diff --git a/BYM2_SC_scaling.txt b/BYM2_SC_scaling.txt
new file mode 100644
index 0000000..508746e
--- /dev/null
+++ b/BYM2_SC_scaling.txt
@@ -0,0 +1,162 @@
+
+###SCcongen90 is dataframe including the vectors obs,expe,pov,inc######
+#######################################################################
+SCcongen90<-list(obs=c(0,7,1,5,1,1,5,16,0,17,4,0,0,1,1,7,1,3,0,0,8,2,13,7,0,8,0,3,2,4,1,11,0,1,2,3,3,8,6,14,3,11,6,0,1,5),
+expe=c(1.0807,6.3775,0.622,6.6854,0.9142,1.0744,5.6518,8.1682,0.5749,18.0989,2.174,1.6619,1.9321,1.6148,
+1.6713,3.0819,1.7562,4.9952,0.9362,1.2001,6.1293,2.5604,15.8589,2.9437,1.0399,7.276,0.9739,2.064,2.7206,2.8275,0.9425,
+8.828,0.3644,1.775,1.5111,1.5111,2.5321,4.5836,3.9647,15.0264,0.732,10.8292,5.9848,1.4357,1.9949,6.9807),
+pov=c(13.6,13.8,32.3,11,24.2,19.9,12.3,13.3,17,15.4,14.1,15.7,18,24.3,21.8,20.2,24.9,12,17.4,18.1,18.7,17.5,
+10.6,13.8,22.8,13.7,21,12.6,14,14,26.9,9.4,17.8,23.1,23.1,14.4,10.8,22.1,10.1,13.6,15.9,11.2,18.3,14,26.4,10.6),
+inc=c(36.786,38.534,20.727,37.205,24.3,27.607,45.822,40.161,32.247,38.458,33.232,31.715,29.505,25.896,28.919,
+30.776,25.552,42.886,34.297,29.96,34.009,35.008,41.658,34.109,27.65,34.654,27.117,39.04,33.698,32.32,25.144,
+45.14,29.805,25.008,25.993,32.231,36.912,28.624,37.054,39.587,31.324,37.092,31.948,30.801,23.748,44.619))
+########################################################################
+SCcon<-data.frame(SCcongen90)
+#graphics....using SC_poly
+library(INLA)
+setwd("C:/A_NEW_FOLDER/A_NEW_FOLDER/A_PROGRAMS/GITHUB repository")
+#SC_poly.txt   is the graph file 
+g=inla.read.graph("SC_poly.txt")
+Q=-inla.graph2matrix(g)
+diag(Q) = 0
+diag(Q) = -rowSums(Q)
+n=dim(Q)[1]    # n=46
+Q.scaled=inla.scale.model(Q,constr=list(A=matrix(1,1,n),e=0))
+scale=Q.scaled[1,1]/Q[1,1]
+# scale of SC graph is 0.3783966
+### BYM2 model code in Nimble
+library(nimble)
+BYM2<-nimbleCode({
+for(i in 1: n){
+obs[i]~dpois(mu[i])
+log(mu[i])<-log(expe[i])+log(theta[i]) 
+theta[i]<-exp(ac0+bym2[i]) 
+vc[i]~dnorm(0,1)
+Corr[i]<-sigma*uc[i]*sqrt(rho/scale)
+UCorr[i]<-sigma*vc[i]*sqrt((1-rho)) 
+bym2[i]<-Corr[i]+UCorr[i]
+}  
+for (k in 1 :nsumN){wei[k]<-1} 
+uc[1:n]~dcar_normal(adj[1:nsumN],wei[1:nsumN],num[1:n],1,zero_mean=1)  
+sigma~dunif(0,10)
+tau<-pow(sigma,-2)
+rho~dbeta(1,1)
+ac0~dnorm(0,tau0)
+tau0<-pow(sd0,-2)
+sd0~dunif(0,10)
+})
+nsumN=228
+LSTdata<-list(obs=c(0,7,1,5,1,1,5,16,0,17,4,0,0,1,1,7,1,3,0,0,8,2,13,7,0,8,0,3,2,4,1,11,0,1,2,3,3,8,6,14,3,11,6,0,1,5))
+LSTconsts<-list(n,scale,nsumN,expe=c(1.0807,6.3775,0.622,6.6854,0.9142,1.0744,5.6518,8.1682,0.5749,18.0989,2.174,1.6619,1.9321,1.6148,
+1.6713,3.0819,1.7562,4.9952,0.9362,1.2001,6.1293,2.5604,15.8589,2.9437,1.0399,7.276,0.9739,2.064,2.7206,2.8275,0.9425,
+8.828,0.3644,1.775,1.5111,1.5111,2.5321,4.5836,3.9647,15.0264,0.732,10.8292,5.9848,1.4357,1.9949,6.9807),
+pov=c(13.6,13.8,32.3,11,24.2,19.9,12.3,13.3,17,15.4,14.1,15.7,18,24.3,21.8,20.2,24.9,12,17.4,18.1,18.7,17.5,
+10.6,13.8,22.8,13.7,21,12.6,14,14,26.9,9.4,17.8,23.1,23.1,14.4,10.8,22.1,10.1,13.6,15.9,11.2,18.3,14,26.4,10.6),
+inc=c(36.786,38.534,20.727,37.205,24.3,27.607,45.822,40.161,32.247,38.458,33.232,31.715,29.505,25.896,28.919,
+30.776,25.552,42.886,34.297,29.96,34.009,35.008,41.658,34.109,27.65,34.654,27.117,39.04,33.698,32.32,25.144,
+45.14,29.805,25.008,25.993,32.231,36.912,28.624,37.054,39.587,31.324,37.092,31.948,30.801,23.748,44.619),
+num = c(5, 5, 4, 5, 5, 4, 3, 6, 5, 4, 3, 4, 5, 6, 7, 5, 4, 4, 4, 6, 
+8,5, 5, 6, 5, 3, 2, 7, 5, 7, 5, 6, 3, 5, 4, 7, 2, 9, 3, 6,5, 4, 6, 7, 5, 4),
+adj = c(
+33, 30, 24, 23, 4, 
+41, 38, 32, 19, 6, 
+25, 15, 6, 5, 
+39, 37, 30, 23, 1, 
+38, 25, 15, 6, 3, 
+38, 5, 3, 2, 
+27, 25, 15, 
+45, 38, 22, 18, 14, 10, 
+43, 40, 38, 32, 14, 
+22, 18, 15, 8, 
+46, 44, 42, 
+46, 44, 29, 20, 
+35, 31, 29, 28, 16, 
+45, 43, 38, 21, 9, 8, 
+38, 25, 18, 10, 7, 5, 3, 
+35, 31, 28, 21, 13, 
+35, 34, 26, 21, 
+38, 15, 10, 8, 
+41, 33, 24, 2, 
+44, 40, 36, 29, 28, 12, 
+45, 43, 35, 34, 31, 17, 16, 14, 
+45, 34, 26, 10, 8, 
+42, 39, 30, 4, 1, 
+41, 36, 33, 30, 19, 1, 
+27, 15, 7, 5, 3, 
+34, 22, 17, 
+25, 7, 
+43, 40, 31, 29, 20, 16, 13, 
+46, 28, 20, 13, 12, 
+44, 42, 36, 24, 23, 4, 1, 
+43, 28, 21, 16, 13, 
+41, 40, 38, 36, 9, 2, 
+24, 19, 1, 
+45, 26, 22, 21, 17, 
+21, 17, 16, 13, 
+44, 41, 40, 32, 30, 24, 20, 
+39, 4, 
+32, 18, 15, 14, 9, 8, 6, 5, 2, 
+37, 23, 4, 
+43, 36, 32, 28, 20, 9, 
+36, 32, 24, 19, 2, 
+44, 30, 23, 11, 
+40, 31, 28, 21, 14, 9, 
+46, 42, 36, 30, 20, 12, 11, 
+34, 22, 21, 14, 8, 
+44, 29, 12, 11))
+LSTinits<-list(vc=c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
+uc=c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
+0, 0, 0, 0, 0),sigma=0.1,rho=0.5,ac0=0.1,sd0=0.1)
+names(LSTconsts)<-c("n","scale","nsumN","expe","pov","inc","num","adj")
+LBYM2<-nimbleModel(code=BYM2,name="BYM2",constants=LSTconsts,
+data=LSTdata,inits=LSTinits)
+###################################################################
+CLBYM2<-compileNimble(LBYM2)
+#####################################################################
+LBYM2Conf<-configureMCMC(LBYM2,print=TRUE)
+LBYM2Conf$addMonitors(c("ac0","sigma","rho","vc","uc","Corr","UCorr","theta"))
+LBYM2MCMC<-buildMCMC(LBYM2Conf)
+CLBYM2MCMC<-compileNimble(LBYM2MCMC,project=LBYM2)
+niter<-10000
+set.seed(1)
+samples<-runMCMC(CLBYM2MCMC,niter=niter,nburnin=7000,nchains=1,summary=TRUE,samplesAsCodaMCMC=TRUE)  
+samples$summary
+names(samples$summary)
+MCorr<-samples$summary[1:46,1]
+MUCorr<-samples$summary[47:92,1]
+Mac0<-samples$summary[93,1]
+Mrho<-samples$summary[94,1]
+Msd<-samples$summary[95,1]
+Msigma<-samples$summary[96,1]
+Mtheta<-samples$summary[97:142,1]
+Muc<-samples$summary[143:188,1]
+Mvc<-samples$summary[189:234,1]
+hist(Muc);x11();hist(Mvc);x11();hist(Mtheta);x11()
+
+
+###############################################
+SCcon<-data.frame(MCorr,MUCorr,Muc,Mvc,Mtheta)
+#SCcon is dataframe with vectors eg SCcon<-data.frame(......Muc,Mvc,Mtheta)
+library(tmap)
+areaID<-as.character(seq(1:46))
+area<-paste(c("area"),areaID,sep="")
+attr<-data.frame(SCcon,row.names=area)
+library(maptools)
+polySC<-readSplus("SC_SPLus_export_map.txt")
+spg<-SpatialPolygonsDataFrame(polySC, attr, match.ID = TRUE)
+#### this qtm call produces common legend #############################################
+labels<-c("Uc","Vc","Theta")
+qtm(spg,fill=c("Muc","Mvc","Mtheta"),fill.palette="Blues",ncol=3,free.scales=FALSE)
+### use tm_layout with legend.outside=TRUE and separate titles  ###########################
+qtm(spg,fill=c("Muc","Mvc","Mtheta"),fill.palette="Blues",ncol=3)+tm_layout(legend.outside=TRUE,title="",panel.labels=labels)
+### separate legends
+qtm(spg,fill=c("Muc","Mvc","Mtheta"),fill.palette="Blues",ncol=3)+tm_layout(legend.position=c("LEFT","BOTTOM"),main.title="BYM2 results",panel.labels=labels)
+#############################################################
+####### mapping the UCorr and Corr components
+labels<-c("Ucorr","Corr","Theta") 
+qtm(spg,fill=c("MUCorr","MCorr","Mtheta"),fill.palette="Blues",ncol=3)+tm_layout(legend.position=c("LEFT","BOTTOM"),main.title="BYM2 UCorr, Corr",panel.labels=labels)
+######################################################
+mcmc.out<-nimbleMCMC(code=BYM2,constants=LSTconsts,
+data=LSTdata,inits=LSTinits,nchains=2,nburnin=7000,niter=10000,summary=TRUE,WAIC=TRUE)
\ No newline at end of file
diff --git a/BYM_ordinary_SC_NOscaling.txt b/BYM_ordinary_SC_NOscaling.txt
new file mode 100644
index 0000000..1676b12
--- /dev/null
+++ b/BYM_ordinary_SC_NOscaling.txt
@@ -0,0 +1,149 @@
+
+###SCcongen90 is dataframe including the vectors obs,expe,pov,inc######
+#######################################################################
+SCcongen90<-list(obs=c(0,7,1,5,1,1,5,16,0,17,4,0,0,1,1,7,1,3,0,0,8,2,13,7,0,8,0,3,2,4,1,11,0,1,2,3,3,8,6,14,3,11,6,0,1,5),
+expe=c(1.0807,6.3775,0.622,6.6854,0.9142,1.0744,5.6518,8.1682,0.5749,18.0989,2.174,1.6619,1.9321,1.6148,
+1.6713,3.0819,1.7562,4.9952,0.9362,1.2001,6.1293,2.5604,15.8589,2.9437,1.0399,7.276,0.9739,2.064,2.7206,2.8275,0.9425,
+8.828,0.3644,1.775,1.5111,1.5111,2.5321,4.5836,3.9647,15.0264,0.732,10.8292,5.9848,1.4357,1.9949,6.9807),
+pov=c(13.6,13.8,32.3,11,24.2,19.9,12.3,13.3,17,15.4,14.1,15.7,18,24.3,21.8,20.2,24.9,12,17.4,18.1,18.7,17.5,
+10.6,13.8,22.8,13.7,21,12.6,14,14,26.9,9.4,17.8,23.1,23.1,14.4,10.8,22.1,10.1,13.6,15.9,11.2,18.3,14,26.4,10.6),
+inc=c(36.786,38.534,20.727,37.205,24.3,27.607,45.822,40.161,32.247,38.458,33.232,31.715,29.505,25.896,28.919,
+30.776,25.552,42.886,34.297,29.96,34.009,35.008,41.658,34.109,27.65,34.654,27.117,39.04,33.698,32.32,25.144,
+45.14,29.805,25.008,25.993,32.231,36.912,28.624,37.054,39.587,31.324,37.092,31.948,30.801,23.748,44.619))
+########################################################################
+SCcon<-data.frame(SCcongen90)
+#graphics....using SC_poly
+library(INLA)
+setwd("C:/A_NEW_FOLDER/A_NEW_FOLDER/A_PROGRAMS/GITHUB repository")
+#SC_poly.txt   is the graph file 
+
+
+### BYM model code in Nimble
+library(nimble)
+BYM<-nimbleCode({
+for(i in 1: n){
+obs[i]~dpois(mu[i])
+log(mu[i])<-log(expe[i])+log(theta[i]) 
+theta[i]<-exp(ac0+bym[i]) 
+vc[i]~dnorm(0,tauV)
+bym[i]<-vc[i]+uc[i]
+}  
+for (k in 1 :nsumN){wei[k]<-1} 
+uc[1:n]~dcar_normal(adj[1:nsumN],wei[1:nsumN],num[1:n],tauU,zero_mean=1)  
+tauU<-pow(sdU,-2)
+tauV<-pow(sdV,-2)
+sdV~dunif(0,10)
+sdU~dunif(0,10)
+ac0~dnorm(0,tau0)
+sd0~dunif(0,10)
+})
+nsumN=228
+LSTdata<-list(obs=c(0,7,1,5,1,1,5,16,0,17,4,0,0,1,1,7,1,3,0,0,8,2,13,7,0,8,0,3,2,4,1,11,0,1,2,3,3,8,6,14,3,11,6,0,1,5))
+LSTconsts<-list(n,nsumN,expe=c(1.0807,6.3775,0.622,6.6854,0.9142,1.0744,5.6518,8.1682,0.5749,18.0989,2.174,1.6619,1.9321,1.6148,
+1.6713,3.0819,1.7562,4.9952,0.9362,1.2001,6.1293,2.5604,15.8589,2.9437,1.0399,7.276,0.9739,2.064,2.7206,2.8275,0.9425,
+8.828,0.3644,1.775,1.5111,1.5111,2.5321,4.5836,3.9647,15.0264,0.732,10.8292,5.9848,1.4357,1.9949,6.9807),
+pov=c(13.6,13.8,32.3,11,24.2,19.9,12.3,13.3,17,15.4,14.1,15.7,18,24.3,21.8,20.2,24.9,12,17.4,18.1,18.7,17.5,
+10.6,13.8,22.8,13.7,21,12.6,14,14,26.9,9.4,17.8,23.1,23.1,14.4,10.8,22.1,10.1,13.6,15.9,11.2,18.3,14,26.4,10.6),
+inc=c(36.786,38.534,20.727,37.205,24.3,27.607,45.822,40.161,32.247,38.458,33.232,31.715,29.505,25.896,28.919,
+30.776,25.552,42.886,34.297,29.96,34.009,35.008,41.658,34.109,27.65,34.654,27.117,39.04,33.698,32.32,25.144,
+45.14,29.805,25.008,25.993,32.231,36.912,28.624,37.054,39.587,31.324,37.092,31.948,30.801,23.748,44.619),
+num = c(5, 5, 4, 5, 5, 4, 3, 6, 5, 4, 3, 4, 5, 6, 7, 5, 4, 4, 4, 6, 
+8,5, 5, 6, 5, 3, 2, 7, 5, 7, 5, 6, 3, 5, 4, 7, 2, 9, 3, 6,5, 4, 6, 7, 5, 4),
+adj = c(
+33, 30, 24, 23, 4, 
+41, 38, 32, 19, 6, 
+25, 15, 6, 5, 
+39, 37, 30, 23, 1, 
+38, 25, 15, 6, 3, 
+38, 5, 3, 2, 
+27, 25, 15, 
+45, 38, 22, 18, 14, 10, 
+43, 40, 38, 32, 14, 
+22, 18, 15, 8, 
+46, 44, 42, 
+46, 44, 29, 20, 
+35, 31, 29, 28, 16, 
+45, 43, 38, 21, 9, 8, 
+38, 25, 18, 10, 7, 5, 3, 
+35, 31, 28, 21, 13, 
+35, 34, 26, 21, 
+38, 15, 10, 8, 
+41, 33, 24, 2, 
+44, 40, 36, 29, 28, 12, 
+45, 43, 35, 34, 31, 17, 16, 14, 
+45, 34, 26, 10, 8, 
+42, 39, 30, 4, 1, 
+41, 36, 33, 30, 19, 1, 
+27, 15, 7, 5, 3, 
+34, 22, 17, 
+25, 7, 
+43, 40, 31, 29, 20, 16, 13, 
+46, 28, 20, 13, 12, 
+44, 42, 36, 24, 23, 4, 1, 
+43, 28, 21, 16, 13, 
+41, 40, 38, 36, 9, 2, 
+24, 19, 1, 
+45, 26, 22, 21, 17, 
+21, 17, 16, 13, 
+44, 41, 40, 32, 30, 24, 20, 
+39, 4, 
+32, 18, 15, 14, 9, 8, 6, 5, 2, 
+37, 23, 4, 
+43, 36, 32, 28, 20, 9, 
+36, 32, 24, 19, 2, 
+44, 30, 23, 11, 
+40, 31, 28, 21, 14, 9, 
+46, 42, 36, 30, 20, 12, 11, 
+34, 22, 21, 14, 8, 
+44, 29, 12, 11))
+LSTinits<-list(vc=c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
+uc=c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
+0, 0, 0, 0, 0),ac0=0.1,sd0=0.1,sdV=0.1,sdU=0.1)
+names(LSTconsts)<-c("n","nsumN","expe","pov","inc","num","adj")
+LBYM<-nimbleModel(code=BYM,name="BYM",constants=LSTconsts,
+data=LSTdata,inits=LSTinits)
+###################################################################
+CLBYM<-compileNimble(LBYM)
+#####################################################################
+LBYMConf<-configureMCMC(LBYM,print=TRUE)
+LBYMConf$addMonitors(c("ac0","vc","uc","theta"))
+LBYMMCMC<-buildMCMC(LBYMConf)
+CLBYMMCMC<-compileNimble(LBYMMCMC,project=LBYM)
+niter<-10000
+set.seed(1)
+samples<-runMCMC(CLBYMMCMC,niter=niter,nburnin=7000,nchains=1,summary=TRUE,samplesAsCodaMCMC=TRUE)  
+samples$summary
+
+Mac0<-samples$summary[1,1]
+Msd0<-samples$summary[2,1]
+MsdU<-samples$summary[3,1]
+MsdV<-samples$summary[4,1]
+Mtheta<-samples$summary[5:50,1]
+Muc<-samples$summary[51:96,1]
+Mvc<-samples$summary[97:142,1]
+hist(Muc);x11();hist(Mvc);x11();hist(Mtheta);x11()
+
+
+###############################################
+SCcon<-data.frame(Muc,Mvc,Mtheta)
+#SCcon is dataframe with vectors eg SCcon<-data.frame(......Muc,Mvc,Mtheta)
+library(tmap)
+areaID<-as.character(seq(1:46))
+area<-paste(c("area"),areaID,sep="")
+attr<-data.frame(SCcon,row.names=area)
+library(maptools)
+polySC<-readSplus("SC_SPLus_export_map.txt")
+spg<-SpatialPolygonsDataFrame(polySC, attr, match.ID = TRUE)
+#### this qtm call produces common legend #############################################
+labels<-c("Uc","Vc","Theta")
+qtm(spg,fill=c("Muc","Mvc","Mtheta"),fill.palette="Blues",ncol=3,free.scales=FALSE)
+### use tm_layout with legend.outside=TRUE and separate titles ###########################
+qtm(spg,fill=c("Muc","Mvc","Mtheta"),fill.palette="Blues",ncol=3)+tm_layout(legend.outside=TRUE,title="",panel.labels=labels)
+### separate legends ##############################################
+qtm(spg,fill=c("Muc","Mvc","Mtheta"),fill.palette="Blues",ncol=3)+tm_layout(legend.position=c("LEFT","BOTTOM"),main.title="BYM results",panel.labels=labels)
+#############################################################
+
+mcmc.out<-nimbleMCMC(code=BYM,constants=LSTconsts,
+data=LSTdata,inits=LSTinits,nchains=2,nburnin=7000,niter=10000,summary=TRUE,WAIC=TRUE)
\ No newline at end of file
diff --git a/Georgia_MultiScale_CONV_Nimble.txt b/Georgia_MultiScale_CONV_Nimble.txt
new file mode 100644
index 0000000..c204c88
--- /dev/null
+++ b/Georgia_MultiScale_CONV_Nimble.txt
@@ -0,0 +1,314 @@
+#### Goergia county/Ph district joint multi-scale model ########
+MultiSC<-nimbleCode({ 
+
+for( i in 1:18){ 
+yph[i]~dpois(muph[i]) 
+log(muph[i])<-log(eph[i])+log(thph[i]) 
+thph[i]<-exp(aph0+uph[i]+vph[i]) 
+vph[i]~dnorm(0,0.0001) 
+} 
+for( j in 1:159){ 
+yc[j]~dpois(muc[j]) 
+log(muc[j])<-log(ec[j])+log(thc[j]) 
+thc[j]<-exp(ac0+uc[j]+vc[j]) 
+vc[j]~dnorm(0,0.0001) 
+}  
+for (k in 1 :nsumph){weiph[k]<-1} 
+uph[1:18]~dcar_normal(adj[1:nsumph],weiph[1:nsumph],num[1:18],tauph)  
+tauph<-1/pow(sdph,2) 
+aph0~dflat() 
+sdph~dunif(0,100) 
+for (o in 1 :nsumc){weic[o]<-1} 
+uc[1:159]~dcar_normal(adj2[1:nsumc],weic[1:nsumc],num2[1:159],tauc) 
+tauc<-1/pow(sdc,2) 
+ac0~dflat()  
+sdc~dunif(0,100) 
+})
+##################################################### 
+MultiSCinits<-list(uc=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0),vc=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0),ac0=-6.6,sdc=2.4, 
+uph=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0),vph=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), 
+aph0=-1.5,sdph=0.1)  
+
+MSCdata<-list(
+yph=c(24,17,13,22,24,38,8,38,24,48,5,27,30,17,8,
+29,18,24) ,
+yc=c(3,1,0,0,5,4,2,2,1,0,8,0,0,0,1,2,1,1,1,0,0,9,0,1,15,1,1,5,2,0,8,0,19,4,4,3,0,4,0,2,0,2,0,24,0,1,9,5,0,0,1,0,2,1,1,5,5,2,2,38,1,1,5,4,7,1,28,2,3,0,0,2,0,2,7,6,0,1,0,0,2,0,0,2,0,0,2,1,1,1,1,4,3,1,0,0,3,0,0,0,2,1,1,1,4,
+7,7,1,0,4,3,2,2,3,6,1,0,0,1,0,18,3,0,0,0,6,1,1,2,1,0,
+2,0,1,0,2,3,1,1,0,7,0,0,0,4,2,2,0,1,0,1,0,0,1,4,0,0,2,3))
+MSConsts<-list(nsumc = 860, 
+num2= c(6, 5, 5, 6, 5, 6, 6, 7, 5, 7,  
+6, 6, 6, 4, 5, 7, 5, 6, 6, 3,  
+5, 6, 2, 3, 2, 5, 3, 7, 5, 4,  
+5, 4, 5, 8, 6, 3, 5, 7, 5, 6,  
+1, 7, 5, 5, 6, 6, 6, 4, 5, 3,  
+4, 5, 10, 5, 5, 4, 4, 5, 4, 10,  
+6, 4, 4, 9, 3, 6, 7, 6, 9, 7,  
+3, 4, 3, 3, 6, 7, 5, 6, 6, 7,  
+8, 4, 6, 7, 5, 5, 7, 5, 5, 4,  
+4, 5, 6, 7, 4, 6, 7, 6, 7, 4,  
+7, 7, 5, 6, 4, 3, 6, 7, 7, 6,  
+5, 5, 5, 4, 4, 5, 6, 3, 2, 6,  
+4, 5, 4, 4, 3, 8, 3, 4, 8, 7,  
+6, 8, 7, 6, 6, 4, 6, 7, 4, 6,  
+4, 6, 6, 4, 7, 5, 6, 7, 6, 6,  
+7, 6, 6, 5, 4, 7, 6, 7, 7 
+), 
+adj2 = c( 
+151, 138, 132, 113, 80, 3,  
+148, 86, 34, 32, 10,  
+148, 113, 80, 34, 1,  
+101, 100, 49, 47, 43, 19,  
+158, 150, 117, 84, 70,  
+127, 97, 78, 69, 68, 59,  
+147, 108, 78, 69, 67, 29,  
+115, 112, 110, 64, 57, 33, 28,  
+156, 142, 134, 77, 34,  
+137, 92, 86, 77, 37, 34, 2,  
+143, 111, 102, 84, 76, 39,  
+158, 143, 116, 87, 76, 45,  
+151, 148, 113, 63, 24, 20,  
+136, 92, 37, 35,  
+89, 54, 51, 25, 16,  
+124, 82, 54, 53, 51, 21, 15,  
+124, 121, 82, 81, 53,  
+126, 107, 102, 85, 79, 75,  
+135, 120, 49, 47, 30, 4,  
+63, 24, 13,  
+138, 132, 54, 53, 16,  
+110, 74, 71, 60, 48, 38,  
+155, 146,  
+148, 20, 13,  
+51, 15,  
+152, 130, 128, 106, 98,  
+146, 64, 57,  
+112, 64, 60, 58, 42, 33, 8,  
+109, 108, 97, 78, 7,  
+120, 118, 49, 19,  
+126, 75, 60, 56, 44,  
+148, 86, 50, 2,  
+110, 60, 48, 28, 8,  
+148, 134, 80, 77, 10, 9, 3, 2,  
+159, 137, 136, 101, 37, 14,  
+121, 94, 90,  
+137, 92, 35, 14, 10,  
+141, 126, 99, 74, 60, 56, 22,  
+145, 133, 111, 102, 11,  
+159, 156, 142, 129, 88, 46,  
+146,  
+112, 93, 69, 61, 58, 55, 28,  
+125, 101, 100, 65, 4,  
+122, 75, 67, 60, 31,  
+156, 153, 134, 116, 87, 12,  
+156, 129, 116, 96, 76, 40,  
+159, 135, 101, 88, 19, 4,  
+110, 60, 33, 22,  
+125, 100, 30, 19, 4,  
+92, 86, 32,  
+124, 25, 16, 15,  
+157, 109, 97, 90, 73,  
+140, 138, 132, 103, 83, 82, 81, 21, 17, 16,  
+132, 89, 21, 16, 15,  
+144, 105, 93, 61, 42,  
+126, 60, 38, 31,  
+115, 64, 27, 8,  
+69, 67, 60, 42, 28,  
+127, 97, 73, 6,  
+67, 58, 56, 48, 44, 38, 33, 31, 28, 22,  
+112, 105, 93, 64, 55, 42,  
+150, 149, 81, 70,  
+151, 95, 20, 13,  
+155, 146, 112, 105, 61, 57, 28, 27, 8,  
+136, 101, 43,  
+131, 117, 109, 108, 104, 70,  
+147, 122, 69, 60, 58, 44, 7,  
+154, 139, 127, 119, 69, 6,  
+154, 93, 78, 68, 67, 58, 42, 7, 6,  
+150, 149, 131, 117, 66, 62, 5,  
+115, 110, 22,  
+141, 130, 106, 99,  
+97, 59, 52,  
+141, 38, 22,  
+126, 122, 107, 44, 31, 18,  
+143, 116, 111, 96, 46, 12, 11,  
+142, 137, 34, 10, 9,  
+108, 97, 69, 29, 7, 6,  
+117, 107, 104, 102, 84, 18,  
+153, 138, 134, 103, 34, 3, 1,  
+150, 149, 121, 94, 83, 62, 53, 17,  
+124, 53, 17, 16,  
+158, 150, 140, 87, 81, 53,  
+158, 143, 117, 102, 79, 11, 5,  
+145, 126, 114, 102, 18,  
+92, 50, 32, 10, 2,  
+158, 153, 143, 140, 83, 45, 12,  
+159, 135, 129, 47, 40,  
+132, 95, 91, 54, 15,  
+157, 94, 52, 36,  
+151, 132, 95, 89,  
+86, 50, 37, 14, 10,  
+154, 144, 69, 61, 55, 42,  
+157, 149, 131, 121, 90, 81, 36,  
+151, 91, 89, 63,  
+133, 129, 123, 111, 76, 46,  
+109, 78, 73, 59, 52, 29, 6,  
+152, 133, 130, 129, 123, 26,  
+145, 141, 130, 126, 114, 72, 38,  
+125, 49, 43, 4,  
+159, 136, 65, 47, 43, 35, 4,  
+145, 85, 84, 79, 39, 18, 11,  
+153, 140, 138, 80, 53,  
+147, 117, 108, 107, 79, 66,  
+155, 64, 61, 55,  
+130, 72, 26,  
+147, 122, 104, 79, 75, 18,  
+147, 109, 104, 78, 66, 29, 7,  
+157, 131, 108, 97, 66, 52, 29,  
+115, 71, 48, 33, 22, 8,  
+133, 96, 76, 39, 11,  
+64, 61, 42, 28, 8,  
+151, 148, 13, 3, 1,  
+145, 126, 99, 85,  
+110, 71, 57, 8,  
+156, 76, 46, 45, 12,  
+104, 84, 79, 70, 66, 5,  
+128, 120, 30,  
+139, 68,  
+152, 135, 128, 118, 30, 19,  
+94, 81, 36, 17,  
+147, 107, 75, 67, 44,  
+133, 129, 98, 96,  
+82, 51, 17, 16,  
+100, 49, 43,  
+114, 99, 85, 75, 56, 38, 31, 18,  
+68, 59, 6,  
+152, 120, 118, 26,
+152, 135, 123, 98, 96, 88, 46, 40,  
+145, 133, 106, 99, 98, 72, 26,  
+157, 149, 109, 94, 70, 66,  
+151, 138, 91, 89, 54, 53, 21, 1,  
+145, 130, 123, 111, 98, 96, 39,  
+156, 153, 80, 45, 34, 9,    
+152, 129, 120, 88, 47, 19,  
+101, 65, 35, 14,  
+159, 142, 77, 37, 35, 10,  
+140, 132, 103, 80, 53, 21, 1,  
+154, 144, 119, 68,  
+153, 138, 103, 87, 83, 53,  
+99, 74, 72, 38,  
+159, 156, 137, 77, 40, 9,  
+158, 87, 84, 76, 12, 11,  
+154, 139, 93, 55,  
+133, 130, 114, 102, 99, 85, 39,  
+155, 64, 41, 27, 23,  
+122, 108, 107, 104, 67, 7,  
+113, 34, 32, 24, 13, 3, 2,  
+157, 131, 94, 81, 70, 62,  
+158, 83, 81, 70, 62, 5,  
+132, 113, 95, 91, 63, 13, 1,  
+135, 129, 128, 120, 98, 26,  
+140, 134, 103, 87, 80, 45,  
+144, 139, 93, 69, 68, 
+146, 105, 64, 23,  
+142, 134, 116, 46, 45, 40, 9,  
+149, 131, 109, 94, 90, 52,  
+150, 143, 87, 84, 83, 12, 5,  
+142, 137, 101, 88, 47, 40, 35), 
+phc=c(18,18,18,16,12,4,3,1,15,15,12,11,18,15,17,18,
+13,10,16,17,18,10,1,18,17,14,1,2,3,14,7,18,5,18,
+16,13,15,10,12,14,1,4,16,9,11,14,16,5,16,15,
+17,3,13,18,2,10,1,4,4,6,2,13,17,1,16,3,8,4,4,
+12,1,14,4,10,10,12,15,3,12,18,13,13,11,12,10,15,
+11,16,17,13,17,15,4,13,17,14,3,14,10,16,16,12,11,
+3,2,14,8,3,3,1,12,2,18,10,1,11,12,14,4,14,13,8,14,
+13,16,10,4,14,14,14,13,18,14,11,16,16,15,18,4,11,
+10,15,12,4,10,1,3,18,13,12,18,14,11,4,2,11,13,12,16),
+eph=c(26.7313582387354,17.8569786812963,
+19.000833240556,24.8322642703346,35.6928573604747,
+38.1880967220473,12.4232592469938,40.2840771546551,
+31.6839976247491,33.1291602142528,6.65287755667638,
+23.232040109711,20.3645029329909
+,16.4453885396069,10.949025543461,16.922717476408,
+15.6506686820585,23.9598964049923), 
+ec=c(0.842405862334888,0.375626926592719,0.484362270840443,0.199184019993243,2.11970621276708,0.735452296043789,2.64537759886506,4.0780208537788,0.813194081624956,0.782106745171208,7.27574961919763,0.564870501143738,0.728747184259648
+,0.767430521475849,1.29108568514923,2.82946339512059,1.08730655358364,1.04852943858025,0.286116028719108,2.11506421230113,0.477938492417873,4.76283314473956,2.80606583721648,0.501617383683548,11.1838451225867,0.633281396899421
+,1.24508768053215,8.19055193324381,4.87414737813503,0.155530460055929,12.4232592469938,0.325830921594408,30.6655708558571,1.84643774089311,2.05199865041532,4.71650691786731,0.762178965393165,4.94096405150847,0.604304060657466
+,1.03286854811939,0.749847186377576,0.893889867502633,1.34172569023226,31.6839976247491,0.914380314003821,0.544098721280977,4.4863762281011,5.02728650461759,0.566933612461935,0.19229135263472,2.09410487686399,0.980352987292544
+,1.03591632620309,0.527406275161016,1.0134096572773,4.75139225470228,4.4079779980096,6.18300395395692,1.00590743430203,38.1880967220473,1.25451234814483,0.123364679049487,3.34585078028669,2.30116623098126,1.13846233649622
+,0.733904962555141,32.8594552982921,1.82763529456135,7.54559520183913,0.460026935064432,1.31612435432918,1.25605968163347,1.09574655443081,0.529375608692023,7.47905986182727,5.80264124911106,0.465888046763857,2.32287578871593
+,0.603272504998367,0.601115615892979,0.791625190571074,0.406995596408039,0.449476934005468,1.23013012347522,0.769446743900452,0.349931812902442,2.19008644205377,1.40258747411909,2.89529540172852,0.393772928414137,0.51240182921049
+,4.49134645082222,1.12181677926985,1.00890832349214,0.522248496865523,0.653396732251846,1.28062946187746,0.333473811250458,1.06672232929526,0.289070029015618,1.11773744552705,1.09851299915294,0.420593375550704,0.79767385784488
+,1.90162596865489,8.57363419391819,3.82257016147108,1.35696458065077,0.635672730472786,4.96722183192189,1.15651456053045,1.30215146403775,0.783982300915024,0.73850007412749,1.88807507840582,0.461246046297912,0.925868092934693
+,0.115674900499842,0.750738075355888,0.343742478947849,9.20264870150043,3.60205169489193,0.189478019018996,0.719088072178996,0.434566265842132,2.85487717544929,1.17165967316176,0.233553578998668,1.54137859916146,0.308857142112875
+,8.89013422568712E-02,1.07816321933254,0.421296708954635,0.605476282997351,0.513433384869588,2.06259554036788,1.88390196687583,1.25545012601673,0.47512515880215,0.330332255379566,2.86964717693184,0.440755599796724,0.489942049178295
+,0.919350536724933,1.31781235449861,2.97177118718262,3.3732338941464,1.66994794540004,0.293243140545608,0.987526988012639,1.32217302160298,0.109063566502892,0.30890403100647,1.10634344438337,4.19472730993774,0.407511374237588
+,0.496225160920078,0.477844714630683,1.03193077024748),
+nsumph= 88,
+num = c(3, 4, 5, 4, 6, 7, 4, 8, 6, 8,
+5, 6, 5, 5, 4, 2, 2, 4), 
+adj = c( 
+10, 5, 2, 
+6, 5, 4, 1, 
+13, 12, 9, 8, 4, 
+8, 6, 3, 2, 
+10, 9, 8, 6, 2, 1, 
+10, 9, 8, 7, 5, 4, 2, 
+10, 9, 8, 6, 
+12, 10, 9, 7, 6, 5, 4, 3, 
+10, 8, 7, 6, 5, 3, 
+14, 12, 9, 8, 7, 6, 5, 1, 
+18, 15, 14, 13, 12, 
+14, 13, 11, 10, 8, 3, 
+18, 17, 12, 11, 3, 
+16, 15, 12, 11, 10, 
+18, 16, 14, 11, 
+15, 14, 
+18, 13, 
+17, 15, 13, 11)) 
+  
+
+############################################################
+MSC<-nimbleModel(code=MultiSC,name="MSC",constants=MSConsts,
+data=MSCdata,inits=MultiSCinits)
+########
+CMSC<-compileNimble(MSC)
+################## Running with monitors and CODA output  #####
+LSTConf<-configureMCMC(MSC,print=TRUE)
+LSTConf$addMonitors(c("ac0","uph","vph","uc","vc","thph","thc"))
+LSTMCMC<-buildMCMC(LSTConf)
+CLSTMCMC<-compileNimble(LSTMCMC,project=MSC)
+niter<-20000
+set.seed(1)
+samples<-runMCMC(CLSTMCMC,niter=niter,nburnin=15000,nchains=1,summary=TRUE,WAIC=TRUE,samplesAsCodaMCMC=TRUE)
+#samples$summary
+samples$WAIC
+
+
+######################################################################
+sampMAT<-as.matrix(samples$samples)  ### dim is 5000 by 535
+par(mfrow=c(2,2))
+plot(sampMAT[,"ac0"],type="l",xlab="iteration",ylab=expression(ac0))
+plot(sampMAT[,"aph0"],type="l",xlab="iteration",ylab=expression(aph0))
+plot(sampMAT[,"sdc"],type="l",xlab="iteration",ylab=expression(sdc))
+plot(sampMAT[,"sdph"],type="l",xlab="iteration",ylab=expression(sdph))
+
+
+setwd("C:/A_NEW_FOLDER/A_NEW_FOLDER/A_PROGRAMS/R_program/NIMBLE examples/CAR and other models on NIMBLE/Multi_scale/")
+library(maptools)
+library(sp)
+source("fillmap.R")
+GCpoly<-readSplus("Georgia_Counties_SPlus.txt")
+plot(GCpoly)
+GHDpoly<-readSplus("GeorgiaHDistrictsSPlus.txt")
+plot(GHDpoly)
+
+fillmap(GCpoly,"",,n.col=5);x11()
+fillmap(GCpoly,"",,n.col=5);x11()
+fillmap(GCpoly,"",,n.col=5)
\ No newline at end of file
diff --git a/README.md b/README.md
index 6a06eb6..239eab6 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,3 @@
 # Bayesian_DM_Nimble_code
 This repositry is solely for new Nimble code for Bayesian disease mapping. Currently included are ICAR and convolution models, M models 
-and ST extensions. In addition the new MICAR and MPCAR code files for fitting MCAR models on Nimble are here 
+and ST extensions. In addition the new MICAR and MPCAR code files for fitting MCAR models on Nimble are here in the MCAR branch.
diff --git a/SC_congen90_lognormal_Nimble_new.txt b/SC_congen90_lognormal_Nimble_new.txt
new file mode 100644
index 0000000..60c9ca6
--- /dev/null
+++ b/SC_congen90_lognormal_Nimble_new.txt
@@ -0,0 +1,74 @@
+library(nimble)
+LNCode<-nimbleCode({
+for(i in 1:N){
+log(theta[i])<-a0+v[i]
+lambda[i]<-theta[i]*texp[i]
+y[i]~dpois(lambda[i])
+v[i]~dnorm(0,tauv)
+}
+a0~dnorm(0,tau0)
+tauv~dgamma(2,0.5)
+tau0~dgamma(2,0.5)
+})
+########################################################################
+
+######################## SC data  #######################################
+
+LNConsts<-list(N=46,texp=c(1.0807,6.3775,0.622,6.6854,0.9142,1.0744,5.6518,
+8.1682,0.5749,18.0989,2.174,1.6619,1.9321,1.6148,1.6713,3.0819,1.7562,4.9952,
+0.9362,1.2001,6.1293,2.5604,15.8589,2.9437,1.0399,7.276,0.9739,2.064,2.7206,2.8275,
+0.9425,8.828,0.3644,1.775,1.5111,1.5111,2.5321,
+4.5836,3.9647,15.0264,0.732,10.8292,5.9848,1.4357,1.9949,6.9807),
+pov=c(13.6,13.8,32.3,11,24.2,19.9,12.3,13.3,17,15.4,14.1,15.7,18,24.3,21.8,
+20.2,24.9,12,17.4,18.1,18.7,17.5,10.6,13.8,22.8,13.7,21,12.6,14,14,26.9,
+9.4,17.8,23.1,23.1,14.4,10.8,22.1,10.1,13.6,15.9,11.2,18.3,14,26.4,10.6),
+inc=c(36.786,38.534,20.727,37.205,24.3,27.607,45.822,40.161,32.247,38.458,
+33.232,31.715,29.505,25.896,28.919,30.776,25.552,42.886,34.297,29.96,34.009,
+35.008,41.658,34.109,27.65,34.654,27.117,39.04,33.698,32.32,25.144,45.14,
+29.805,25.008,25.993,32.231,36.912,28.624,37.054,39.587,31.324,37.092,
+31.948,30.801,23.748,44.619))
+LNData<-list(y=c(0,7,1,5,1,1,5,16,0,17,4,0,0,1,1,7,1,3,0,0,8,2,13,7,0,8,0,3,2,4,1,11,0,1,2,3,3,8,6,14,3,11,6,0,1,5))
+LNInits<-list(a0=0.1,tauv=0.1,tau0=0.1,v=rep(0.0,LNConsts$N))
+
+LN<-nimbleModel(code=LNCode,name="LN",constants=LNConsts,
+data=LNData,inits=LNInits)
+#####################################################################
+
+######################## Compile  ######################################
+CLN<-compileNimble(LN)
+CLN$v
+
+
+mcmc.out<-nimbleMCMC(code=LNCode,constants=LNConsts,
+data=LNData,inits=LNInits,nchains=2,niter=10000,summary=TRUE,WAIC=TRUE)
+
+mcmc.out$summary
+mcmc.out$WAIC
+
+#####################################################################
+
+LNConf<-configureMCMC(LN,print=TRUE)
+LNConf$addMonitors(c("a0","tauv","tau0","v","theta"))
+LNMCMC<-buildMCMC(LNConf)
+CLNMCMC<-compileNimble(LNMCMC,project=LN)
+niter<-10000
+set.seed(1)
+samples<-runMCMC(CLNMCMC,niter=niter,nburnin=7000)
+plot(samples[,"a0"],type="l",xlab="iteration",ylab=expression(a0));x11()
+plot(samples[,"tauv"],type="l",xlab="iteration",ylab=expression(tauv));x11()
+plot(samples[,"tau0"],type="l",xlab="iteration",ylab=expression(tau0))
+boxplot(samples[,"a0"],xlab=expression(a0))
+boxplot(samples[,"tauv"],xlab=expression(tauv))
+
+for(k in 1:46){x11()
+plot(samples[,3+k],type="l",xlab="iteration",ylab=expression(theta))
+}
+for(k in 1:46){x11()
+plot(samples[,49+k],type="l",xlab="iteration",ylab=expression(v))
+}
+
+for (k in 1:46){
+boxplot(samples[,3+k],xlab=expression(theta))}
+
+samplesDF<-data.frame(samples)
+plot(density(samplesDF$theta.1.))
diff --git a/SC_congen_90_CONV_Nimble.txt b/SC_congen_90_CONV_Nimble.txt
new file mode 100644
index 0000000..af386a7
--- /dev/null
+++ b/SC_congen_90_CONV_Nimble.txt
@@ -0,0 +1,168 @@
+#### SC congenital data 1990  CONvolution model #######
+library(nimble)
+LconvCode<-nimbleCode({
+for(i in 1:N){
+log(theta[i])<-a0+v[i]+B[i]
+lambda[i]<-theta[i]*texp[i]
+y[i]~dpois(lambda[i])
+v[i]~dnorm(0,tauv)
+}
+for(k in 1:L){wei[k]<-1}
+B[1:N]~dcar_normal(adj[1:L],wei[1:L],num[1:N],tauB,zero_mean=1)
+a0~dnorm(0,tau0)
+tauv~dgamma(2,0.5)
+tau0~dgamma(2,0.5)
+tauB~dgamma(2,0.5)
+})
+########################################################################
+
+######################## SC data  #######################################
+
+LconvConsts<-list(N=46,L=228,texp=c(1.0807,6.3775,0.622,6.6854,0.9142,1.0744,5.6518,
+8.1682,0.5749,18.0989,2.174,1.6619,1.9321,1.6148,1.6713,3.0819,1.7562,4.9952,
+0.9362,1.2001,6.1293,2.5604,15.8589,2.9437,1.0399,7.276,0.9739,2.064,2.7206,2.8275,
+0.9425,8.828,0.3644,1.775,1.5111,1.5111,2.5321,
+4.5836,3.9647,15.0264,0.732,10.8292,5.9848,1.4357,1.9949,6.9807),
+pov=c(13.6,13.8,32.3,11,24.2,19.9,12.3,13.3,17,15.4,14.1,15.7,18,24.3,21.8,
+20.2,24.9,12,17.4,18.1,18.7,17.5,10.6,13.8,22.8,13.7,21,12.6,14,14,26.9,
+9.4,17.8,23.1,23.1,14.4,10.8,22.1,10.1,13.6,15.9,11.2,18.3,14,26.4,10.6),
+inc=c(36.786,38.534,20.727,37.205,24.3,27.607,45.822,40.161,32.247,38.458,
+33.232,31.715,29.505,25.896,28.919,30.776,25.552,42.886,34.297,29.96,34.009,
+35.008,41.658,34.109,27.65,34.654,27.117,39.04,33.698,32.32,25.144,45.14,
+29.805,25.008,25.993,32.231,36.912,28.624,37.054,39.587,31.324,37.092,
+31.948,30.801,23.748,44.619),
+num = c(5, 5, 4, 5, 5, 4, 3, 6, 5, 4, 
+3, 4, 5, 6, 7, 5, 4, 4, 4, 6, 
+8, 5, 5, 6, 5, 3, 2, 7, 5, 7, 
+5, 6, 3, 5, 4, 7, 2, 9, 3, 6, 
+5, 4, 6, 7, 5, 4
+),
+adj = c(
+33, 30, 24, 23, 4, 
+41, 38, 32, 19, 6, 
+25, 15, 6, 5, 
+39, 37, 30, 23, 1, 
+38, 25, 15, 6, 3, 
+38, 5, 3, 2, 
+27, 25, 15, 
+45, 38, 22, 18, 14, 10, 
+43, 40, 38, 32, 14, 
+22, 18, 15, 8, 
+46, 44, 42, 
+46, 44, 29, 20, 
+35, 31, 29, 28, 16, 
+45, 43, 38, 21, 9, 8, 
+38, 25, 18, 10, 7, 5, 3, 
+35, 31, 28, 21, 13, 
+35, 34, 26, 21, 
+38, 15, 10, 8, 
+41, 33, 24, 2, 
+44, 40, 36, 29, 28, 12, 
+45, 43, 35, 34, 31, 17, 16, 14, 
+45, 34, 26, 10, 8, 
+42, 39, 30, 4, 1, 
+41, 36, 33, 30, 19, 1, 
+27, 15, 7, 5, 3, 
+34, 22, 17, 
+25, 7, 
+43, 40, 31, 29, 20, 16, 13, 
+46, 28, 20, 13, 12, 
+44, 42, 36, 24, 23, 4, 1, 
+43, 28, 21, 16, 13, 
+41, 40, 38, 36, 9, 2, 
+24, 19, 1, 
+45, 26, 22, 21, 17, 
+21, 17, 16, 13, 
+44, 41, 40, 32, 30, 24, 20, 
+39, 4, 
+32, 18, 15, 14, 9, 8, 6, 5, 2, 
+37, 23, 4, 
+43, 36, 32, 28, 20, 9, 
+36, 32, 24, 19, 2, 
+44, 30, 23, 11, 
+40, 31, 28, 21, 14, 9, 
+46, 42, 36, 30, 20, 12, 11, 
+34, 22, 21, 14, 8, 
+44, 29, 12, 11
+),sumNumNeigh = 228)
+LconvData<-list(y=c(0,7,1,5,1,1,5,16,0,17,4,0,0,1,1,7,1,3,0,0,8,2,13,7,0,8,0,3,2,4,1,11,0,1,2,3,3,8,6,14,3,11,6,0,1,5))
+LconvInits<-list(a0=0.1,tauv=0.1,tau0=0.1,tauB=0.1,v=rep(0.0,LconvConsts$N),B=rep(0.0,LconvConsts$N))
+
+Lconv<-nimbleModel(code=LconvCode,name="Lconv",constants=LconvConsts,
+data=LconvData,inits=LconvInits)
+#####################################################################
+
+######################## Compile  ######################################
+CLconv<-compileNimble(Lconv)
+CLconv$v
+
+
+mcmc.out<-nimbleMCMC(code=LconvCode,constants=LconvConsts,
+data=LconvData,inits=LconvInits,nchains=2,nburnin=7000,niter=10000,summary=TRUE,WAIC=TRUE)
+
+mcmc.out$summary
+mcmc.out$WAIC
+
+#####################################################################
+
+LconvConf<-configureMCMC(Lconv,print=TRUE,enableWAIC=nimbleOptions('enableWAIC'=TRUE))
+LconvConf$addMonitors(c("a0","tauv","tau0","v","B","theta"))
+LconvMCMC<-buildMCMC(LconvConf)
+CLconvMCMC<-compileNimble(LconvMCMC,project=Lconv)
+niter<-10000
+set.seed(1)
+samples<-runMCMC(CLconvMCMC,niter=niter,nburnin=7000,nchains=2,summary=TRUE,samplesAsCodaMCMC=TRUE)  
+# if specifying CODA then use library(coda); then gelman.diag or geweke.diag and plot
+# the samples$samples is an mcmc.list   eg samples$samples$chain1 lists the first chain etc
+# to access elements of the output eg samples$samples$chain1[,"B[1]"] will yield the sampler values for B[1]
+#as a vector. Diagnostics: geweke.diag(samples$samples$chain1[,"B[1]"]), plotting: plot(samples$samples$chain1[,"B[1]"])
+
+samples$summary  # this a list as two chains run and its CODA?
+
+samples$summary$all.chains  # this is a matrix of 142 by 5....first column is th mean
+Bmean<-samples$summary$all.chains[1:46,1]   #gives B values
+a0mean<-samples$summary$all.chains[47,1]
+tau0mean<-samples$summary$all.chains[48,1]
+tauBmean<-samples$summary$all.chains[49,1]
+tauvmean<-samples$summary$all.chains[50,1]
+THmean<-samples$summary$all.chains[51:96,1]
+vmean<-samples$summary$all.chains[97:142,1]
+
+#### now a single chain ruun without CODA  ########################################
+
+samples2<-runMCMC(CLconvMCMC,niter=niter,nburnin=7000,summary=TRUE,WAIC=TRUE)
+samples2$WAIC
+samples2$summary   # this is a matrix as only 1 chian run
+
+#####    posterior means  ####################################################
+Bmean<-samples2$summary[1:46,1]  # mean of B values
+a0mean<-samples2$summary[47,1]
+tau0mean<-samples2$summary[48,1]
+tauBmean<-samples2$summary[49,1]
+tauvmean<-samples2$summary[50,1]
+THmean<-samples2$summary[51:96,1]
+vmean<-samples2$summary[97:142,1]
+WAIC<-samples2$WAIC
+
+sampMAT<-as.matrix(samples2$samples)  ### dim is 3000 by 142
+par(mfrow=c(2,2))
+plot(sampMAT[,"a0"],type="l",xlab="iteration",ylab=expression(a0))
+plot(sampMAT[,"tauv"],type="l",xlab="iteration",ylab=expression(tauv))
+plot(sampMAT[,"tau0"],type="l",xlab="iteration",ylab=expression(tau0))
+plot(sampMAT[,"tauB"],type="l",xlab="iteration",ylab=expression(tauB))
+x11();par(mfrow=c(2,2))
+plot(density(sampMAT[,"a0"]),xlab=expression(a0))
+plot(density(sampMAT[,"tauv"]),xlab=expression(tauv))
+plot(density(sampMAT[,"tau0"]),xlab=expression(tau0))
+plot(density(sampMAT[,"tauB"]),xlab=expression(tauB))
+x11()
+setwd("C:/A_NEW_FOLDER/A_NEW_FOLDER/A_PROGRAMS/R_program/NIMBLE examples")
+library(maptools)
+library(sp)
+source("fillmap.R")
+SCpoly<-readSplus("SC_SPLus_export_map.txt")
+plot(SCpoly)
+
+fillmap(SCpoly,"UH",vmean,n.col=5);x11()
+fillmap(SCpoly,"Theta",THmean,n.col=5);x11()
+fillmap(SCpoly,"CH",Bmean,n.col=5)
\ No newline at end of file
diff --git a/STinT_models_Ohio_Nimble.txt b/STinT_models_Ohio_Nimble.txt
new file mode 100644
index 0000000..e6832a6
--- /dev/null
+++ b/STinT_models_Ohio_Nimble.txt
@@ -0,0 +1,632 @@
+#### range of ST interaction models in Nimble  ########
+library(nimble)
+
+######################################################
+###### Ohio Data  ####################################
+e=structure(.Data=c(11.08265, 12.43046, 12.58617, 13.07113, 13.64872, 13.44648, 14.20172, 14.38548, 14.95299, 15.44726, 
+51.54766, 57.20417, 57.04018, 58.30136, 61.00702, 60.78989, 64.1098, 64.32432, 66.04699, 67.65539, 
+21.18653, 23.58796, 23.70401, 24.58979, 25.74367, 25.60512, 27.00496, 26.73573, 27.48413, 28.12705, 
+48.02128, 53.02829, 52.80107, 54.51591, 56.9513, 56.11595, 58.9642, 58.08966, 58.91479, 59.60639, 
+25.54036, 28.85985, 29.14606, 30.34142, 31.9459, 31.69799, 33.39785, 33.45538, 34.54226, 35.47209, 
+19.65611, 21.73145, 21.82331, 22.744, 23.8938, 23.95328, 25.31747, 25.35509, 26.03825, 26.72759, 
+38.56623, 42.0636, 41.97363, 43.43165, 45.88792, 44.81808, 46.23249, 44.58007, 45.22339, 45.36022, 
+14.60163, 16.3542, 16.67166, 17.49995, 18.45894, 18.54305, 19.89741, 19.88945, 20.67724, 21.42489, 
+118.7359, 132.3991, 133.8755, 139.09, 145.8855, 146.4967, 155.7944, 157.4248, 163.6123, 169.2534, 
+11.82541, 13.03703, 13.08939, 13.6795, 14.60024, 14.62257, 15.47169, 15.4304, 15.81459, 16.15364, 
+15.67667, 17.15562, 17.08506, 17.82085, 18.65204, 18.47113, 19.61539, 19.92314, 20.61976, 21.28398, 
+69.8358, 76.60449, 76.37443, 78.53021, 82.3222, 81.24256, 85.3237, 85.31212, 87.24273, 89.11415, 
+57.71282, 65.82852, 67.17506, 70.67236, 74.95687, 75.22547, 80.34092, 81.57098, 85.39924, 88.77426, 
+16.01126, 17.66246, 17.66381, 18.29873, 19.23135, 19.14769, 20.11954, 20.21182, 20.97767, 21.57185, 
+52.67453, 57.85574, 57.74398, 60.01879, 62.86696, 61.75908, 64.5058, 62.88037, 64.11284, 64.84256, 
+16.55965, 18.39023, 18.49928, 19.18715, 20.17, 20.04829, 20.82488, 20.73979, 21.24078, 21.6668, 
+23.48562, 25.5304, 25.47703, 26.19371, 27.21013, 27.18946, 28.543, 28.23602, 28.82986, 29.35596, 
+702.4902, 762.9026, 756.0005, 779.8555, 822.0158, 809.6126, 845.5759, 844.0905, 855.8276, 869.7341, 
+25.70603, 28.01751, 27.7992, 28.40706, 29.93484, 29.77232, 31.3417, 31.05596, 31.78028, 32.41493, 
+18.48065, 20.33881, 20.22841, 20.71682, 21.59112, 21.23599, 22.69088, 22.68849, 23.27034, 23.87908, 
+24.805, 27.5521, 27.93598, 29.38989, 31.19252, 31.27092, 33.3397, 34.18607, 35.54015, 36.99492, 
+36.24261, 40.57072, 40.45171, 41.55404, 43.33169, 42.92893, 44.9724, 44.73515, 45.64945, 46.47362, 
+42.30873, 47.8858, 48.49051, 50.51246, 53.0288, 52.6939, 55.83695, 56.66023, 58.79983, 60.75547, 
+12.7047, 13.97347, 13.9019, 14.48464, 15.41407, 15.19235, 15.95782, 15.92759, 16.30998, 16.69189, 
+406.4846, 444.3345, 447.9958, 467.6531, 495.0504, 493.6799, 522.6015, 529.1328, 546.3256, 564.1479, 
+17.45652, 19.26888, 19.32098, 20.00927, 21.10864, 21.07612, 22.27337, 21.93282, 22.58947, 23.08501, 
+13.63535, 15.36918, 15.39472, 15.77881, 16.67008, 16.62236, 17.48306, 17.37735, 17.9514, 18.41551, 
+34.03191, 37.97007, 38.03563, 39.53481, 41.63126, 41.20462, 43.48262, 44.34541, 45.61567, 47.0657, 
+60.06467, 66.19062, 66.30437, 69.08276, 72.43422, 71.29946, 75.27091, 75.69876, 78.17459, 80.2487, 
+19.00359, 21.44709, 21.43185, 22.10858, 23.18576, 22.81702, 23.65325, 23.04279, 23.46055, 23.6662, 
+407.7868, 445.5072, 444.9867, 461.4336, 484.2658, 478.9633, 503.4001, 507.0756, 517.8945, 530.1678, 
+29.92659, 32.92372, 33.0029, 34.45148, 36.3201, 36.24579, 38.31725, 37.87029, 38.78331, 39.59003, 
+14.95705, 16.66925, 16.62521, 17.09685, 17.73578, 17.54398, 18.44368, 18.25615, 18.80826, 19.24466, 
+8.223136, 9.199208, 9.044219, 9.307426, 9.734615, 9.357822, 9.683545, 9.43737, 9.2791, 9.210176, 
+13.21699, 14.46189, 14.36684, 14.86706, 15.76502, 15.66644, 16.51372, 16.39807, 16.78344, 17.14971, 
+15.48276, 17.13619, 17.35708, 17.92799, 18.85522, 18.89875, 19.95381, 20.02827, 20.82716, 21.48658, 
+11.08913, 12.39415, 12.39784, 12.78047, 13.67559, 13.66001, 14.29243, 14.34889, 14.86173, 15.29123, 
+13.66543, 15.03009, 15.261, 15.9618, 16.94154, 16.78611, 17.82091, 17.68577, 18.01362, 18.35322, 
+25.26315, 27.883, 27.92015, 28.84994, 30.42291, 30.3825, 31.86446, 32.1276, 33.03179, 33.8531, 
+14.05833, 15.58909, 15.45903, 15.98089, 16.97009, 16.77393, 17.66914, 17.47493, 17.97451, 18.35927, 
+42.13704, 46.5668, 46.22499, 47.71302, 49.26917, 48.36682, 50.33897, 48.49604, 48.90682, 49.00401, 
+21.60812, 23.64268, 23.77036, 24.95947, 26.3241, 26.09414, 27.57889, 27.29623, 28.12766, 28.81045, 
+98.29676, 108.4951, 108.9542, 113.7492, 119.6884, 117.9353, 123.3566, 123.7011, 126.4734, 129.3168, 
+29.23195, 32.54986, 32.50121, 33.32862, 35.20066, 34.97511, 36.30995, 36.13476, 36.90544, 37.5362, 
+55.50258, 61.91143, 62.72108, 65.61343, 69.11733, 68.54123, 72.42336, 72.3067, 74.21561, 76.06302, 
+17.98687, 19.98131, 20.0411, 20.71045, 21.93815, 21.89872, 23.17992, 23.14502, 23.98498, 24.67195, 
+126.3616, 139.9924, 139.737, 144.8432, 151.641, 149.4939, 156.9894, 155.4053, 158.835, 161.6906, 
+218.5626, 240.4953, 239.8109, 248.0714, 260.3825, 256.0689, 269.0686, 269.1015, 274.4122, 280.0392, 
+15.02646, 16.88968, 17.11976, 18.04467, 19.3265, 18.94633, 20.13931, 20.34076, 20.98775, 21.63898, 
+133.9614, 147.3142, 146.1641, 150.3313, 158.2776, 155.0762, 161.9477, 159.0297, 161.3286, 163.2364, 
+31.10297, 34.60226, 34.48909, 35.66503, 37.35222, 36.75197, 38.38354, 37.69953, 38.50183, 39.11044, 
+51.48056, 57.77391, 58.37173, 60.84568, 64.23379, 63.78598, 67.20449, 66.97814, 69.27415, 71.1111, 
+10.62496, 12.03359, 12.02476, 12.55771, 13.17856, 13.06422, 13.79991, 13.74192, 14.07124, 14.38829, 
+17.7365, 19.55017, 19.57361, 20.45003, 21.51948, 21.33668, 22.57982, 22.33302, 22.99301, 23.53497, 
+41.81355, 46.1152, 46.15864, 47.72734, 49.8552, 49.26023, 51.80665, 52.06061, 53.43704, 54.81957, 
+7.922329, 8.841202, 8.741061, 9.016768, 9.394306, 9.198502, 9.534684, 9.128947, 9.167696, 9.14244, 
+264.8869, 291.6328, 290.8818, 300.4148, 314.7764, 312.2435, 328.7067, 329.3978, 337.411, 345.2845, 
+6.43588, 7.257283, 7.257423, 7.573024, 7.930644, 7.847051, 8.22924, 8.173478, 8.375426, 8.532823, 
+12.47516, 13.51164, 13.67121, 14.16481, 14.84484, 14.80125, 15.68975, 15.67493, 16.16302, 16.61811, 
+38.55466, 42.55919, 42.73356, 44.77462, 46.89149, 46.67626, 48.96491, 48.30727, 49.45495, 50.26619, 
+5.165549, 5.765423, 5.743163, 5.979708, 6.258207, 6.210151, 6.660381, 6.547146, 6.696667, 6.819485, 
+18.76665, 20.43444, 20.47185, 21.09605, 22.19562, 21.92915, 23.11014, 23.05906, 23.47892, 23.98129, 
+9.838238, 10.83785, 10.78763, 11.07418, 11.64549, 11.46604, 12.08742, 12.00175, 12.39663, 12.69068, 
+14.30453, 15.85453, 16.0189, 16.63488, 17.36357, 17.39849, 18.35704, 18.34967, 18.87226, 19.35897, 
+20.39795, 22.21271, 22.12289, 22.75408, 23.67607, 23.56273, 26.124, 26.47552, 27.2708, 28.31332, 
+10.36488, 11.71599, 12.09417, 12.92527, 13.78977, 13.75626, 14.58608, 14.28558, 14.77699, 15.10677, 
+62.13098, 69.43773, 70.19184, 72.84647, 76.47707, 75.42849, 79.18259, 79.55898, 81.5173, 83.49575, 
+17.51529, 19.46834, 19.43428, 20.05223, 21.26592, 21.41523, 22.6589, 22.46312, 23.19864, 23.76841, 
+15.89973, 16.85797, 17.00544, 17.59914, 18.50092, 18.49658, 19.38861, 19.35567, 19.87964, 20.35625, 
+60.26829, 66.856, 66.74379, 68.72845, 71.79167, 71.5495, 75.14473, 74.25946, 75.812, 77.07059, 
+30.01128, 33.21524, 33.46478, 34.83443, 36.98001, 37.29299, 39.37498, 38.87397, 40.03778, 40.8903, 
+29.4962, 32.23379, 32.03269, 33.0141, 34.59225, 34.50268, 36.09945, 35.85596, 36.62397, 37.28642, 
+39.07806, 43.08545, 42.90556, 44.69506, 47.42099, 46.62039, 48.37818, 47.44009, 48.39128, 48.90422, 
+29.00427, 31.49937, 31.23754, 32.38823, 34.20381, 33.9683, 35.91977, 35.55103, 36.51612, 37.27735, 
+19.92545, 22.00916, 22.19128, 23.07338, 24.07907, 24.00417, 25.44191, 25.45151, 26.11824, 26.75481, 
+174.7026, 193.1358, 193.0245, 200.3059, 210.1053, 207.67, 217.5818, 215.3065, 219.1714, 222.7557, 
+243.7286, 267.0859, 265.44, 274.3023, 287.5316, 283.302, 295.7671, 295.0601, 301.478, 307.4574, 
+111.7262, 123.315, 123.1225, 127.2642, 132.7925, 130.4624, 136.5146, 134.1485, 135.9463, 137.436, 
+38.89249, 43.20411, 43.4246, 45.23235, 47.70868, 47.30137, 49.71968, 48.81783, 49.89583, 50.72703, 
+13.60943, 15.1017, 15.23089, 16.07265, 16.88501, 16.89564, 17.95465, 18.30668, 18.94278, 19.6166, 
+14.08841, 15.50726, 15.41871, 15.88489, 16.62306, 16.54049, 17.44178, 17.14676, 17.56031, 17.88089, 
+5.246998, 5.882542, 5.80798, 6.009941, 6.37127, 6.390492, 6.649333, 6.685965, 6.812811, 6.950722, 
+45.4547, 50.73245, 51.18831, 53.51505, 56.3983, 56.37872, 59.76026, 60.88114, 63.63759, 66.06119, 
+29.50638, 32.76467, 32.82683, 34.19052, 36.21935, 35.71362, 37.41943, 36.98278, 37.74334, 38.37079, 
+45.04144, 49.70088, 50.01446, 52.3609, 55.26991, 54.94208, 58.23152, 58.24706, 60.07682, 61.70376, 
+16.69849, 18.5631, 18.56716, 19.11343, 20.07093, 20.06876, 21.27903, 20.93902, 21.5981, 22.07382, 
+49.07087, 54.90015, 55.5739, 57.83726, 60.39749, 60.01984, 63.66206, 63.51696, 65.07695, 66.56497, 
+10.55323, 11.56767, 11.5246, 11.92865, 12.53544, 12.49332, 13.16783, 12.85789, 13.16342, 13.39404),.Dim=c(10,88))
+e<-t(e)
+LSTConsts<-list(L=463,m=88,e,
+m=88, T=10,t=c(1,2,3,4,5,6,7,8,9,10),
+sumNumNeigh=463,
+adj=c(
+8, 36, 66, 73, 
+6, 81, 69, 32, 33, 
+42, 70, 39, 47, 52, 85, 38,
+43, 28, 78,
+53, 82, 37, 64, 58, 84,
+75, 19, 54, 81, 2, 33, 46,
+56, 61, 30, 34, 41,
+13, 14, 36, 1,
+31, 68, 57, 83,
+34, 79, 76, 15, 41,
+12, 55, 75, 46, 80, 49,
+29, 57, 55, 11, 49,
+31, 83, 14, 8,
+8, 13, 83, 29, 24, 36,
+41, 10, 76, 50,
+60, 45, 42, 38, 79, 30,
+59, 51, 88, 74, 39, 70,
+47, 52, 77, 67, 28, 43,
+68, 57, 55, 75, 6, 54,
+86, 35, 69, 63,
+25, 80, 51, 59, 42, 45,
+62, 72, 39, 47,
+65, 25, 45, 64, 37,
+36, 14, 29, 49, 65, 71,
+65, 49, 80, 21, 45, 23, 
+86, 35, 48,
+44, 40, 82, 53,
+67, 77, 18, 43, 4, 78,
+14, 83, 57, 12, 49, 24,
+61, 60, 16, 79, 34, 7,
+9, 83, 13,
+33, 2, 69, 35, 87, 74, 88,
+80, 46, 6, 2, 32, 88, 51,
+7, 30, 79, 10, 41,
+32, 69, 20, 86, 26, 48, 87,
+1, 8, 14, 24, 71, 66,
+82, 71, 65, 23, 64, 5,
+16, 42, 3, 85, 85, 76, 79,
+70, 17, 74, 72, 22, 47, 3,
+44, 73, 66, 71, 82, 27,
+7, 34, 10, 15,
+45, 21, 59, 70, 3, 38, 16,
+18, 28, 4,
+73, 40, 27,
+64, 23, 25, 21, 42, 16, 60,
+11, 75, 6, 33, 80,
+22, 39, 3, 52, 18,
+26, 35, 87, 62,
+24, 29, 12, 11, 80, 25, 65,
+15, 76, 67, 78,
+21, 80, 33, 88, 17, 59,
+85, 3, 47, 18, 77,
+27, 82, 5,
+19, 6, 81,
+57, 19, 75, 11, 12,
+84, 61, 7,
+83, 9, 68, 19, 55, 12, 29,
+5, 64, 60, 61, 84,
+21, 51, 17, 70, 42,
+58, 64, 45, 16, 30, 61,
+58, 60, 30, 7, 56, 84,
+48, 87, 72, 22,
+81, 69, 20,
+37, 23, 45, 60, 58, 5,
+71, 24, 49, 25, 23, 37,
+73, 1, 36, 71, 40,
+76, 77, 18, 28, 78, 50,
+9, 57, 19,
+2, 81, 63, 20, 35, 87, 32,
+42, 59, 17, 39, 3,
+66, 36, 24, 65, 37, 82, 40,
+74, 87, 62, 22, 39,
+1, 66, 40, 44,
+17, 88, 32, 87, 72, 39,
+55, 19, 6, 46, 11,
+10, 79, 38, 85, 77, 67, 50, 15,
+76, 85, 52, 18, 28, 67,
+50, 67, 28, 4,
+30, 16, 38, 76, 10, 34,
+49, 11, 46, 33, 51, 21, 25,
+54, 6, 2, 69, 63,
+40, 71, 37, 5, 53, 27,
+13, 31, 9, 57, 29, 14,
+5, 58, 61, 56,
+38, 3, 52, 77, 76,
+20, 35, 26,
+32, 69, 35, 48, 62, 72, 74,
+51, 33, 32, 74, 17),
+num=c(4,5,7,3,6,7,5,4,4,5,6,5,4,6,4,6,6,6,6,
+4,6,4,5,6,6,3,4,6,6,6,3,7,7,5,7,6,6,7,7,6,
+4,7,3,3,7,5,5,4,7,4,6, 5,3,3,5,3,7,5,5,6,
+6,4,3,6,6,5,6,3,7,5,7,5,4,6,5,8,6,4,6,
+7,5,6,6,4,5,3,7,5))
+names(LSTConsts)[3]<-'e'
+y=structure(.Data=c(5, 14, 12, 15, 9, 12, 20, 12, 16, 15,
+76, 46, 53, 47, 62, 69, 53, 65, 69, 58,
+11, 22, 18, 20, 27, 25, 34, 19, 28, 30,
+38, 58, 56, 53, 59, 56, 56, 50, 53, 65,
+30, 25, 33, 24, 21, 38, 21, 15, 27, 24,
+18, 17, 20, 23, 16, 35, 22, 23, 19, 22,
+40, 54, 64, 47, 45, 55, 44, 50, 62, 70,
+7, 21, 12, 22, 27, 24, 19, 23, 30, 23,
+91, 135, 129, 138, 150, 141, 139, 164, 159, 158,
+12, 7, 12, 14, 16, 17, 11, 8, 19, 10,
+12, 17, 14, 20, 19, 18, 19, 19, 11, 21,
+60, 99, 80, 88, 73, 87, 92, 95, 114, 105,
+51, 62, 71, 59, 51, 75, 94, 80, 89, 75,
+11, 11, 16, 15, 19, 17, 26, 19, 22, 24,
+55, 51, 63, 64, 62, 74, 65, 68, 67, 60,
+13, 22, 13, 13, 18, 15, 11, 28, 13, 19,
+23, 28, 21, 37, 21, 26, 35, 30, 24, 31,
+825, 917, 913, 909, 940, 923, 937, 953, 952, 993,
+22, 18, 18, 23, 23, 24, 37, 28, 28, 35,
+15, 12, 13, 12, 10, 16, 12, 25, 16, 22,
+25, 17, 10, 24, 27, 14, 24, 23, 28, 22,
+33, 41, 32, 36, 40, 42, 40, 43, 32, 54,
+41, 33, 38, 48, 53, 33, 54, 51, 54, 45,
+15, 14, 22, 15, 15, 14, 16, 23, 17, 18,
+402, 402, 431, 451, 478, 444, 524, 499, 488, 543,
+12, 9, 15, 8, 16, 18, 9, 7, 16, 11,
+20, 15, 17, 12, 15, 10, 6, 16, 19, 29,
+20, 22, 29, 36, 25, 20, 20, 38, 28, 30,
+49, 44, 44, 36, 57, 50, 71, 50, 60, 52,
+17, 22, 24, 31, 32, 19, 22, 18, 33, 27,
+528, 583, 542, 554, 568, 642, 639, 572, 593, 576,
+24, 29, 22, 24, 41, 27, 31, 33, 36, 29,
+11, 18, 15, 18, 25, 15, 26, 19, 20, 21,
+7, 8, 12, 20, 22, 7, 7, 16, 12, 14,
+6, 7, 13, 12, 2, 12, 23, 16, 9, 9,
+24, 25, 13, 17, 24, 24, 25, 19, 21, 24,
+9, 14, 14, 15, 15, 18, 18, 10, 17, 12,
+4, 3, 8, 9, 5, 6, 8, 9, 6, 8,
+26, 25, 28, 23, 18, 21, 21, 33, 23, 16,
+21, 22, 12, 19, 14, 14, 15, 15, 26, 18,
+57, 55, 68, 65, 54, 68, 56, 71, 68, 77,
+25, 15, 22, 27, 28, 26, 26, 23, 27, 37,
+87, 101, 83, 94, 114, 103, 127, 111, 108, 147,
+35, 39, 26, 45, 36, 41, 37, 33, 46, 49,
+57, 69, 72, 57, 68, 84, 61, 76, 61, 81,
+11, 19, 31, 24, 24, 20, 25, 24, 30, 25,
+121, 115, 110, 136, 125, 124, 124, 137, 136, 149,
+280, 243, 282, 277, 292, 243, 308, 305, 290, 305,
+10, 10, 13, 14, 24, 20, 21, 12, 15, 16,
+137, 142, 159, 163, 148, 147, 183, 200, 163, 187,
+25, 27, 36, 41, 37, 26, 40, 50, 40, 55,
+27, 38, 40, 36, 34, 41, 45, 31, 40, 42,
+19, 12, 14, 12, 9, 14, 17, 15, 19, 16,
+16, 12, 15, 14, 14, 9, 15, 15, 12, 12,
+44, 48, 37, 49, 63, 39, 48, 47, 44, 42,
+7, 4, 7, 6, 7, 7, 6, 6, 8, 11,
+259, 314, 306, 325, 345, 326, 341, 374, 361, 340,
+7, 8, 8, 4, 7, 6, 11, 11, 13, 11,
+9, 16, 16, 6, 13, 9, 12, 10, 14, 10,
+49, 54, 59, 44, 59, 62, 60, 45, 75, 66,
+6, 3, 2, 5, 5, 10, 9, 7, 2, 5,
+10, 15, 21, 18, 26, 25, 28, 14, 26, 26,
+13, 11, 15, 8, 9, 11, 6, 11, 14, 9,
+10, 21, 15, 25, 20, 23, 23, 25, 14, 14,
+27, 23, 15, 23, 21, 24, 22, 23, 26, 27,
+8, 14, 10, 10, 20, 17, 8, 11, 13, 23,
+38, 37, 49, 66, 55, 65, 58, 44, 62, 55,
+13, 24, 13, 21, 27, 21, 22, 26, 18, 18,
+11, 13, 14, 9, 13, 10, 12, 16, 14, 11,
+45, 62, 66, 63, 59, 66, 50, 79, 79, 67,
+27, 23, 33, 28, 34, 36, 29, 25, 36, 42,
+23, 27, 23, 26, 24, 25, 34, 33, 22, 32,
+41, 54, 38, 56, 44, 70, 56, 71, 58, 67,
+19, 30, 17, 28, 36, 39, 25, 31, 41, 40,
+9, 19, 21, 19, 30, 23, 22, 21, 16, 22,
+182, 197, 183, 188, 206, 198, 205, 217, 244, 224,
+247, 277, 253, 282, 310, 302, 292, 293, 323, 337,
+91, 114, 112, 111, 133, 114, 139, 143, 146, 146,
+28, 36, 40, 54, 57, 37, 58, 49, 38, 51,
+4, 17, 9, 9, 14, 11, 10, 14, 12, 6,
+9, 13, 12, 17, 13, 17, 13, 15, 17, 10,
+6, 5, 7, 7, 13, 3, 6, 7, 9, 4,
+43, 33, 37, 40, 42, 42, 55, 40, 56, 59,
+20, 26, 34, 30, 32, 39, 27, 40, 40, 40,
+36, 27, 28, 30, 30, 31, 46, 38, 45, 32,
+10, 17, 23, 10, 13, 17, 17, 13, 22, 17,
+28, 32, 30, 30, 44, 37, 55, 40, 45, 30,
+7, 7, 11, 5, 10, 16, 10, 11, 21, 16),.Dim=c(10,88))
+y<-t(y)
+LSTdata<-list(y)
+names(LSTdata)<-'y'
+##########################################################
+###### inits ##############################################
+LSTinits<-list(a0=0.1,sdv=0.1,sdpsi=0.1,sdg=0.1,sdu=0.1,g=c(0,0,0,0,0,0,0,0,0,0),
+v=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0),
+u=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0),
+psi=structure(.Data=rep(0.1,880),.Dim=c(88,10)))
+
+###########################################################
+########## model running  ################################
+
+LST<-nimbleModel(code=STCode,name="LST",constants=LSTConsts,
+data=LSTdata,inits=LSTinits)
+#####################################################################
+######################## Compile  ######################################
+CLST<-compileNimble(LST)
+CLST$v
+################## Running with monitors and CODA output  #####
+LSTConf<-configureMCMC(LST,monitors=CLST$getNodeNames(stochOnly=TRUE,includeData=FALSE),print=TRUE)
+##LSTConf$addMonitors(c("a0","g","u","v"))
+LSTMCMC<-buildMCMC(LSTConf)
+CLSTMCMC<-compileNimble(LSTMCMC,project=LST)
+niter<-20000
+set.seed(1)
+samples<-runMCMC(CLSTMCMC,niter=niter,nburnin=15000,nchains=1,summary=TRUE,WAIC=TRUE,samplesAsCodaMCMC=TRUE)
+#samples$summary
+samples$WAIC
+ 
+##############################################################
+##### models for Ohio lung cancer ###########################
+### KH model ##############################################
+STCode<-nimbleCode(
+{
+for (i in 1:m)
+{
+	for (k in 1:T)
+	{y[i,k]~dpois(mu[i,k])
+       log(mu[i,k])<-log(e[i,k])+log(theta[i,k])
+log(theta[i,k])<-a0+g[k]+u[i]+v[i]+psi[i,k]
+psi[i,k]~dnorm(0,taupsi)
+	}	
+v[i]~dnorm(0,tauv)
+}
+g[1]~dnorm(0,taug)
+for (k in 2:T){
+      g[k]~dnorm(g[k-1],taug)
+		}
+		u[1:m]~dcar_normal(adj[1:L],weights[1:L],num[1:m],tauu)
+		for(k in 1:sumNumNeigh) {
+		weights[k] <- 1}
+a0~dflat()
+tauu<-1/pow(sdu,2)
+sdu~dunif(0,10)
+taug<-1/pow(sdg,2)
+sdg~dunif(0,10)
+taupsi<-1/pow(sdpsi,2)
+sdpsi~dunif(0,2)
+tauv<-1/pow(sdv,2)
+sdv~dunif(0,10)
+for(k in 1:T){t1[k]<-t[k]}
+})
+LSTinits<-list(a0=0.1,sdv=0.1,sdpsi=0.1,sdg=0.1,sdu=0.1,g=c(0,0,0,0,0,0,0,0,0,0),
+v=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0),
+u=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0),
+psi=structure(.Data=rep(0.1,880),.Dim=c(88,10)))
+#########################################################
+############## model with no interaction  ###############
+STCode<-nimbleCode(
+{
+for (i in 1:m)
+{
+	for (k in 1:T)
+	{y[i,k]~dpois(mu[i,k])
+       log(mu[i,k])<-log(e[i,k])+log(theta[i,k])
+log(theta[i,k])<-a0+g[k]+u[i]+v[i]
+	}	
+v[i]~dnorm(0,tauv)
+}
+g[1]~dnorm(0,taug)
+for (k in 2:T){
+      g[k]~dnorm(g[k-1],taug)
+		}
+		u[1:m]~dcar_normal(adj[1:L],weights[1:L],num[1:m],tauu)
+		for(k in 1:sumNumNeigh) {
+		weights[k] <- 1}
+a0~dflat()
+tauu<-1/pow(sdu,2)
+sdu~dunif(0,10)
+taug<-1/pow(sdg,2)
+sdg~dunif(0,10)
+tauv<-1/pow(sdv,2)
+sdv~dunif(0,10)
+for(k in 1:T){t1[k]<-t[k]}
+})
+LSTinits<-list(a0=0.1,sdv=0.1,sdg=0.1,sdu=0.1,g=c(0,0,0,0,0,0,0,0,0,0),
+v=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0),
+u=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0))
+LST<-nimbleModel(code=STCode,name="LST",constants=LSTConsts,
+data=LSTdata,inits=LSTinits)
+#####################################################################
+######################## Compile  ######################################
+CLST<-compileNimble(LST)
+#CLST$v
+################## Running with monitors and CODA output  #####
+LSTConf<-configureMCMC(LST,monitors=CLST$getNodeNames(stochOnly=TRUE,includeData=FALSE),print=TRUE)
+##LSTConf$addMonitors(c("a0","u","v","theta"))
+LSTMCMC<-buildMCMC(LSTConf)
+CLSTMCMC<-compileNimble(LSTMCMC,project=LST)
+niter<-20000
+set.seed(1)
+samples<-runMCMC(CLSTMCMC,niter=niter,nburnin=15000,nchains=1,summary=TRUE,WAIC=TRUE,samplesAsCodaMCMC=TRUE)
+samples$summary
+samples$WAIC
+############################################################
+################# model with psi and no time  ############## 
+STCode<-nimbleCode(
+{
+for (i in 1:m)
+{
+	for (k in 1:T)
+	{y[i,k]~dpois(mu[i,k])
+       log(mu[i,k])<-log(e[i,k])+log(theta[i,k])
+log(theta[i,k])<-a0+u[i]+v[i]+psi[i,k]
+psi[i,k]~dnorm(0,taupsi)
+	}	
+v[i]~dnorm(0,tauv)
+}
+		u[1:m]~dcar_normal(adj[1:L],weights[1:L],num[1:m],tauu)
+		for(k in 1:sumNumNeigh) {
+		weights[k] <- 1}
+a0~dflat()
+tauu<-1/pow(sdu,2)
+sdu~dunif(0,10)
+taupsi<-1/pow(sdpsi,2)
+sdpsi~dunif(0,2)
+tauv<-1/pow(sdv,2)
+sdv~dunif(0,10)
+for(k in 1:T){t1[k]<-t[k]}
+})
+LSTinits<-list(a0=0.1,sdv=0.1,sdpsi=0.1,sdu=0.1,
+v=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0),
+u=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0),
+psi=structure(.Data=rep(0.1,880),.Dim=c(88,10)))
+###########################################################
+################ Space-time trend #########################
+STCode<-nimbleCode(
+{
+for (i in 1:m)
+{
+	for (k in 1:T)
+	{y[i,k]~dpois(mu[i,k])
+       log(mu[i,k])<-log(e[i,k])+log(theta[i,k])
+log(theta[i,k])<-a0+g[k]+u[i]+v[i]+psi[i,k]
+psi[i,k]~dnorm(0,taupsi)
+	}	
+v[i]~dnorm(0,tauv)
+}
+for (k in 1:T){
+      g[k]~dnorm(beta*t[k],taug)
+}
+u[1:m]~dcar_normal(adj[1:L],weights[1:L],num[1:m],tauu)
+for(k in 1:sumNumNeigh) {weights[k] <- 1}
+a0~dnorm(0,tau0)
+tauu<-1/pow(sdu,2)
+sdu~dunif(0,10)
+taug<-1/pow(sdg,2)
+sdg~dunif(0,10)
+taupsi<-1/pow(sdpsi,2)
+sdpsi~dunif(0,2)
+tauv<-1/pow(sdv,2)
+sdv~dunif(0,10)
+beta~dnorm(0,tauB)
+tauB<-1/pow(sdB,2)
+sdB~dunif(0,10)
+})
+LSTinits<-list(a0=0.1,sdv=0.1,sdpsi=0.1,sdg=0.1,sdu=0.1,g=c(0,0,0,0,0,0,0,0,0,0),
+v=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0),
+u=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0),
+psi=structure(.Data=rep(0.1,880),.Dim=c(88,10)))
+
+########################################################
+############# g UH and ST int  #########################
+
+STCode<-nimbleCode(
+{
+for (i in 1:m)
+{
+	for (k in 1:T)
+	{y[i,k]~dpois(mu[i,k])
+       log(mu[i,k])<-log(e[i,k])+log(theta[i,k])
+log(theta[i,k])<-a0+g[k]+u[i]+v[i]+psi[i,k]
+psi[i,k]~dnorm(0,taupsi)
+	}	
+v[i]~dnorm(0,tauv)
+}
+for (k in 1:T){
+      g[k]~dnorm(0,taug)
+}
+u[1:m]~dcar_normal(adj[1:L],weights[1:L],num[1:m],tauu)
+for(k in 1:sumNumNeigh) {weights[k] <- 1}
+a0~dnorm(0,tau0)
+tauu<-1/pow(sdu,2)
+sdu~dunif(0,10)
+taug<-1/pow(sdg,2)
+sdg~dunif(0,10)
+taupsi<-1/pow(sdpsi,2)
+sdpsi~dunif(0,2)
+tauv<-1/pow(sdv,2)
+sdv~dunif(0,10)
+
+})
+LSTinits<-list(a0=0.1,sdv=0.1,sdpsi=0.1,sdg=0.1,sdu=0.1,g=c(0,0,0,0,0,0,0,0,0,0),
+v=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0),
+u=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0),
+psi=structure(.Data=rep(0.1,880),.Dim=c(88,10)))
+
+##########################################################
+########### CONV + g trend    ############################
+STCode<-nimbleCode(
+{
+for (i in 1:m)
+{
+	for (k in 1:T)
+	{y[i,k]~dpois(mu[i,k])
+       log(mu[i,k])<-log(e[i,k])+log(theta[i,k])
+log(theta[i,k])<-a0+g[k]+u[i]+v[i]
+#
+	}	
+v[i]~dnorm(0,tauv)
+}
+g[1]~dnorm(0,taug)
+for (k in 2:T){
+      g[k]~dnorm(g[k-1],taug)
+}
+u[1:m]~dcar_normal(adj[1:L],weights[1:L],num[1:m],tauu)
+for(k in 1:sumNumNeigh) {weights[k] <- 1}
+a0~dnorm(0,tau0)
+tauu<-1/pow(sdu,2)
+sdu~dunif(0,10)
+taug<-1/pow(sdg,2)
+sdg~dunif(0,10)
+tauv<-1/pow(sdv,2)
+sdv~dunif(0,10)
+tau0~dgamma(2,0.5)
+})
+LSTinits<-list(a0=0.1,sdv=0.1,tau0=0.1,sdg=0.1,sdu=0.1,g=c(0,0,0,0,0,0,0,0,0,0),
+v=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0),
+u=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0))
+
+###########################################################
+############## g RW1 +conv  ###############################
+###########################################################
+STCode<-nimbleCode(
+{
+for (i in 1:m)
+{
+	for (k in 1:T)
+	{y[i,k]~dpois(mu[i,k])
+       log(mu[i,k])<-log(e[i,k])+log(theta[i,k])
+log(theta[i,k])<-a0+g[k]+u[i]+v[i]
+#
+	}	
+v[i]~dnorm(0,tauv)
+}
+g[1]~dnorm(0,taug)
+for (k in 2:T){
+      g[k]~dnorm(g[k-1],taug)
+}
+u[1:m]~dcar_normal(adj[1:L],weights[1:L],num[1:m],tauu)
+for(k in 1:sumNumNeigh) {weights[k] <- 1}
+a0~dnorm(0,tau0)
+tauu<-1/pow(sdu,2)
+sdu~dunif(0,10)
+taug<-1/pow(sdg,2)
+sdg~dunif(0,10)
+tauv<-1/pow(sdv,2)
+sdv~dunif(0,10)
+tau0~dgamma(2,0.5)
+})
+LSTinits<-list(a0=0.1,sdv=0.1,tau0=0.1,sdg=0.1,sdu=0.1,g=c(0,0,0,0,0,0,0,0,0,0),
+v=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0),
+u=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0))
+
+
+
+
+LST<-nimbleModel(code=STCode,name="LST",constants=LSTConsts,
+data=LSTdata,inits=LSTinits)
+#####################################################################
+######################## Compile  ######################################
+CLST<-compileNimble(LST)
+#CLST$v
+################## Running with monitors and CODA output  #####
+LSTConf<-configureMCMC(LST,monitors=CLST$getNodeNames(stochOnly=TRUE,includeData=FALSE),print=TRUE)
+##LSTConf$addMonitors(c("a0","u","v","theta"))
+LSTMCMC<-buildMCMC(LSTConf)
+CLSTMCMC<-compileNimble(LSTMCMC,project=LST)
+niter<-20000
+set.seed(1)
+samples<-runMCMC(CLSTMCMC,niter=niter,nburnin=15000,nchains=1,summary=TRUE,WAIC=TRUE,samplesAsCodaMCMC=TRUE)
+#samples$summary
+samples$WAIC
\ No newline at end of file