point to the other vignettes regarding nlg and sde models

vignettes/bssm.Rmd

@@ -140,7 +140,7 @@ Here we use helper function `halfnormal` which defines half-Normal prior distrib
 
 For non-Gaussian models, function `bsm_ng` can be used for constructing an BSM model where the observations are assumed to be distributed according to Poisson, binomial, negative binomial, or Gamma distribution. The syntax is nearly identical as in case of `bsm_lg`, but we now define also the distribution via argument `distribution`, and depending on the model, we can also define parameters `u` and `phi`. For Poisson and negative binomial models, the known parameter `u` corresponds to the offset term, whereas in case of binomial model `u` defines the number of trials. For negative binomial model, argument `phi` defines the dispersion term, which can be given as a fixed value, or as a prior function. For same observational densities, a model where the state equation follows a first order autoregressive process can be defined using the function `ng_ar1`. Finally, a stochastic volatility model can be defined using a function `svm`, and an arbitrary linear-Gaussian state model with Poisson, binomial or negative binomial distributed observations can be defined with `ssm_ung` and `ssm_mng` for univariate and multivariate models respectively.
 
-For models where the state equation is no longer linear-Gaussian, we can use our pointer-based C++ interface with the function `ssm_nlg`. Diffusion models can be defined with the function `ssm_sde`.
+For models where the state equation is no longer linear-Gaussian, we can use our pointer-based C++ interface with the function `ssm_nlg`. Diffusion models can be defined with the function `ssm_sde`. For details regarding these types of models, see the corresponding vignettes `growth_model` and `sde_model` respectively.
 
 ## Filtering and smoothing