version 1.3

cran · May 13, 2022 · da7e8d5 · da7e8d5
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,15 +1,15 @@
 Package: hglasso
 Type: Package
-Title: Learning graphical models with hubs
-Version: 1.2
-Date: 2014-08-09
+Title: Learning Graphical Models with Hubs
+Version: 1.3
+Date: 2022-05-13
 Author: Kean Ming Tan
-Maintainer: Kean Ming Tan <keanming@uw.edu>
+Maintainer: Kean Ming Tan <keanming@umich.edu>
 Depends: glasso, mvtnorm, igraph
 Imports: fields
-Description: Implements the hub graphical lasso and hub covariance graph proposal by Tan, KM., London, P., Mohan, K., Lee, S-I., Fazel, M., and Witten, D. (2014). Learning graphical models with hubs. To appear in Journal of Machine Learning Research. arXiv.org/pdf/1402.7349.pdf.
+Description: Implements the hub graphical lasso and hub covariance graph proposal by Tan, KM., London, P., Mohan, K., Lee, S-I., Fazel, M., and Witten, D. (2014). Learning graphical models with hubs. Journal of Machine Learning Research 15(Oct):3297-3331.
 License: GPL (>= 2)
-Packaged: 2014-08-09 22:11:27 UTC; keanmingtan
 NeedsCompilation: no
+Packaged: 2022-05-13 04:58:47 UTC; keanmingtan
 Repository: CRAN
-Date/Publication: 2014-08-10 00:32:51
+Date/Publication: 2022-05-13 08:20:02 UTC
diff --git a/MD5 b/MD5
@@ -1,11 +1,10 @@
-e1fce17913287ac81a68362f66b36879 *DESCRIPTION
-1e3f81ba1e3250422aca5ff9b6a737a3 *NAMESPACE
-e04cebdff2d589bdb16ae7d2b4ab1f3d *R/Functions.R
+7aeb7d6d05dbbf101648c9334bdd83e5 *DESCRIPTION
+789bf98a8c77ebb634a65d0ea7f21b40 *NAMESPACE
+18cdbb38297cefb0344d13911f45325a *R/Functions.R
 a1b193525f60917367233ffbe841749d *R/HubNetwork.R
 286c86156264dcb20e2fcbe5d8eb2d14 *R/binaryMCMC.R
 da835bbabe4f0f25a6cf9306047ab437 *R/hbn.R
 e9d17da41b4772456ffb89ac6f2d36c4 *R/hcov.R
-9685ff65f77e90194979e2ffcb832e0c *R/hglasso-internal.R
 3f24b364889601edcf0193c63234c4e2 *R/hglasso.R
 4331294581bef2f15170e6c313a2227f *R/hglassoBIC.R
 45157485e9677bf3f96ef84369b9167f *R/image.hbn.R
@@ -24,9 +23,9 @@ c11bcd6700ffad1e4b93aa016210f012 *man/HubNetwork.Rd
 7cf66d100724936c610a09d020bc1306 *man/binaryMCMC.Rd
 7cebeac8d620ff5fe145f61f0f8cc047 *man/hbn.Rd
 77b0d875a899bf2ecf113c72a07f13d1 *man/hcov.Rd
-109fe9d536ab33c21da768ae769f03d1 *man/hglasso-package.Rd
+2164e9b1f05b17898da365057b8e14d1 *man/hglasso-package.Rd
 ca11df65ca07358245b87905e0d63224 *man/hglasso.Rd
 b6acf11f7568bca3f1cb431fd146591e *man/hglassoBIC.Rd
 3cd7dec482b4560903ec880032743e31 *man/image.hglasso.Rd
 40f4d6edaeffe226da787d4afd79f0a1 *man/plot.hglasso.Rd
-58eb628373569fb38f740d556f675083 *man/summary.hglasso.Rd
+dd52d44607b1b2fd3c44929e1e4b6e46 *man/summary.hglasso.Rd
diff --git a/NAMESPACE b/NAMESPACE
@@ -11,8 +11,11 @@ S3method(plot, hbn)
 S3method(image, hbn)
 S3method(print, hbn)
 S3method(summary, hbn)
-
 import(fields)
 import(igraph)
 import(glasso)
-import(mvtnorm)
+import(mvtnorm)
+
+importFrom("grDevices", "col2rgb", "dev.new", "rgb")
+importFrom("graphics", "image", "par")
+importFrom("stats", "cov2cor", "rbinom", "runif")
diff --git a/R/Functions.R b/R/Functions.R
@@ -119,7 +119,7 @@ updateTheta <- function(tildeTheta,W1,S,rho){
   a <- eigen(C)
   D <- diag(a$values)
   U <- a$vectors
-  Theta <- 1/2*U%*%(D+sqrt(D*D+4/rho*diag(rep(1,nrow(S)))))%*%t(U)		
+  Theta <- 1/2*U%*%(D+sqrt(D*D+4/rho*diag(rep(1,nrow(S)))))%*%t(U)			
   return(Theta)
 }
 

diff --git a/R/hglasso-internal.R b/R/hglasso-internal.R
diff --git a/man/hglasso-package.Rd b/man/hglasso-package.Rd
@@ -48,7 +48,7 @@ Maintainer: Kean Ming Tan <keanming@uw.edu>
 
 }
 \references{
-Tan, KM., London, P., Mohan, K., Lee, S-I., Fazel, M., and Witten, D. (2014). Learning graphical models with hubs. To appear in Journal of Machine Learning Research. arXiv.org/pdf/1402.7349.pdf.
+Tan, KM., London, P., Mohan, K., Lee, S-I., Fazel, M., and Witten, D. (2014). Learning graphical models with hubs. Journal of Machine Learning Research 15(Oct):3297-3331.
 }
 \keyword{ package }
 

diff --git a/man/summary.hglasso.Rd b/man/summary.hglasso.Rd
@@ -27,7 +27,7 @@ any other arguments passed to \code{print}.
 Some information for an object hglasso, hcov, or hbn: (1) The number of observations n and the number of features p.  (2) The number of edges in Theta, V, and Z. (3) The indices for hub nodes, and also the number of edges within each hub node.}
 
 \references{
-Tan et al. (2014). Learning graphical models with hubs. To appear in Journal of Machine Learning Research.  arXiv.org/pdf/1402.7349.pdf.
+Tan et al. (2014). Learning graphical models with hubs. Journal of Machine Learning Research 15(Oct):3297-3331.
 }
 \author{
 Kean Ming Tan