add LA ref back in

vignettes/articles/methods.Rmd

@@ -41,6 +41,7 @@ Fixed and random effects can be used in Bayesian or frequentist models.
 
 The frequentist approach simply identifies the parameter values that maximize the likelihood, i.e., have the greatest probability of having produced the data if they were true.
 It does this by searching parameter space for the combination of parameter values with the Maximum Likelihood.
+Parameter estimates for random effects can be estimated using the Laplace approximation (i.e., with software packages [TMB](https://arxiv.org/pdf/1509.00660.pdf) or [Nimble](https://r-nimble.org/html_manual/cha-AD.html#how-to-use-laplace-approximation)). 
 The CIs are calculated using the standard errors, assuming that the likelihood is normally distributed.
 This approach has the advantage of being fast.