[Top][All Lists]

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

[bug#44033] [PATCH 1/3] gnu: Add r-decon.

From: Aniket Patil
Subject: [bug#44033] [PATCH 1/3] gnu: Add r-decon.
Date: Fri, 16 Oct 2020 18:43:36 +0530

* gnu/packages/cran.scm (r-decon): New variable.
 gnu/packages/cran.scm | 23 +++++++++++++++++++++++
 1 file changed, 23 insertions(+)

diff --git a/gnu/packages/cran.scm b/gnu/packages/cran.scm
index 59a409f8e9..0370cdd993 100644
--- a/gnu/packages/cran.scm
+++ b/gnu/packages/cran.scm
@@ -24596,3 +24596,26 @@ enrichment analysis (GSEA) calculation with or without 
the absolute filtering.
   Without filtering, users can perform (original) two-tailed or one-tailed
 absolute GSEA.")
     (license license:gpl2)))
+(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
+      "";)
+    (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), 
+    (license license:gpl3+)))

reply via email to

[Prev in Thread] Current Thread [Next in Thread]