pimi wrote 7 years ago
(address . guix-patches@gnu.org)(name . pimi)(address . madalinionel.patrascu@mdc-berlin.de)
20180910112138.24192-1-madalinionel.patrascu@mdc-berlin.de
gnu/packages/cran.scm (r-abodoutlier): New variable.
---
gnu/packages/cran.scm | 28 ++++++++++++++++++++++++++++
1 file changed, 28 insertions(+)
Toggle diff (38 lines)
diff --git a/gnu/packages/cran.scm b/gnu/packages/cran.scm
index 814439172..962b82168 100644
--- a/gnu/packages/cran.scm
+++ b/gnu/packages/cran.scm
@@ -5564,3 +5564,31 @@ gf_density(), and so on, bring the formula interface to ggplot(). This captures
and extends the excellent simplicity of the lattice-graphics formula interface,
while providing the intuitive capabilities of @code{r-ggplot2}.")
(license license:expat)))
+
+(define-public r-abodoutlier
+ (package
+ (name "r-abodoutlier")
+ (version "0.1")
+ (source
+ (origin
+ (method url-fetch)
+ (uri (cran-uri "abodOutlier" version))
+ (sha256
+ (base32 "1pvhgxmh23br84r0fbmv7g53z2427birdja96a67vqgz18r3fdvj"))))
+ (properties `((upstream-name . "abodOutlier")))
+ (build-system r-build-system)
+ (propagated-inputs
+ `(("r-cluster" ,r-cluster)))
+ (home-page "https://cran.r-project.org/web/packages/abodOutlier/")
+ (synopsis "Angle-based outlier detection")
+ (description
+ "This package performs angle-based outlier detection on a given data frame.
+ The @code{r-abodoutlier} package offers three methods to process data:
+@enumerate
+@item - full but slow implementation using all the data that has cubic
+complexity;
+@item - a fully randomized method;
+@item - a method using k-nearest neighbours.
+@end enumerate
+ These algorithms are well suited for high dimensional data outlier detection.")
+ (license license:expat)))
--
2.17.1