diff --git a/nimbleHMC/R/HMC_samplers.R b/nimbleHMC/R/HMC_samplers.R
index 821d4bd..598ea0b 100644
--- a/nimbleHMC/R/HMC_samplers.R
+++ b/nimbleHMC/R/HMC_samplers.R
@@ -127,24 +127,70 @@ hmc_checkTarget <- function(model, targetNodes, hmcType) {
     ## checks for:
     ## - target with discrete or truncated distribution
     ## - dependencies with truncated, dinterval, or dconstraint distribution
-    calcNodes <- model$getDependencies(targetNodes, stochOnly = TRUE)
-    if(any(model$isDiscrete(targetNodes)))
-        stop(paste0(hmcType, ' sampler cannot operate on discrete-valued nodes: ', paste0(targetNodes[model$isDiscrete(targetNodes)], collapse = ', ')))
-    if(any(model$isTruncated(targetNodes)))
-        stop(paste0(hmcType, ' sampler cannot operate on nodes with truncated prior distributions: ', paste0(targetNodes[model$isTruncated(targetNodes)], collapse = ', ')))
-    if(any(model$isTruncated(calcNodes)))
-        stop(paste0(hmcType, ' sampler cannot operate since these dependent nodes have truncated distributions, which do not support AD calculations: ', paste0(calcNodes[model$isTruncated(calcNodes)], collapse = ', ')))
-    if(any(model$getDistribution(calcNodes) == 'dinterval'))
-        stop(paste0(hmcType, ' sampler cannot operate since these dependent nodes have dinterval distributions, which do not support AD calculations: ', paste0(calcNodes[which(model$getDistribution(calcNodes) == 'dinterval')], collapse = ', ')))
-    if(any(model$getDistribution(calcNodes) == 'dconstraint'))
-        stop(paste0(hmcType, ' sampler cannot operate since these dependent nodes have dconstraint distributions, which do not support AD calculations: ', paste0(calcNodes[which(model$getDistribution(calcNodes) == 'dconstraint')], collapse = ', ')))
+    ##
+    targetDeclIDs_unique <- unique(model$getDeclID(targetNodes))
+    targetDeclInfo_unique <- model$getModelDef()$declInfo[targetDeclIDs_unique]
+    ##
+    ##if(any(model$isDiscrete(targetNodes)))
+    ##    stop(paste0(hmcType, ' sampler cannot operate on discrete-valued nodes: ', paste0(targetNodes[model$isDiscrete(targetNodes)], collapse = ', ')))
+    targetDists_unique <- unique(sapply(targetDeclInfo_unique, function(x) x$getDistributionName()))
+    targetDiscreteBool <- sapply(targetDists_unique, isDiscrete)
+    if(any(targetDiscreteBool)) {
+        stop(paste0(hmcType, ' sampler cannot operate on nodes with discrete-valued distributions: ', paste0(targetDists_unique[targetDiscreteBool], collapse = ', ')))
+    }
+    ##
+    ##if(any(model$isTruncated(targetNodes)))
+    ##    stop(paste0(hmcType, ' sampler cannot operate on nodes with truncated prior distributions: ', paste0(targetNodes[model$isTruncated(targetNodes)], collapse = ', ')))
+    targetTruncatedBool <- any(sapply(targetDeclInfo_unique, function(x) x$isTruncated()))
+    if(any(targetTruncatedBool)) {
+        targetExpanded <- model$expandNodeNames(targetNodes)
+        stop(paste0(hmcType, ' sampler cannot operate on nodes with truncated prior distributions: ', paste0(targetExpanded[model$isTruncated(targetExpanded)], collapse = ', ')))
+    }
+    ##
+    ##calcNodes <- model$getDependencies(targetNodes, stochOnly = TRUE)
+    depNodes <- model$getDependencies(targetNodes, self = FALSE, stochOnly = TRUE)
+    depDeclIDs_unique <- unique(model$getDeclID(depNodes))
+    depDeclInfo_unique <- model$getModelDef()$declInfo[depDeclIDs_unique]
+    ##
+    ##if(any(model$isTruncated(calcNodes)))
+    ##    stop(paste0(hmcType, ' sampler cannot operate since these dependent nodes have truncated distributions, which do not support AD calculations: ', paste0(calcNodes[model$isTruncated(calcNodes)], collapse = ', ')))
+    depTruncatedBool <- any(sapply(depDeclInfo_unique, function(x) x$isTruncated()))
+    if(any(depTruncatedBool)) {
+        stop(paste0(hmcType, ' sampler cannot operate since these dependent nodes have truncated distributions, which do not support AD calculations: ', paste0(depNodes[model$isTruncated(depNodes)], collapse = ', ')))
+    }
+    ##
+    depDists_unique <- sapply(depDeclInfo_unique, function(x) x$getDistributionName())
+    ##
+    ##if(any(model$getDistribution(calcNodes) == 'dinterval'))
+    ##    stop(paste0(hmcType, ' sampler cannot operate since these dependent nodes have dinterval distributions, which do not support AD calculations: ', paste0(calcNodes[which(model$getDistribution(calcNodes) == 'dinterval')], collapse = ', ')))
+    depIntervalBool <- (depDists_unique == 'dinterval')
+    if(any(depIntervalBool)) {
+        stop(paste0(hmcType, ' sampler cannot operate since these dependent nodes have dinterval distributions, which do not support AD calculations: ', paste0(depNodes[which(model$getDistribution(depNodes) == 'dinterval')], collapse = ', ')))
+    }
+    ##
+    ##if(any(model$getDistribution(calcNodes) == 'dconstraint'))
+    ##    stop(paste0(hmcType, ' sampler cannot operate since these dependent nodes have dconstraint distributions, which do not support AD calculations: ', paste0(calcNodes[which(model$getDistribution(calcNodes) == 'dconstraint')], collapse = ', ')))
+    depConstraintBool <- (depDists_unique == 'dconstraint')
+    if(any(depConstraintBool)) {
+        stop(paste0(hmcType, ' sampler cannot operate since these dependent nodes have dconstraint distributions, which do not support AD calculations: ', paste0(depNodes[which(model$getDistribution(depNodes) == 'dconstraint')], collapse = ', ')))
+    }
+    ##
     ## next, check for:
     ## - target with user-defined distribution (without AD support)
-    dists <- model$getDistribution(targetNodes)
+    ##
+    ##dists <- model$getDistribution(targetNodes)
+    dists <- targetDists_unique
     ADok <- rep(TRUE, length(dists))
     for(i in seq_along(dists)) {
+        ##
+        ## if/when modelDef$checkADsupportForDistribution() is added to core nimble,
+        ## change the entire body of this for-loop to instead be:
+        ## ADoak[i] <- model$getModelDef()$checkADsupportForDistribution(dists[i])
+        ##
         ## these distributions get re-named to a nimble-version, and won't be found:
         if(dists[i] %in% c('dweib', 'dmnorm', 'dmvt', 'dwish', 'dinvwish'))   next
+        ##
+        ##
         ## find the function or this distribution:
         nfObj <- get(dists[i], envir = parent.frame(4))    ## this took a bit of an investigation to make work
         ## is a user-defined distribution: