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diff --git a/math/Makefile b/math/Makefile
--- a/math/Makefile
+++ b/math/Makefile
@@ -52,6 +52,7 @@
SUBDIR += R-cran-combinat
SUBDIR += R-cran-conf.design
SUBDIR += R-cran-conquer
+ SUBDIR += R-cran-corpcor
SUBDIR += R-cran-cvar
SUBDIR += R-cran-date
SUBDIR += R-cran-ddalpha
diff --git a/math/R-cran-corpcor/Makefile b/math/R-cran-corpcor/Makefile
new file mode 100644
--- /dev/null
+++ b/math/R-cran-corpcor/Makefile
@@ -0,0 +1,14 @@
+PORTNAME= corpcor
+DISTVERSION= 1.6.10
+CATEGORIES= math
+DISTNAME= ${PORTNAME}_${DISTVERSION}
+
+MAINTAINER= longros@FreeBSD.org
+COMMENT= Efficient estimation of covariance and partial correlation
+WWW= https://strimmerlab.github.io/software/corpcor/
+
+LICENSE= GPLv3+
+
+USES= cran:auto-plist
+
+.include <bsd.port.mk>
diff --git a/math/R-cran-corpcor/distinfo b/math/R-cran-corpcor/distinfo
new file mode 100644
--- /dev/null
+++ b/math/R-cran-corpcor/distinfo
@@ -0,0 +1,3 @@
+TIMESTAMP = 1784139308
+SHA256 (corpcor_1.6.10.tar.gz) = 71a04c503c93ec95ddde09abe8c7ddeb36175b7da76365a14b27066383e10e09
+SIZE (corpcor_1.6.10.tar.gz) = 22678
diff --git a/math/R-cran-corpcor/pkg-descr b/math/R-cran-corpcor/pkg-descr
new file mode 100644
--- /dev/null
+++ b/math/R-cran-corpcor/pkg-descr
@@ -0,0 +1,12 @@
+Implements a James-Stein-type shrinkage estimator for the covariance
+matrix, with separate shrinkage for variances and correlations. The
+approach is both computationally as well as statistically very efficient,
+it is applicable to "small n, large p" data, and always returns a positive
+definite and well-conditioned covariance matrix. In addition to inferring
+the covariance matrix the package also provides shrinkage estimators for
+partial correlations and partial variances. The inverse of the covariance
+and correlation matrix can be efficiently computed, as well as any
+arbitrary power of the shrinkage correlation matrix. Furthermore,
+functions are available for fast singular value decomposition, for
+computing the pseudoinverse, and for checking the rank and positive
+definiteness of a matrix.

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