sample. To do this, different
characteristics of triangular/trapezoidal numbers are kept (like the value, the
ambiguity, etc., see Grzegorzewski et al. <doi:10.2991/eusflat-19.2019.68>,
Grzegorzewski et al. (2020) <doi:10.2991/ijcis.d.201012.003>, Grzegorzewski et
al. (2020) <doi:10.34768/amcs-2020-0022>, Grzegorzewski and Romaniuk (2022)
<doi:10.1007/978-3-030-95929-6_3>, Romaniuk and Hryniewicz (2019)
<doi:10.1007/s00500-018-3251-5>). Some additional procedures related to these
resampling methods are also provided, like calculation of the Bertoluzza et
al.'s distance (aka the mid/spread distance, see Bertoluzza et al. (1995) \"On a
new class of distances between fuzzy numbers\") and estimation of the p-value of
the one- and two- sample bootstrapped test for the mean (see Lubiano et al.
(2016, <doi:10.1016/j.ejor.2015.11.016>)). Additionally, there are procedures
which randomly generate trapezoidal fuzzy numbers using some well-known
statistical distributions.")
(license license:gpl3)))
(define-public r-fuzzyreg
(package
(name "r-fuzzyreg")
(version "0.6.2")
(source
(origin
(method url-fetch)
(uri (cran-uri "fuzzyreg" version))
(sha256
(base32 "056ryj4w26fb4fpy43fgvqs8ijcpm6v503pnzw73d8gxahwkvwl2"))))
(properties `((upstream-name . "fuzzyreg")))
(build-system r-build-system)
(propagated-inputs (list r-quadprog r-limsolve))
(native-inputs (list r-rmarkdown r-knitr))
(home-page "https://cran.r-project.org/package=fuzzyreg")
(synopsis "Fuzzy Linear Regression")
(description
"Estimators for fuzzy linear regression. The functions estimate parameters of
fuzzy linear regression models with crisp or fuzzy independent variables
(triangular fuzzy numbers are supported). Implements multiple methods for
parameter estimation and algebraic operations with triangular fuzzy numbers.
Includes functions for summarising, printing and plotting the model fit.
Calculates predictions from the model and total error of fit. Individual
methods are described in Diamond (1988) <doi:10.1016/0020-0255(88)90047-3>, Hung
& Yang (2006) <doi:10.1016/j.fss.2006.08.004>, Lee & Tanaka (1999)
<doi:10.15807/jorsj.42.98>, Nasrabadi, Nasrabadi & Nasrabady (2005)
<doi:10.1016/j.amc.2004.02.008>, Skrabanek, Marek & Pozdilkova (2021)
<doi:10.3390/math9060685>, Tanaka, Hayashi & Watada (1989)
<doi:10.1016/0377-2217(89)90431-1>, Zeng, Feng & Li (2017)
<doi:10.1016/j.asoc.2016.09.029>.")
(license license:gpl3)))
(define-public r-fuzzyranktests
(package
(name "r-fuzzyranktests")
(version "0.4")
(source
(origin
(method url-fetch)
(uri (cran-uri "fuzzyRankTests" version))
(sha256
(base32 "04841fh4nf7qriqk0b2ny943bji60bj42j8czg056d2dza4q4039"))))
(properties `((upstream-name . "fuzzyRankTests")))
(build-system r-build-system)
(home-page "http://www.stat.umn.edu/geyer/fuzz/")
(synopsis "Fuzzy Rank Tests and Confidence Intervals")
(description
"Does fuzzy tests and confidence intervals (following Geyer and Meeden,
Statistical Science, 2005, <doi:10.1214/088342305000000340>) for sign test and
Wilcoxon signed rank and rank sum tests.")
(license license:expat)))
(define-public r-fuzzyr
(package
(name "r-fuzzyr")
(version "2.3.2")
(source
(origin
(method url-fetch)
(uri (cran-uri "FuzzyR" version))
(sha256
(base32 "0d0zf8diw7m10zfx0r5zg5arhf4a90sva77h6rvfywixldwrnk7s"))))
(properties `((upstream-name . "FuzzyR")))
(build-system r-build-system)
(propagated-inputs (list r-shiny r-plyr))
(home-page "https://www.lucidresearch.org/")
(synopsis "Fuzzy Logic Toolkit for R")
(description
"Design and simulate fuzzy logic systems using Type-1 and Interval Type-2 Fuzzy
Logic. This toolkit includes with graphical user interface (GUI) and an
adaptive neuro- fuzzy inference system (ANFIS). This toolkit is a continuation
from the previous package ('@code{FuzzyToolkitUoN}'). Produced by the
Intelligent Modelling & Analysis Group (IMA) and Lab for @code{UnCertainty} In
Data and decision making (LUCID), University of Nottingham. A big thank you to
the many people who have contributed to the development/evaluation of the
toolbox. Please cite the toolbox and the corresponding paper
<doi:10.1109/FUZZ48607.2020.9177780> when using it. More related papers can be
found in the NEWS.")
(license license:gpl2+)))
(define-public r-fuzzyq
(package
(name "r-fuzzyq")
(version "0.1.0")
(source
(origin
(method url-fetch)
(uri (cran-uri "FuzzyQ" version))
(sha256
(base32 "1camdw7rnzf02nqjlmkr7mqpc38dafq9nw8x7pzwi1lh5xwv5dia"))))
(properties `((upstream-name . "FuzzyQ")))
(build-system r-build-system)
(propagated-inputs (list r-cluster))
(home-page "https://ligophorus.github.io/FuzzyQ/")
(synopsis "Fuzzy Quantification of Common and Rare Species")
(description
"Fuzzy clustering of species in an ecological community as common or rare based
on their abundance and occupancy. It also includes functions to compute
confidence intervals of classification metrics and plot results. See Balbuena
et al. (2020, <doi:10.1101/2020.08.12.247502>).")
(license license:gpl3)))
(define-public r-fuzzypovertyr
(package
(name "r-fuzzypovertyr")
(version "2.0.1")
(source
(origin
(method url-fetch)
(uri (cran-uri "FuzzyPovertyR" version))
(sha256
(base32 "1v5vnk8r7vhwgmp5hbpwzn0cks4i8z3jm7r3rbbdfjjf4fba2vkd"))))
(properties `((upstream-name . "FuzzyPovertyR")))
(build-system r-build-system)
(propagated-inputs (list r-tidyr r-reshape2 r-ggplot2 r-ecp r-dplyr))
(native-inputs (list r-knitr))
(home-page "https://cran.r-project.org/package=FuzzyPovertyR")
(synopsis "Estimation of Fuzzy Poverty Measures")
(description
"Estimates fuzzy measures of poverty and deprivation. It also estimates the
sampling variance of these measures using bootstrap or jackknife repeated
replications.")
(license license:expat)))
(define-public r-fuzzynumbers-ext-2
(package
(name "r-fuzzynumbers-ext-2")
(version "3.2")
(source
(origin
(method url-fetch)
(uri (cran-uri "FuzzyNumbers.Ext.2" version))
(sha256
(base32 "0gldq0bg1p1vmrn35prha44d7lyymz0jzshdyp2c5rx433mny7h5"))))
(properties `((upstream-name . "FuzzyNumbers.Ext.2")))
(build-system r-build-system)
(propagated-inputs (list r-fuzzynumbers))
(home-page "https://cran.r-project.org/package=FuzzyNumbers.Ext.2")
(synopsis "Apply Two Fuzzy Numbers on a Monotone Function")
(description
"One can easily draw the membership function of f(x,y) by package
@code{FuzzyNumbers.Ext.2} in which f(.,.) is supposed monotone and x and y are
two fuzzy numbers. This work is possible using function f2apply() which is an
extension of function fapply() from Package @code{FuzzyNumbers} for two-variable
monotone functions. Moreover, this package has the ability of computing the
core, support and alpha-cuts of the fuzzy-valued final result.")
(license license:lgpl3+)))
(define-public r-fuzzynumbers
(package
(name "r-fuzzynumbers")
(version "0.4-7")
(source
(origin
(method url-fetch)
(uri (cran-uri "FuzzyNumbers" version))
(sha256
(base32 "12xficdsln31rzfziycw6z0912cgrq6mkvz3f1nbli9lzqrypxzl"))))
(properties `((upstream-name . "FuzzyNumbers")))
(build-system r-build-system)
(native-inputs (list r-knitr))
(home-page "https://github.com/gagolews/FuzzyNumbers/")
(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.")
(license license:lgpl3+)))
(define-public r-fuzzym
(package
(name "r-fuzzym")
(version "0.1.0")
(source
(origin
(method url-fetch)
(uri (cran-uri "FuzzyM" version))
(sha256
(base32 "12arx4flgykdkynnj341pgpd85k7xh6491bv9v9da63w6rp0kdm6"))))
(properties `((upstream-name . "FuzzyM")))
(build-system r-build-system)
(home-page "https://cran.r-project.org/package=FuzzyM")
(synopsis "Fuzzy Cognitive Maps Operations")
(description
"This package contains functions for operations with fuzzy cognitive maps using
t-norm and s-norm operators. T-norms and S-norms are described by Dov M. Gabbay
and George Metcalfe (2007) <doi:10.1007/s00153-007-0047-1>. System indicators
are described by Cox, Earl D. (1995) <isbn:1886801010>. Executable examples are
provided in the \"inst/examples\" folder.")
(license license:expat)))
(define-public r-fuzzylp
(package
(name "r-fuzzylp")
(version "0.1-7")
(source
(origin
(method url-fetch)
(uri (cran-uri "FuzzyLP" version))
(sha256
(base32 "1x584h5a82npxk1jhz0mnn8zkyfd8qvvvhkrv3bfi81w2l893hi7"))))
(properties `((upstream-name . "FuzzyLP")))
(build-system r-build-system)
(propagated-inputs (list r-roi-plugin-glpk r-roi r-fuzzynumbers))
(native-inputs (list r-r-rsp))
(home-page "https://github.com/olbapjose/FuzzyLP")
(synopsis "Fuzzy Linear Programming")
(description
"This package provides methods to solve Fuzzy Linear Programming Problems with
fuzzy constraints (following different approaches proposed by Verdegay,
Zimmermann, Werners and Tanaka), fuzzy costs, and fuzzy technological matrix.")
(license license:gpl3+)))
(define-public r-fuzzyjoin
(package
(name "r-fuzzyjoin")
(version "0.1.6")
(source
(origin
(method url-fetch)
(uri (cran-uri "fuzzyjoin" version))
(sha256
(base32 "0s5rhqz8vih4za3a8k1k7i3gq8hj0w7bqnakw40k6mg87jvyzsj7"))))
(properties `((upstream-name . "fuzzyjoin")))
(build-system r-build-system)
(propagated-inputs (list r-tidyr
r-tibble
r-stringr
r-stringdist
r-purrr
r-geosphere
r-dplyr))
(native-inputs (list r-knitr))
(home-page "https://github.com/dgrtwo/fuzzyjoin")
(synopsis "Join Tables Together on Inexact Matching")
(description
"Join tables together based not on whether columns match exactly, but whether
they are similar by some comparison. Implementations include string distance
and regular expression matching.")
(license license:expat)))
(define-public r-fuzzyforest
(package
(name "r-fuzzyforest")
(version "1.0.8")
(source
(origin
(method url-fetch)
(uri (cran-uri "fuzzyforest" version))
(sha256
(base32 "0sb7qia01a5h6p12riq6vdq9likmqb6i949axsxszy5sf1zk50v5"))))
(properties `((upstream-name . "fuzzyforest")))
(build-system r-build-system)
(propagated-inputs (list r-randomforest r-mvtnorm r-ggplot2 r-foreach
r-doparallel))
(home-page "https://cran.r-project.org/package=fuzzyforest")
(synopsis "Fuzzy Forests")
(description
"Fuzzy forests, a new algorithm based on random forests, is designed to reduce
the bias seen in random forest feature selection caused by the presence of
correlated features. Fuzzy forests uses recursive feature elimination random
forests to select features from separate blocks of correlated features where the
correlation within each block of features is high and the correlation between
blocks of features is low. One final random forest is fit using the surviving
features. This package fits random forests using the @code{randomForest}
package and allows for easy use of WGCNA to split features into distinct blocks.
See D. Conn, Ngun, T., C. Ramirez, and G. Li (2019) <doi:10.18637/jss.v091.i09>
for further details.")
(license license:gpl3)))
(define-public r-fuzzydbscan
(package
(name "r-fuzzydbscan")
(version "0.0.3")
(source
(origin
(method url-fetch)
(uri (cran-uri "FuzzyDBScan" version))
(sha256
(base32 "1a974s5j03caifcypk1c7la96754gh6n20xak7qaf9hrbm5i7vr9"))))
(properties `((upstream-name . "FuzzyDBScan")))
(build-system r-build-system)
(propagated-inputs (list r-r6 r-ggplot2 r-dbscan r-data-table r-checkmate))
(home-page "https://cran.r-project.org/package=FuzzyDBScan")
(synopsis "Run and Predict a Fuzzy DBScan")
(description
"An interface for training Fuzzy DBScan with both Fuzzy Core and Fuzzy Border.
Therefore, the package provides a method to initialize and run the algorithm and
a function to predict new data w.t.h. of R6'. The package is build upon the
paper \"Fuzzy Extensions of the DBScan algorithm\" from Ienco and Bordogna (2018)
<doi:10.1007/s00500-016-2435-0>. A predict function assigns new data according
to the same criteria as the algorithm itself. However, the prediction function
freezes the algorithm to preserve the trained cluster structure and treats each
new prediction object individually.")
(license license:lgpl3)))
(define-public r-fuzzyclass
(package
(name "r-fuzzyclass")
(version "0.1.6")
(source
(origin
(method url-fetch)
(uri (cran-uri "FuzzyClass" version))
(sha256
(base32 "1ld40qaiig743x21iwzpiabc1ky72hqrdnh855wfgvkmln9jc9by"))))
(properties `((upstream-name . "FuzzyClass")))
(build-system r-build-system)
(propagated-inputs (list r-trapezoid
r-rootsolve
r-rdpack
r-purrr
r-mvtnorm
r-mass
r-foreach
r-envstats
r-e1071
r-doparallel
r-catools))
(native-inputs (list r-knitr))
(home-page "https://github.com/leapigufpb/FuzzyClass")
(synopsis "Fuzzy and Non-Fuzzy Classifiers")
(description
"It provides classifiers which can be used for discrete variables and for
continuous variables based on the Naive Bayes and Fuzzy Naive Bayes hypothesis.
Those methods were developed by researchers belong to the Laboratory of
Technologies for Virtual Teaching and Statistics (@code{LabTEVE}) and Laboratory
of Applied Statistics to Image Processing and Geoprocessing (LEAPIG) at Federal
University of Paraiba, Brazil'. They considered some statistical distributions
and their papers were published in the scientific literature, as for instance,
the Gaussian classifier using fuzzy parameters, proposed by Moraes, Ferreira and
Machado (2021) <doi:10.1007/s40815-020-00936-4>.")
(license license:expat)))
(define-public r-fuzzyahp
(package
(name "r-fuzzyahp")
(version "0.9.5")
(source
(origin
(method url-fetch)
(uri (cran-uri "FuzzyAHP" version))
(sha256
(base32 "02sx32vlvnr0fzw8rf0f6hiikqn7xp1ibpqzxhxwv8yij2qkiyci"))))
(properties `((upstream-name . "FuzzyAHP")))
(build-system r-build-system)
(propagated-inputs (list r-mass))
(native-inputs (list r-knitr))
(home-page "http://github.com/JanCaha/FuzzyAHP/")
(synopsis "(Fuzzy) AHP Calculation")
(description
"Calculation of AHP (Analytic Hierarchy Process -
<http://en.wikipedia.org/wiki/Analytic_hierarchy_process>) with classic and
fuzzy weights based on Saaty's pairwise comparison method for determination of
weights.")
(license license:lgpl3+)))
(define-public r-fuzzy-p-value
(package
(name "r-fuzzy-p-value")
(version "1.1")
(source
(origin
(method url-fetch)
(uri (cran-uri "Fuzzy.p.value" version))
(sha256
(base32 "13h6armh9g57zqxyjqk6mq81jlfqxqrg2sb5p9rrhslka5m01zis"))))
(properties `((upstream-name . "Fuzzy.p.value")))
(build-system r-build-system)
(propagated-inputs (list r-fuzzynumbers))
(home-page "https://cran.r-project.org/package=Fuzzy.p.value")
(synopsis "Computing Fuzzy p-Value")
(description
"The main goal of this package is drawing the membership function of the fuzzy
p-value which is defined as a fuzzy set on the unit interval for three following
problems: (1) testing crisp hypotheses based on fuzzy data, (2) testing fuzzy
hypotheses based on crisp data, and (3) testing fuzzy hypotheses based on fuzzy
data. In all cases, the fuzziness of data or/and the fuzziness of the boundary
of null fuzzy hypothesis transported via the p-value function and causes to
produce the fuzzy p-value. If the p-value is fuzzy, it is more appropriate to
consider a fuzzy significance level for the problem. Therefore, the comparison
of the fuzzy p-value and the fuzzy significance level is evaluated by a fuzzy
ranking method in this package.")
(license license:lgpl3+)))
(define-public r-fuzzr
(package
(name "r-fuzzr")
(version "0.2.2")
(source
(origin
(method url-fetch)
(uri (cran-uri "fuzzr" version))
(sha256
(base32 "1cwq7a5j6lzrlz9dw3hsfap988rh1kkgf03yni7c33zl69xp5w77"))))
(properties `((upstream-name . "fuzzr")))
(build-system r-build-system)
(propagated-inputs (list r-purrr r-progress r-assertthat))
(native-inputs (list r-knitr))
(home-page "https://github.com/mdlincoln/fuzzr")
(synopsis "Fuzz-Test R Functions")
(description
"Test function arguments with a wide array of inputs, and produce reports
summarizing messages, warnings, errors, and returned values.")
(license license:expat)))
(define-public r-future-tests
(package
(name "r-future-tests")
(version "0.7.0")
(source
(origin
(method url-fetch)
(uri (cran-uri "future.tests" version))
(sha256
(base32 "10g1w99xqr7l0cn27642aphqcvfidgpas38f84r815yy6k1ryrlx"))))
(properties `((upstream-name . "future.tests")))
(build-system r-build-system)
(propagated-inputs (list r-sessioninfo r-prettyunits r-future r-crayon
r-cli))
(home-page "https://future.tests.futureverse.org")
(synopsis "Test Suite for 'Future API' Backends")
(description