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Merged
paciorek merged 3 commits into from
Sep 15, 2026
Merged

add omit arg for getParents, getDependencies, getConditionallyIndependentSets #55

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624c697
Add omit option for getDeps/getParents.
paciorek Sep 14, 2026
9bf4033
Fix minor issue with omit. Add some tests.
paciorek Sep 14, 2026
0e4ccf4
Add omit option for getCIsets.
paciorek Sep 15, 2026
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13 changes: 7 additions & 6 deletions nimbleModel/R/modelBaseClass.R
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Original file line number Diff line number Diff line change
Expand Up @@ -370,29 +370,30 @@ modelBase_nClass <- nClass(
},
# `self` arg masks the reference to the object.
# TODO: perhaps we should rename the arg `includeSelf`, but that is not back compatible.
getDependencies = function(nodes, self = TRUE, determOnly = FALSE, stochOnly = FALSE,
getDependencies = function(nodes, omit = NULL, self = TRUE,
determOnly = FALSE, stochOnly = FALSE,
includeData = TRUE, dataOnly = FALSE,
includePredictive = nimble::getNimbleOption('getDependenciesIncludesPredictiveNodes'),
predictiveOnly = FALSE, includeRHSonly = FALSE,
downstream = FALSE, immediateOnly = FALSE,
nodesAsChars = getNimbleModelOption("nodesAsChars"),
returnScalarComponents = FALSE, .sort = FALSE) {
nimbleModel::getDependencies(
thisModel, nodes, self,
thisModel, nodes, omit, self,
determOnly, stochOnly, includeData, dataOnly,
includePredictive, predictiveOnly, includeRHSonly,
downstream, immediateOnly,
nodesAsChars, returnScalarComponents, .sort
)
},
getParents = function(nodes, self = FALSE,
getParents = function(nodes, omit = NULL, self = FALSE,
determOnly = FALSE, stochOnly = FALSE,
includeData = TRUE, dataOnly = FALSE, includeRHSonly = FALSE,
upstream = FALSE, immediateOnly = FALSE,
nodesAsChars = getNimbleModelOption("nodesAsChars"),
returnScalarComponents = FALSE, .sort = FALSE) {
nimbleModel::getParents(
thisModel, nodes, self,
thisModel, nodes, omit, self,
determOnly, stochOnly, includeData, dataOnly, includeRHSonly,
upstream, immediateOnly,
nodesAsChars, returnScalarComponents, .sort
Expand Down Expand Up @@ -446,13 +447,13 @@ modelBase_nClass <- nClass(
topologicallySortNodes = function(nodes) {
nimbleModel::expandNodeNames(self, nodes, sort = TRUE, unique = TRUE)
},
getConditionallyIndependentSets = function(nodes, givenNodes,
getConditionallyIndependentSets = function(nodes, givenNodes, omit = NULL,
explore = c("both", "down", "up"),
unknownAsGiven = TRUE, returnScalarComponents = FALSE,
endAsGiven = FALSE,
nodesAsChars = getNimbleModelOption("nodesAsChars")) {
nimbleModel::getConditionallyIndependentSets(
self, nodes, givenNodes, explore, unknownAsGiven,
self, nodes, givenNodes, omit, explore, unknownAsGiven,
returnScalarComponents, endAsGiven, nodesAsChars
)
},
Expand Down
27 changes: 20 additions & 7 deletions nimbleModel/R/modelFunctions.R
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Expand Up @@ -216,7 +216,7 @@ taggedClass <- R6Class(
)
)

getDependencies <- function(model, nodes,
getDependencies <- function(model, nodes, omit = NULL,
self = TRUE, determOnly = FALSE, stochOnly = FALSE,
includeData = TRUE, dataOnly = FALSE,
includePredictive = nimble::getNimbleOption('getDependenciesIncludesPredictiveNodes'),
Expand All @@ -225,7 +225,7 @@ getDependencies <- function(model, nodes,
nodesAsChars = getNimbleModelOption("nodesAsChars"),
returnScalarComponents = FALSE, .sort = FALSE) {
traverseGraph(model$modelDef$downstreamRules, model$modelDef$declRules,
nodes = nodes,
nodes = nodes, omit = omit,
down = TRUE, self = self,
determOnly = determOnly, stochOnly = stochOnly,
includeData = includeData, dataOnly = dataOnly, includePredictive = includePredictive,
Expand All @@ -236,14 +236,14 @@ getDependencies <- function(model, nodes,
)
}

getParents <- function(model, nodes,
getParents <- function(model, nodes, omit = NULL,
self = FALSE, determOnly = FALSE, stochOnly = FALSE,
includeData = TRUE, dataOnly = FALSE, includeRHSonly = FALSE,
upstream = FALSE, immediateOnly = FALSE,
nodesAsChars = getNimbleModelOption("nodesAsChars"),
returnScalarComponents = FALSE, .sort = FALSE) {
traverseGraph(model$modelDef$upstreamRules, model$modelDef$declRules,
nodes = nodes,
nodes = nodes, omit = omit,
down = FALSE, self = self,
determOnly = determOnly, stochOnly = stochOnly,
includeData = includeData, dataOnly = dataOnly, includePredictive = TRUE,
Expand Down Expand Up @@ -356,7 +356,7 @@ setdiff_nodes <- function(nodeSet1, nodeSet2) {
}

#' @export
getConditionallyIndependentSets <- function(model, nodes, givenNodes,
getConditionallyIndependentSets <- function(model, nodes, givenNodes, omit = NULL,
explore = c("both", "down", "up"),
unknownAsGiven = TRUE, returnScalarComponents = FALSE,
endAsGiven = FALSE,
Expand Down Expand Up @@ -428,10 +428,19 @@ getConditionallyIndependentSets <- function(model, nodes, givenNodes,
if (unknownAsGiven) {
givenNodes <- c(givenNodes, unknownNodes)
} # else nodes <- c(nodes, unknownNodes)

stochDecl <- sapply(model$modelDef$declInfo, \(declInfo) declInfo$stoch)
touched <- lapply(model$modelDef$declInfo[stochDecl], \(declInfo) taggedClass$new(declInfo$declRule))
names(touched) <- sapply(model$modelDef$declInfo[stochDecl], \(declInfo) declInfo$declRule$ID)

if(!is.null(omit)) {
omit <- model$getNodes(omit, nodesAsChars = FALSE)
givenNodes <- setdiff_nodes(givenNodes, omit)
nodes <- setdiff_nodes(nodes, omit)
tmp <- sapply(omit, \(node) touched[[node$decl$declRule$ID]]$tag(node$getIDs()))
}


given <- lapply(model$modelDef$declInfo[stochDecl], \(declInfo) taggedClass$new(declInfo$declRule))
names(given) <- names(touched)
tmp <- sapply(givenNodes, \(node) given[[node$decl$declRule$ID]]$tag(node$getIDs()))
Expand Down Expand Up @@ -932,7 +941,11 @@ setupMargNodes <- function(model, paramNodes, randomEffectsNodes, calcNodes,
nodes = randomEffectsNodes, givenNodes = givenNodes,
unknownAsGiven = TRUE
)
} else if (is.numeric(split)) { # TODO: check this makes sense with inputs being nodeRanges.
} else if (is.numeric(split)) {
# TODO: reconsider this - it relies on user knowing what the `randomEffectsNodes` will be
# even if the user provides them as character strings instead of a list of nodeRanges.
if(length(randomEffectsNodes) != length(split))
stop("when providing numeric vector for `split`, its length must match that of the `randomEffectsNodes`. This will usually mean that `randomEffectsNodes` should be provided by the user as a list of nodeRanges.")
reSets <- split(randomEffectsNodes, split)
} else {
stop("setupMargNodes: Invalid value for `split`")
Expand Down
20 changes: 14 additions & 6 deletions nimbleModel/R/processModelGraph.R
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Original file line number Diff line number Diff line change
Expand Up @@ -237,7 +237,7 @@ setSortIDs <- function(calcRules) {
# (`follow = TRUE`) or to stop at immediate parent or child
# (`immediateOnly = TRUE`).
traverseGraph <- function(streamRules, declRules,
nodes, down, self = TRUE,
nodes, omit = NULL, down, self = TRUE,
determOnly = FALSE, stochOnly = FALSE,
includeData = TRUE, dataOnly = FALSE,
includePredictive = TRUE, predictiveOnly = FALSE, includeRHSonly = FALSE,
Expand All @@ -253,7 +253,10 @@ traverseGraph <- function(streamRules, declRules,

if (inherits(nodes, "varRangeClass")) nodes <- list(nodes) # We use `lapply` on 'nodes' later.

results <- traverseGraphRecurse(streamRules, nodes, down, follow, immediateOnly)
if(!is.null(omit))
omit <- model$getNodes(omit, nodesAsChars = FALSE)

results <- traverseGraphRecurse(streamRules, nodes, omit, down, follow, immediateOnly, model)

results <- model$getNodes(results, determOnly = determOnly, stochOnly = stochOnly,
includeData = includeData, dataOnly = dataOnly,
Expand Down Expand Up @@ -315,8 +318,13 @@ traverseGraph <- function(streamRules, declRules,
return(results)
}

traverseGraphRecurse <- function(rules, nodes, down, follow = FALSE, immediateOnly = FALSE, firstPass = TRUE) {
traverseGraphRecurse <- function(rules, nodes, omit = NULL, down, follow = FALSE, immediateOnly = FALSE, model = NULL, firstPass = TRUE) {
results <- flatten(lapply(nodes, function(node) applyRules(rules, node)))
if(!is.null(omit)) {
results <- model$getNodes(results, nodesAsChars = FALSE)
results <- setdiff_nodes(results, omit)
## We presumably don't need to convert back to varRange(s) since the nodeRange has all that information.
}
if (immediateOnly) {
return(results)
}
Expand All @@ -326,18 +334,18 @@ traverseGraphRecurse <- function(rules, nodes, down, follow = FALSE, immediateOn
if (!down && !firstPass && !follow) {
# For upward traversal, check current rule to see if continue upwards, but always go up on first pass.
# (Because we need to determine stochasticity of the next rule up, not stochasticity of starting rule.
stoch <- sapply(results, function(varRange) varRange$fromStochRule)
stoch <- sapply(results, function(x) if(inherits(x, 'nodeRangeClass')) x$decl$stoch else x$fromStochRule)
results <- results[!stoch] # Stop here if upwards involves stochastic rule, excluding the upwards result.
}
propagators <- results
if (!follow && down) {
# For downward traversal, stop propagating at stochastic cases, but results included.
stoch <- sapply(propagators, function(varRange) varRange$fromStochRule)
stoch <- sapply(propagators, function(x) if(inherits(x, 'nodeRangeClass')) x$decl$stoch else x$fromStochRule)
propagators <- propagators[!stoch]
}
# Continue traversing.
if (length(propagators)) {
results <- c(results, traverseGraphRecurse(rules, propagators, down, follow, firstPass = FALSE))
results <- c(results, traverseGraphRecurse(rules, propagators, omit, down, follow, immediateOnly, model, firstPass = FALSE))
} else {
return(results)
}
Expand Down
53 changes: 53 additions & 0 deletions nimbleModel/tests/testthat/test-modelGraph.R
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Expand Up @@ -2605,3 +2605,56 @@ test_that("handling RHSonly with getParents", {
c("mu[2]","y[2]"))

})

test_that("using omit with getDependencies and getParents", {
setNimbleModelOption('nodesAsChars', TRUE)

code <- nimbleCode({
y ~ dnorm(mu,1)
mu <- mu0+1
mu0 ~ dnorm(0,1)
})
m <- nimbleModel(code)
expect_identical(m$getDependencies('mu0',omit='mu'), "mu0")
expect_identical(m$getParents('y',omit='mu',self=TRUE), "y")

code <- nimbleCode({
z ~ dnorm(y,1)
y ~ dnorm(mu,1)
mu <- mu0+1
mu0~dnorm(0,1)
})
m <- nimbleModel(code)
expect_identical(m$getDependencies('mu0',omit='y'), c("mu0", "mu"))
expect_identical(m$getParents('y',omit='mu0'), "mu")


code <- nimbleCode({
w ~ dnorm(y,1)
z ~ dnorm(y+mu,1)
y ~ dnorm(mu+b,1)
mu <- mu0+1
b ~ dnorm(0,1)
mu0~dnorm(0,1)
})
m <- nimbleModel(code)
expect_identical(m$getDependencies('mu0',omit='y',downstream=TRUE),
c("mu0","mu","lifted_y_plus_mu","lifted_mu_plus_b","z"))
expect_identical(m$getParents('z',omit='y',upstream=TRUE),
c("lifted_y_plus_mu","mu","mu0"))

setNimbleModelOption('nodesAsChars', FALSE)
})

test_that("getDependencies handles non-node-based query", {
code <- nimbleCode({
for(i in 1:4)
y[i] ~ dnorm(mu[i],1)
mu[1:4]~dmnorm(z[1:4],pr[1:4,1:4])
})
m=nimbleModel(code)
expect_identical(m$getDependencies('mu[2]', self = FALSE, nodesAsChars =TRUE),
"y[2]") # Not all of y is included.
expect_identical(m$getDependencies('mu[2]', nodesAsChars =TRUE),
c("mu[1:4]", "y[2]")) # All of mu is included.
})
9 changes: 4 additions & 5 deletions nimbleModel/tests/testthat/test-setupMargNodes.R
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Original file line number Diff line number Diff line change
Expand Up @@ -18,8 +18,7 @@ test_that("getConditionallyIndependentSets works in model with a couple of sets"
expect_identical(getConditionallyIndependentSets(m, 'y[2]', explore = "down"), list('y[2]'))
expect_identical(getConditionallyIndependentSets(m, 'x[1:2]', explore = "up"), list(c('x[1]'), c('x[2]')))
expect_true(nimble:::testConditionallyIndependentSets(m, getConditionallyIndependentSets(m)))
# expect_identical(getConditionallyIndependentSets(m, omit = 'y[2]'), list(c('x[1]', 'y[1]'), c('x[2]')))
# expect_identical(getConditionallyIndependentSets(m, omit = 5), list(c('x[1]', 'y[1]'), c('x[2]')))
expect_identical(getConditionallyIndependentSets(m, omit = 'y[2]'), list(c('x[1]', 'y[1]'), c('x[2]')))
expect_identical(getConditionallyIndependentSets(m, 'x[1]'), list(c('x[1]')))
expect_identical(getConditionallyIndependentSets(m, 'x[1]', unknownAsGiven=FALSE), list(c('x[1]', 'y[1]')))

Expand Down Expand Up @@ -240,7 +239,7 @@ test_that("getConditionallyIndependentSets works in model with diamond shape", {
})

test_that("getConditionallyIndependentSets works in double-state state-space model", {
# two stae-space chains of latent states with one data set that depends on both
# two state-space chains of latent states with one data set that depends on both
mc <- nimbleCode({
x[1] ~ dnorm(0, 1)
w[1] ~ dnorm(0, 1)
Expand All @@ -254,8 +253,8 @@ test_that("getConditionallyIndependentSets works in double-state state-space mod

expect_identical(getConditionallyIndependentSets(m),
list(c(paste0("x[", 2:4, "]"), paste0("w[", 2:4, "]"))))
# expect_identical(getConditionallyIndependentSets(m, omit = "w[2]"),
# list(c("x[2]", "x[3]", "w[3]", "x[4]", "w[4]")))
expect_identical(getConditionallyIndependentSets(m, omit = "w[2]"),
list(c("x[2]", "x[3]", "x[4]", "w[3]", "w[4]")))
expect_identical(getConditionallyIndependentSets(m, givenNodes = c("y", "w[3]"), unknownAsGiven=FALSE),
list(c("x[2]", "x[3]", "x[4]", "x[1]", "w[2]", "w[4]", "w[1]")))
expect_identical(getConditionallyIndependentSets(m, givenNodes = c("y", "x[3]", "w[3]"), unknownAsGiven=FALSE),
Expand Down