version 2.4.3

CHANGELOG

@@ -1,3 +1,5 @@
+2014/01    removed inv_sigma02 term from omega2 update (thanks to Robin Gong). 
+2014/01    fixed bug in use of start function when using largest connected component only and edge covariates
 2013/10    checked for leaks using AddressSanitizer, moved some Depends to Imports
 2013/05    now if mclust fails Fruchterman-Reingold is (re)called until mclust can find clusters. 
 2013/05    changed setting of seed. A single set.seed is now used. 

DESCRIPTION

@@ -1,18 +1,20 @@
 Package: VBLPCM
 Type: Package
 Title: Variational Bayes Latent Position Cluster Model for networks.
-Version: 2.4.2
-Date: 2013-10-31
+Version: 2.4.3
+Date: 2014-03-07
 Author: Michael Salter-Townshend
 Maintainer: Michael Salter-Townshend <mike.saltertownshend@gmail.com>
 Description: A package to fit and simulate latent position and cluster
         models for statistical networks using a Variational Bayes approximation.
 Depends: ergm, sna
 Imports: mclust
 SystemRequirements: Gnu Scientific Library version >= 1.12
+URL: http://www.r-project.org,
+        https://sites.google.com/site/mikesaltertownshend/software/
 License: GPL (>= 2)
 LazyLoad: yes
-Packaged: 2013-10-31 11:45:59 UTC; mst
+Packaged: 2014-03-07 15:13:08 UTC; salter
 NeedsCompilation: yes
 Repository: CRAN
-Date/Publication: 2013-10-31 14:00:51
+Date/Publication: 2014-03-07 16:31:03

MD5

@@ -1,5 +1,5 @@
-7b700f1100f4f02975440441b77fc806 *CHANGELOG
-b77f654729f3b2c6e438b2b07580c9dd *DESCRIPTION
+47c4ad357b789a41e51e3d1b0c1e0e75 *CHANGELOG
+c405b14618ed92c0c3d53a3530a3cee7 *DESCRIPTION
 c771c3b32ace90453b25137ffb8b2230 *NAMESPACE
 93ddbebf082e5c5a3d09d70b0ba310af *R/KL.R
 85b49f0298acd5832328bf7056a7d89a *R/VBLPCM.R
@@ -12,11 +12,11 @@ c771c3b32ace90453b25137ffb8b2230 *NAMESPACE
 6d0b56049f54c59dbe71754983a9a80d *R/predict.R
 203861162888e86a84e9b20d62f2d8ba *R/print.vblpcm.R
 32ef7505c44b2245b165b30aa1ebf816 *R/roc.R
-cbc3892e84de2ad70d53419d1f5b66e2 *R/start.R
+74999cc02889948d93e218447e9882d0 *R/start.R
 4f69c86b26fbc64b90e83eb39a6ac450 *R/summarise_groups.R
 054e8f388ac9c8373ab5b6e66818754e *R/summary.vblpcm.R
 31c8ccdf29df94b0f5f9508fcb1a7e9e *R/vb_bic.R
-63614bbf06782761aa98e6a8896385ef *R/zzz.R
+254ef73e0dbf2d89e737a0b51310b828 *R/zzz.R
 1215b49b49d2dcb0f996500bd0e7c5ce *configure
 654b38e8f3726e7c9613425a5b50bbd8 *configure.ac
 013778ff539b989141783da07f3ddd03 *data/aids.rda
@@ -25,7 +25,7 @@ cbc3892e84de2ad70d53419d1f5b66e2 *R/start.R
 9beed6138652a9e4ebedd2cb1f270acb *inst/CITATION
 4ccabdb12a6c7592d26cceab34755ee0 *man/E_to_Y.Rd
 62b220ff6a4b95449f21d7eabbbd5848 *man/VBLPCM-internal.Rd
-8d1dca5c8575cf5ebd1fa4bb26a50c8d *man/VBLPCM-package.Rd
+fc35fed5ec4639c8185242377165c745 *man/VBLPCM-package.Rd
 1d21ac5b8c3d8909d4782465e94cec14 *man/Y_to_E.Rd
 0eb794fe9b2faea92c95f0146560ac1a *man/Y_to_M.Rd
 a3d9e7566f96741ba738afda6cade6d7 *man/Y_to_nonE.Rd
@@ -48,10 +48,10 @@ faf7bff0082bb61e117b9d83441fcd3a *man/vblpcmfit.Rd
 0168242a2c896382b916a70d449451e6 *man/vblpcmgroups.Rd
 5c4be52f6272def826fa69501171c448 *man/vblpcmroc.Rd
 6b4b558ac60b6a284b02bd5295fb6ee9 *man/vblpcmstart.Rd
-acaeca43833c3432693fe5f9a6149bf9 *src/KL_funcs.c
+b590337ea2d7b3537ddf0f0af08451fb *src/KL_funcs.c
 0cacb14c872b3c9d87e81ab3bb4ce9b5 *src/Makevars.in
 db26274171b9a08ecd0cc28116f5a6f5 *src/Makevars.win
-6de979e4a8336b9dd9a29b760ae19c2d *src/VBLPCM.c
+a62a6fd799c80e8645aa4c8a759a8edf *src/VBLPCM.c
 f817a5918c432421b21e0be0475d4861 *src/adjacency_to_edges.c
 0ce87e3ee8377add5fec4f42df2bb544 *src/bb.c
 61a39c5900a8b02859418ac8e209e974 *src/fruchterman_reingold.c

R/start.R

@@ -69,6 +69,8 @@ vblpcmstart<-function(g.network, G=1, d=2, LSTEPS=5e3, model="plain", CLUST=0, B
       receivecovs<-as.matrix(receivecovs[tmp,])
     if (!is.null(socialcovs))
       socialcovs<-as.matrix(socialcovs[tmp,])
+    if (!is.null(edgecovs))
+      edgecovs<-c(matrix(edgecovs,N)[tmp,tmp])
     g.network<-network(as.sociomatrix(all_g.network)[(1:N)[tmp],(1:N)[tmp]],directed=is.directed(all_g.network))
     for (att in list.vertex.attributes(all_g.network))
       set.vertex.attribute(g.network,att,get.vertex.attribute(all_g.network,att)[tmp])

R/zzz.R

@@ -5,8 +5,8 @@
 .onAttach <- function(lib,pkg) {
     packageStartupMessage("\n\n")
     packageStartupMessage(paste("\n", "Variational Bayes Latent Position Cluster Model for networks.","\n"),
-        paste("VBLPCM", "Version", "2.4.2", 
-        "Created on", "2013-05-22"), paste("\n Created and maintained by ", "Michael Salter-Townshend"), "\n")
+        paste("VBLPCM", "Version", "2.4.3", 
+        "Created on", "2014-01-29"), paste("\n Created and maintained by ", "Michael Salter-Townshend"), "\n")
     packageStartupMessage('For citation information type \'citation("VBLPCM")\'\n')
     packageStartupMessage('Type \'help(VBLPCM)\' to get started.\n')
     packageStartupMessage('Some worked examples are given by \'example(VBLPCM)\' \n')

man/VBLPCM-package.Rd

@@ -11,8 +11,8 @@ Variational Bayes approximation to the posterior for the Latent Position Cluster
 \tabular{ll}{
 Package: \tab VBLPCM\cr
 Type: \tab Package\cr
-Version: \tab 2.4.2\cr
-Date: \tab 2013-10-31\cr
+Version: \tab 2.4.3\cr
+Date: \tab 2014-03-07\cr
 License: \tab GPL (>=2) \cr
 LazyLoad: \tab yes\cr
 }

src/KL_funcs.c

@@ -534,8 +534,8 @@ void gr_KL_V_alpha_g (const gsl_vector *v_V_alpha_g, void *null, gsl_vector *df)
 	(*params->D* *params->inv_sigma02* gsl_sf_psi_1(0.5* *params->inv_sigma02*V_alpha_g)-
                        0.5* *params->inv_sigma02*(params->V_sigma2[i]+params->V_omega2[g]+tmp));
     }
-  KL = tmpsum + (0.5*(V_alpha_g-params->alpha[*params->g])* gsl_sf_psi_1(0.5*V_alpha_g) - 
-		     gsl_sf_psi(0.5*V_alpha_g)) + gsl_sf_psi(0.5*V_alpha_g)/lgamma(0.5*V_alpha_g);
+  KL = tmpsum + (0.5*(V_alpha_g-params->alpha[*params->g])* gsl_sf_psi_1(0.5*V_alpha_g));// - 
+		     //gsl_sf_psi(0.5*V_alpha_g)) + gsl_sf_psi(0.5*V_alpha_g)/lgamma(0.5*V_alpha_g);
   gsl_vector_set(df, 0, -KL);
   return;
 }

src/VBLPCM.c

@@ -242,7 +242,8 @@ for (l=0; l<*steps; l++) // number of cycles through the variational algorithm
         //tmp = GSQRT (tmp+*D*(V_sigma2[samp_nodes[i]]+V_omega2[samp_groups[g]]));
         tmp = GSQRT (tmp+(V_sigma2[samp_nodes[i]]+V_omega2[samp_groups[g]]));
         V_lambda[samp_groups[g]* *N + samp_nodes[i]] = 
-                exp(-1.0-0.5* *inv_sigma02*V_alpha[samp_groups[g]]*(tmp)+
+                //exp(-1.0-0.5* *inv_sigma02*V_alpha[samp_groups[g]]*(tmp)+
+                exp(-1.0+gsl_sf_psi(0.5* *inv_sigma02*V_alpha[samp_groups[g]])-0.5* *inv_sigma02*V_alpha[samp_groups[g]]*(tmp)+
                        gsl_sf_psi(V_nu[samp_groups[g]]) - gsl_sf_psi(tmpsum1));
         }
       tmp = 0.0;
@@ -264,7 +265,7 @@ for (l=0; l<*steps; l++) // number of cycles through the variational algorithm
         {
         tmp = 0.0;
         for (i=0; i<*N; i++)
-          tmp += V_lambda[samp_groups[g]* *N + i]* *inv_sigma02*V_alpha[samp_groups[g]];
+          tmp += V_lambda[samp_groups[g]* *N + i]* V_alpha[samp_groups[g]]; // removed inv_sigma02 31/01/2014
 	tmp = tmp/(*D/ *inv_sigma02) + 1.0/ *omega2;
         V_omega2[samp_groups[g]] = 1.0/tmp;
         }