diff --git a/NAMESPACE b/NAMESPACE
index 91f8e64d..47bf1483 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -23,6 +23,7 @@ S3method(terms,sdmTMB)
 S3method(tidy,sdmTMB)
 S3method(vcov,sdmTMB)
 export(Beta)
+export(CAIC.sdmTMB)
 export(add_barrier_mesh)
 export(add_utm_columns)
 export(censored_poisson)
diff --git a/R/caic.R b/R/caic.R
new file mode 100644
index 00000000..2c37c023
--- /dev/null
+++ b/R/caic.R
@@ -0,0 +1,110 @@
+
+#' @title Calculate conditional AIC
+#'
+#' @description
+#' Calculates the conditional Akaike Information criterion (cAIC).
+#'
+#' @param object Output from \code{\link{sdmTMB}}
+#' @param what Whether to return the cAIC or the effective degrees of freedom
+#'        (EDF) for each group of random effects.
+#'
+#' @details
+#' cAIC is designed to optimize the expected out-of-sample predictive
+#' performance for new data that share the same random effects as the
+#' in-sample (fitted) data, e.g., spatial interpolation.  In this sense,
+#' it should be a fast approximation to optimizing the model structure
+#' based on k-fold crossvalidation.
+#' By contrast, \code{AIC} calculates the
+#' marginal Akaike Information Criterion, which is designed to optimize
+#' expected predictive performance for new data that have new random effects,
+#' e.g., extrapolation, or inference about generative parameters.
+#'
+#' cAIC also calculates as a byproduct the effective degrees of freedom,
+#' i.e., the number of fixed effects that would have an equivalent impact on
+#' model flexibility as a given random effect.
+#'
+#' Both cAIC and EDF are calculated using Eq. 6 of Zheng Cadigan Thorson 2024.
+#'
+#' Note that, for models that include profiled fixed effects, these profiles
+#' are turned off.
+#'
+#' @return
+#' Either the cAIC, or the effective degrees of freedom (EDF) by group
+#' of random effects
+#'
+#' @references
+#'
+#' **Deriving the general approximation to cAIC used here**
+#'
+#' Zheng, N., Cadigan, N., & Thorson, J. T. (2024).
+#' A note on numerical evaluation of conditional Akaike information for
+#' nonlinear mixed-effects models (arXiv:2411.14185). arXiv.
+#' \doi{10.48550/arXiv.2411.14185}
+#'
+#' **The utility of EDF to diagnose hierarchical model behavior**
+#'
+#' Thorson, J. T. (2024). Measuring complexity for hierarchical
+#' models using effective degrees of freedom. Ecology,
+#' 105(7), e4327 \doi{10.1002/ecy.4327}
+#'
+#' @export
+CAIC.sdmTMB <-
+function( object,
+          what = c("CAIC","EDF") ){
+
+  what = match.arg(what)
+  require(Matrix)
+  tmb_data = object$tmb_data
+
+  # Make sure profile = NULL
+  if( is.null(object$control$profile) ){
+    obj = object$tmb_obj
+  }else{
+    obj = TMB::MakeADFun( data = tmb_data,
+                         parameters = object$parlist,
+                         map = object$tmb_map,
+                         random = object$tmb_random,
+                         DLL = "sdmTMB",
+                         profile = NULL )
+  }
+
+  # Make obj_new
+  tmb_data$weights_i[] = 0
+  obj_new = TMB::MakeADFun( data = tmb_data,
+                      parameters = object$parlist,
+                      map = object$tmb_map,
+                      random = object$tmb_random,
+                      DLL = "sdmTMB",
+                      profile = NULL )
+
+  #
+  par = obj$env$parList()
+  parDataMode <- obj$env$last.par
+  indx = obj$env$lrandom()
+  q = length(indx)
+  p = length(object$model$par)
+
+  ## use - for Hess because model returns negative loglikelihood;
+  #cov_Psi_inv = -Hess_new[indx,indx]; ## this is the marginal prec mat of REs;
+  Hess_new = -Matrix(obj_new$env$f(parDataMode,order=1,type="ADGrad"),sparse = TRUE)
+  Hess_new = Hess_new[indx,indx]
+
+  ## Joint hessian etc
+  Hess = -Matrix(obj$env$f(parDataMode,order=1,type="ADGrad"),sparse = TRUE)
+  Hess = Hess[indx,indx]
+  negEDF = diag(solve(Hess, Hess_new))
+
+  if(what=="CAIC"){
+    jnll = obj$env$f(parDataMode)
+    cnll = jnll - obj_new$env$f(parDataMode)
+    cAIC = 2*cnll + 2*(p+q) - 2*sum(negEDF)
+    return(cAIC)
+  }
+  if(what=="EDF"){
+    # Figure out group for each random-effect coefficient
+    group = factor(names(object$last.par.best[obj$env$random]))
+    # Calculate total EDF by group
+    EDF = tapply(negEDF,INDEX=group,FUN=length) - tapply(negEDF,INDEX=group,FUN=sum)
+    return(EDF)
+  }
+}
diff --git a/man/CAIC.sdmTMB.Rd b/man/CAIC.sdmTMB.Rd
new file mode 100644
index 00000000..a74dcbda
--- /dev/null
+++ b/man/CAIC.sdmTMB.Rd
@@ -0,0 +1,55 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/caic.R
+\name{CAIC.sdmTMB}
+\alias{CAIC.sdmTMB}
+\title{Calculate conditional AIC}
+\usage{
+CAIC.sdmTMB(object, what = c("CAIC", "EDF"))
+}
+\arguments{
+\item{object}{Output from \code{\link{sdmTMB}}}
+
+\item{what}{Whether to return the cAIC or the effective degrees of freedom
+(EDF) for each group of random effects.}
+}
+\value{
+Either the cAIC, or the effective degrees of freedom (EDF) by group
+of random effects
+}
+\description{
+Calculates the conditional Akaike Information criterion (cAIC).
+}
+\details{
+cAIC is designed to optimize the expected out-of-sample predictive
+performance for new data that share the same random effects as the
+in-sample (fitted) data, e.g., spatial interpolation.  In this sense,
+it should be a fast approximation to optimizing the model structure
+based on k-fold crossvalidation.
+By contrast, \code{AIC} calculates the
+marginal Akaike Information Criterion, which is designed to optimize
+expected predictive performance for new data that have new random effects,
+e.g., extrapolation, or inference about generative parameters.
+
+cAIC also calculates as a byproduct the effective degrees of freedom,
+i.e., the number of fixed effects that would have an equivalent impact on
+model flexibility as a given random effect.
+
+Both cAIC and EDF are calculated using Eq. 6 of Zheng Cadigan Thorson 2024.
+
+Note that, for models that include profiled fixed effects, these profiles
+are turned off.
+}
+\references{
+\strong{Deriving the general approximation to cAIC used here}
+
+Zheng, N., Cadigan, N., & Thorson, J. T. (2024).
+A note on numerical evaluation of conditional Akaike information for
+nonlinear mixed-effects models (arXiv:2411.14185). arXiv.
+\doi{10.48550/arXiv.2411.14185}
+
+\strong{The utility of EDF to diagnose hierarchical model behavior}
+
+Thorson, J. T. (2024). Measuring complexity for hierarchical
+models using effective degrees of freedom. Ecology,
+105(7), e4327 \doi{10.1002/ecy.4327}
+}
diff --git a/scratch/caic-demo.R b/scratch/caic-demo.R
new file mode 100644
index 00000000..13939685
--- /dev/null
+++ b/scratch/caic-demo.R
@@ -0,0 +1,16 @@
+
+library(sdmTMB)
+
+# Build a mesh to implement the SPDE approach:
+mesh <- make_mesh(pcod_2011, c("X", "Y"), cutoff = 20)
+
+# Fit a Tweedie spatial random field GLMM with a smoother for depth:
+fit <- sdmTMB(
+  density ~ s(depth),
+  data = pcod_2011, mesh = mesh,
+  family = tweedie(link = "log"),
+  control = sdmTMBcontrol(profile="b_j")
+)
+
+CAIC.sdmTMB(fit, what="CAIC")
+CAIC.sdmTMB(fit, what="EDF")