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authorAniket Patil <aniket112.patil@gmail.com>2020-12-14 21:57:33 -0500
committerLeo Famulari <leo@famulari.name>2020-12-15 00:33:24 -0500
commit795f654b2a0593d2a09273fc7e39d7258c87d548 (patch)
tree6c1a3e275f96d76daf28abb31e86dea919bfb010
parentbd740f77ffa0511aded249f4ed32c48f682f3852 (diff)
gnu: Add r-decon.
* gnu/packages/cran.scm (r-decon): New variable. Signed-off-by: Leo Famulari <leo@famulari.name>
-rw-r--r--gnu/packages/cran.scm38
1 files changed, 38 insertions, 0 deletions
diff --git a/gnu/packages/cran.scm b/gnu/packages/cran.scm
index 45703792ed..157ac82fd0 100644
--- a/gnu/packages/cran.scm
+++ b/gnu/packages/cran.scm
@@ -32,6 +32,7 @@
;;; Copyright © 2020 Arun Isaac <arunisaac@systemreboot.net>
;;; Copyright © 2020 Magali Lemes <magalilemes00@gmail.com>
;;; Copyright © 2020 Simon Tournier <zimon.toutoune@gmail.com>
+;;; Copyright © 2020 Aniket Patil <aniket112.patil@gmail.com>
;;;
;;; This file is part of GNU Guix.
;;;
@@ -25174,6 +25175,43 @@ orthogonal coordinate systems: cartesian, polar, spherical, cylindrical,
parabolic or user defined by custom scale factors.")
(license license:gpl3)))
+(define-public r-decon
+ (package
+ (name "r-decon")
+ (version "1.2-4")
+ (source
+ (origin
+ (method url-fetch)
+ (uri (cran-uri "decon" version))
+ (sha256
+ (base32
+ "1v4l0xq29rm8mks354g40g9jxn0didzlxg3g7z08m0gvj29zdj7s"))))
+ (properties `((upstream-name . "decon")))
+ (build-system r-build-system)
+ (native-inputs
+ `(("gfortran" ,gfortran)))
+ (home-page
+ "https://cran.r-project.org/web/packages/decon/")
+ (synopsis "Deconvolution Estimation in Measurement Error Models")
+ (description
+ "This package contains a collection of functions to deal with
+nonparametric measurement error problems using deconvolution
+kernel methods. We focus two measurement error models in the
+package: (1) an additive measurement error model, where the
+goal is to estimate the density or distribution function from
+contaminated data; (2) nonparametric regression model with
+errors-in-variables. The R functions allow the measurement errors
+to be either homoscedastic or heteroscedastic. To make the
+deconvolution estimators computationally more efficient in R,
+we adapt the \"Fast Fourier Transform\" (FFT) algorithm for
+density estimation with error-free data to the deconvolution
+kernel estimation. Several methods for the selection of the
+data-driven smoothing parameter are also provided in the package.
+See details in: Wang, X.F. and Wang, B. (2011). Deconvolution
+estimation in measurement error models: The R package decon.
+Journal of Statistical Software, 39(10), 1-24.")
+ (license license:gpl3+)))
+
(define-public r-aws-signature
(package
(name "r-aws-signature")