diff --git a/R/RcppExports.R b/R/RcppExports.R
index c4aa2645..176f97a5 100644
--- a/R/RcppExports.R
+++ b/R/RcppExports.R
@@ -101,10 +101,6 @@ R_milstein <- function(x0, L, t, theta, drift_pntr, diffusion_pntr, ddiffusion_p
     .Call('_bssm_R_milstein', PACKAGE = 'bssm', x0, L, t, theta, drift_pntr, diffusion_pntr, ddiffusion_pntr, positive, seed)
 }
 
-R_milstein_joint <- function(x0, L_c, L_f, t, theta, drift_pntr, diffusion_pntr, ddiffusion_pntr, positive, seed) {
-    .Call('_bssm_R_milstein_joint', PACKAGE = 'bssm', x0, L_c, L_f, t, theta, drift_pntr, diffusion_pntr, ddiffusion_pntr, positive, seed)
-}
-
 suggest_n_nongaussian <- function(model_, theta, candidates, replications, seed, model_type) {
     .Call('_bssm_suggest_n_nongaussian', PACKAGE = 'bssm', model_, theta, candidates, replications, seed, model_type)
 }
diff --git a/man/logLik.nongaussian.Rd b/man/logLik.nongaussian.Rd
new file mode 100644
index 00000000..cae97503
--- /dev/null
+++ b/man/logLik.nongaussian.Rd
@@ -0,0 +1,49 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/loglik.R
+\name{logLik.nongaussian}
+\alias{logLik.nongaussian}
+\title{Log-likelihood of a Non-Gaussian State Space Model}
+\usage{
+\method{logLik}{nongaussian}(
+  object,
+  particles,
+  method = "psi",
+  max_iter = 100,
+  conv_tol = 1e-08,
+  seed = sample(.Machine$integer.max, size = 1),
+  ...
+)
+}
+\arguments{
+\item{object}{Model model.}
+
+\item{particles}{Number of samples for particle filter or importance sampling. If 0, 
+approximate log-likelihood based on the Gaussian approximation is returned.}
+
+\item{method}{Sampling method, default is psi-auxiliary filter (\code{"psi"}), 
+other choices are \code{"bsf"} bootstrap particle filter, and \code{"spdk"}, 
+which uses the importance sampling approach by Shephard and Pitt (1997) and 
+Durbin and Koopman (1997).}
+
+\item{max_iter}{Maximum number of iterations for Gaussian approximation algorithm.}
+
+\item{conv_tol}{Tolerance parameter for the approximation algorithm.}
+
+\item{seed}{Seed for the random number generator.}
+
+\item{...}{Ignored.}
+}
+\description{
+Computes the log-likelihood of a non-Gaussian state space model of \code{bssm} package.
+}
+\examples{
+model <- ssm_ung(y = c(1,4,3), Z = 1, T = 1, R = 0.5, P1 = 2,
+  distribution = "poisson")
+  
+model2 <- bsm_ng(y = c(1,4,3), sd_level = 0.5, P1 = 2,
+  distribution = "poisson")
+logLik(model, particles = 0)
+logLik(model2, particles = 0)
+logLik(model, particles = 10, seed = 1)
+logLik(model2, particles = 10, seed = 1)
+}
diff --git a/man/priors.Rd b/man/priors.Rd
index f981d8ff..b4a42999 100644
--- a/man/priors.Rd
+++ b/man/priors.Rd
@@ -41,5 +41,13 @@ object of class \code{bssm_prior}.
 These simple objects of class \code{bssm_prior} are used to construct a prior distributions for the 
 MCMC runs of \code{bssm} package. Currently supported priors are uniform (\code{uniform()}), 
 half-normal (\code{halfnormal()}), normal (\code{normal()}), gamma (\code{gamma}), and 
-truncated normal distribution  (\code{tnormal()}).
+truncated normal distribution  (\code{tnormal()}).All parameters are vectorized so 
+for regression coefficient vector beta you can define prior for example 
+as \code{normal(0, 0, c(10, 20))}.
+}
+\examples{
+# create uniform prior on [-1, 1] for one parameter with initial value 0.2:
+uniform(0.2, -1, 1)
+# two normal priors at once i.e. for coefficients beta:
+normal(init = c(0.1, 2), mean = 0, sd = c(1, 2))
 }
diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp
index b5ce1906..355b1de9 100644
--- a/src/RcppExports.cpp
+++ b/src/RcppExports.cpp
@@ -633,26 +633,6 @@ BEGIN_RCPP
     return rcpp_result_gen;
 END_RCPP
 }
-// R_milstein_joint
-double R_milstein_joint(const double x0, const unsigned int L_c, const unsigned int L_f, const double t, const arma::vec& theta, SEXP drift_pntr, SEXP diffusion_pntr, SEXP ddiffusion_pntr, bool positive, const unsigned int seed);
-RcppExport SEXP _bssm_R_milstein_joint(SEXP x0SEXP, SEXP L_cSEXP, SEXP L_fSEXP, SEXP tSEXP, SEXP thetaSEXP, SEXP drift_pntrSEXP, SEXP diffusion_pntrSEXP, SEXP ddiffusion_pntrSEXP, SEXP positiveSEXP, SEXP seedSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< const double >::type x0(x0SEXP);
-    Rcpp::traits::input_parameter< const unsigned int >::type L_c(L_cSEXP);
-    Rcpp::traits::input_parameter< const unsigned int >::type L_f(L_fSEXP);
-    Rcpp::traits::input_parameter< const double >::type t(tSEXP);
-    Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP);
-    Rcpp::traits::input_parameter< SEXP >::type drift_pntr(drift_pntrSEXP);
-    Rcpp::traits::input_parameter< SEXP >::type diffusion_pntr(diffusion_pntrSEXP);
-    Rcpp::traits::input_parameter< SEXP >::type ddiffusion_pntr(ddiffusion_pntrSEXP);
-    Rcpp::traits::input_parameter< bool >::type positive(positiveSEXP);
-    Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP);
-    rcpp_result_gen = Rcpp::wrap(R_milstein_joint(x0, L_c, L_f, t, theta, drift_pntr, diffusion_pntr, ddiffusion_pntr, positive, seed));
-    return rcpp_result_gen;
-END_RCPP
-}
 // suggest_n_nongaussian
 arma::vec suggest_n_nongaussian(const Rcpp::List model_, const arma::vec theta, const arma::vec candidates, const unsigned int replications, const unsigned int seed, const int model_type);
 RcppExport SEXP _bssm_suggest_n_nongaussian(SEXP model_SEXP, SEXP thetaSEXP, SEXP candidatesSEXP, SEXP replicationsSEXP, SEXP seedSEXP, SEXP model_typeSEXP) {
@@ -1303,7 +1283,6 @@ static const R_CallMethodDef CallEntries[] = {
     {"_bssm_nonlinear_ekf_mcmc", (DL_FUNC) &_bssm_nonlinear_ekf_mcmc, 27},
     {"_bssm_nonlinear_is_mcmc", (DL_FUNC) &_bssm_nonlinear_is_mcmc, 33},
     {"_bssm_R_milstein", (DL_FUNC) &_bssm_R_milstein, 9},
-    {"_bssm_R_milstein_joint", (DL_FUNC) &_bssm_R_milstein_joint, 10},
     {"_bssm_suggest_n_nongaussian", (DL_FUNC) &_bssm_suggest_n_nongaussian, 6},
     {"_bssm_suggest_n_nonlinear", (DL_FUNC) &_bssm_suggest_n_nonlinear, 20},
     {"_bssm_postcorrection_nongaussian", (DL_FUNC) &_bssm_postcorrection_nongaussian, 10},
diff --git a/vignettes/sde_model.Rmd b/vignettes/sde_model.Rmd
index 3f1dd887..c934d400 100644
--- a/vignettes/sde_model.Rmd
+++ b/vignettes/sde_model.Rmd
@@ -43,7 +43,7 @@ where $B_t$ is a Brownian motion and where $\mu$ and $\sigma$ are real valued fu
 
 ## Example
 
-Discretely observed latent diffusion models can be constructed using the `ssm_sde` function, which takes pointers to `C++` functions defining the drift, diffusion, the derivative of the diffusion function, and the log-densities of the observations and the prior. As an example, let us consider an Ornstein–Uhlenbeck process
+Discretely observed latent diffusion models can be constructed using the `ssm_sde` function, which takes pointers to `C++` functions defining the drift, diffusion, the derivative of the diffusion function, the log-densities of the observations, and the log-prior. As an example, let us consider an Ornstein–Uhlenbeck process
 $$
 \textrm{d} \alpha_t = \rho (\nu - \alpha_t) \textrm{d} t + \sigma \textrm{d} B_t,
 $$