diff --git a/DESCRIPTION b/DESCRIPTION
index 6ec54f0..f696180 100755
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,15 +1,11 @@
 Package: rrBLUP
 Title: Ridge regression and other kernels for genomic selection
-Version: 2.0
-Author: Jeffrey Endelman
+Version: 2.1
+Author: Jeffrey Endelman 
 Maintainer: Jeffrey Endelman <j.endelman@gmail.com>
-Description: One application is to estimate marker effects by ridge
-        regression; alternatively, BLUPs can be calculated based on
-        kinship.  The genetic correlation between lines can be modeled
-        using a marker-based, additive relationship matrix, or via
-        Gaussian and exponential kernels.
+Description: One application is to estimate marker effects by ridge regression; alternatively, BLUPs can be calculated based on kinship.  The genetic correlation between lines can be modeled using a marker-based, additive relationship matrix, or via Gaussian and exponential kernels.  
 License: GPL-3
 LazyLoad: yes
-Packaged: 2011-10-14 14:59:11 UTC; Jeffrey
+Packaged: 2011-10-31 21:20:54 UTC; Jeffrey
 Repository: CRAN
-Date/Publication: 2011-10-14 15:44:02
+Date/Publication: 2011-11-02 17:26:39
diff --git a/MD5 b/MD5
index 3c5a20d..4a802e0 100644
--- a/MD5
+++ b/MD5
@@ -1,9 +1,9 @@
-7af1f4caf6c3c81eb07a8b2008636908 *DESCRIPTION
+a2ed011b73eef5123e892e8d0764d52a *DESCRIPTION
 d8b658bc9e3390e6e6d0539c702e4597 *NAMESPACE
 ed40c1ee328c5c1a360f8f19e1c3da6f *R/GWA.R
 1eb18cf7496f7a40675c50be85207f5b *R/impute.R
 12d0dfead32ae78381bb56fe1b09573e *R/kinship.BLUP.R
-58a7f4eea932326f53e2e339141ab8d1 *R/mixed.solve.R
+cff27224f8e9b458f5b4cd69af2a3d7a *R/mixed.solve.R
 be5fbfb20ec993da916e34e007aabbe0 *man/GWA.Rd
 5f2221a320a663d862e94a88b6b6f8db *man/impute.Rd
 d54199efda6d19e1581779c7d67bf109 *man/kinship.BLUP.Rd
diff --git a/R/mixed.solve.R b/R/mixed.solve.R
index c3f3acf..a613a7e 100755
--- a/R/mixed.solve.R
+++ b/R/mixed.solve.R
@@ -118,14 +118,14 @@ if (!SE) {
     list(Vu = Vu.opt, Ve = Ve.opt, beta = as.vector(beta), u = as.vector(u), LL = LL)
   }
 } else {
-  beta.var <- Vu.opt * solve(W)
-  beta.SE <- sqrt(diag(beta.var))
+  Winv <- solve(W)
+  beta.SE <- sqrt(Vu.opt*diag(Winv))
   WW <- tcrossprod(KZt.Hinv,KZt)
   WWW <- KZt.Hinv%*%X
   if (is.null(K)) {
-	u.SE <- sqrt(Vu.opt * (rep(1,m) - diag(WW) + diag(tcrossprod(WWW%*%beta.var,WWW))))	
+	u.SE <- sqrt(Vu.opt * (rep(1,m) - diag(WW) + diag(tcrossprod(WWW%*%Winv,WWW))))	
   } else {
-	u.SE <- sqrt(Vu.opt * (diag(K) - diag(WW) + diag(tcrossprod(WWW%*%beta.var,WWW))))
+	u.SE <- sqrt(Vu.opt * (diag(K) - diag(WW) + diag(tcrossprod(WWW%*%Winv,WWW))))
   }
   if (return.Hinv) {
     list(Vu = Vu.opt, Ve = Ve.opt, beta = as.vector(beta), beta.SE = as.vector(beta.SE), u = as.vector(u), u.SE = as.vector(u.SE), LL = LL, Hinv = Hinv)