diff --git a/DESCRIPTION b/DESCRIPTION
index e3db94ad..d4b55d44 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,8 +1,8 @@
 Package: bssm
 Type: Package
 Title: Bayesian Inference of State Space Models
-Version: 0.0.74
-Date: 2017-02-24
+Version: 0.0.75
+Date: 2017-05-10
 Author: Jouni Helske, Matti Vihola
 Maintainer: Jouni Helske <jouni.helske@jyu.fi>
 Description: Efficient methods for Bayesian inference of state space models. 
diff --git a/R/particle_smoother.R b/R/particle_smoother.R
index 856ed031..077e5af1 100644
--- a/R/particle_smoother.R
+++ b/R/particle_smoother.R
@@ -5,7 +5,7 @@
 #'
 #' @param object Model.
 #' @param nsim Number of samples.
-#' @param filter Choice of particle filter algorithm. For Gaussian models, 
+#' @param filter_type Choice of particle filter algorithm. For Gaussian models, 
 #' possible choices are \code{"bsf"} (bootstrap particle filter) and 
 #' \code{"apf"} (auxiliary particle filter). In addition, for non-Gaussian or 
 #' non-linear models, \code{"psi"} uses psi-particle filter, and 
@@ -160,7 +160,7 @@ particle_smoother.svm <- function(object, nsim,
   out$alpha <- aperm(out$alpha, c(2, 1, 3))
   out
 }
-
+#' @rdname particle_smoother
 #' @method particle_smoother nlg_ssm
 #' @export
 particle_smoother.nlg_ssm <- function(object, nsim, 
diff --git a/R/run_mcmc.R b/R/run_mcmc.R
index c2972c6f..dbb78435 100644
--- a/R/run_mcmc.R
+++ b/R/run_mcmc.R
@@ -216,7 +216,7 @@ run_mcmc.ngssm <- function(object, n_iter, nsim_states, type = "full",
   delayed_acceptance = TRUE, n_burnin = floor(n_iter/2),
   n_thin = 1, gamma = 2/3, target_acceptance = 0.234, S, end_adaptive_phase = TRUE,
   local_approx  = TRUE, n_threads = 1,
-  seed = sample(.Machine$integer.max, size = 1), ...) {
+  seed = sample(.Machine$integer.max, size = 1), max_iter = 100, conv_tol = 1e-8, ...) {
   
   a <- proc.time()
   check_target(target_acceptance)
@@ -474,7 +474,7 @@ run_mcmc.nlg_ssm <-  function(object, n_iter, nsim_states, type = "full",
   delayed_acceptance = TRUE, n_burnin = floor(n_iter/2), n_thin = 1,
   gamma = 2/3, target_acceptance = 0.234, S, end_adaptive_phase = TRUE,
   n_threads = 1, seed = sample(.Machine$integer.max, size = 1), max_iter = 100, 
-  conv_tol = 1e-8, iekf_iter = 0, ...) {
+  conv_tol = 1e-4, iekf_iter = 0, ...) {
   
   a <- proc.time()
   check_target(target_acceptance)
diff --git a/man/particle_smoother.Rd b/man/particle_smoother.Rd
index dd3399d7..99525805 100644
--- a/man/particle_smoother.Rd
+++ b/man/particle_smoother.Rd
@@ -4,6 +4,7 @@
 \alias{particle_smoother}
 \alias{particle_smoother.gssm}
 \alias{particle_smoother.ngssm}
+\alias{particle_smoother.nlg_ssm}
 \title{Particle Smoothing}
 \usage{
 particle_smoother(object, nsim, ...)
@@ -15,6 +16,10 @@ particle_smoother(object, nsim, ...)
 \method{particle_smoother}{ngssm}(object, nsim, filter_type = "bsf",
   smoothing_method = "fs", seed = sample(.Machine$integer.max, size = 1),
   max_iter = 100, conv_tol = 1e-08, ...)
+
+\method{particle_smoother}{nlg_ssm}(object, nsim, filter_type = "psi",
+  smoothing_method = "fs", seed = sample(.Machine$integer.max, size = 1),
+  max_iter = 100, conv_tol = 1e-08, iekf_iter = 0, ...)
 }
 \arguments{
 \item{object}{Model.}
@@ -23,6 +28,13 @@ particle_smoother(object, nsim, ...)
 
 \item{...}{Ignored.}
 
+\item{filter_type}{Choice of particle filter algorithm. For Gaussian models, 
+possible choices are \code{"bsf"} (bootstrap particle filter) and 
+\code{"apf"} (auxiliary particle filter). In addition, for non-Gaussian or 
+non-linear models, \code{"psi"} uses psi-particle filter, and 
+for non-linear models options \code{"ekf"} (extended Kalman particle filter) 
+is also available.}
+
 \item{smoothing_method}{Either \code{"fs"} (filter-smoother), or \code{"fbs"} 
 (forward-backward smoother).}
 
@@ -35,13 +47,6 @@ Default is \code{TRUE}.}
 
 \item{conv_tol}{Tolerance parameter used in Gaussian approximation. Used psi-PF.}
 
-\item{filter}{Choice of particle filter algorithm. For Gaussian models, 
-possible choices are \code{"bsf"} (bootstrap particle filter) and 
-\code{"apf"} (auxiliary particle filter). In addition, for non-Gaussian or 
-non-linear models, \code{"psi"} uses psi-particle filter, and 
-for non-linear models options \code{"ekf"} (extended Kalman particle filter) 
-is also available.}
-
 \item{iekf_iter}{If zero (default), first approximation for non-linear 
 Gaussian models is obtained from extended Kalman filter. If 
 \code{iekf_iter > 0}, iterated extended Kalman filter is used with 
diff --git a/man/run_mcmc_ng.Rd b/man/run_mcmc_ng.Rd
index 590b4a3b..ce5e421d 100644
--- a/man/run_mcmc_ng.Rd
+++ b/man/run_mcmc_ng.Rd
@@ -12,7 +12,7 @@
   delayed_acceptance = TRUE, n_burnin = floor(n_iter/2), n_thin = 1,
   gamma = 2/3, target_acceptance = 0.234, S, end_adaptive_phase = TRUE,
   local_approx = TRUE, n_threads = 1, seed = sample(.Machine$integer.max,
-  size = 1), ...)
+  size = 1), max_iter = 100, conv_tol = 1e-08, ...)
 
 \method{run_mcmc}{ng_bsm}(object, n_iter, nsim_states, type = "full",
   method = "pm", simulation_method = "psi", const_m = TRUE,
@@ -33,7 +33,7 @@
   delayed_acceptance = TRUE, n_burnin = floor(n_iter/2), n_thin = 1,
   gamma = 2/3, target_acceptance = 0.234, S, end_adaptive_phase = TRUE,
   n_threads = 1, seed = sample(.Machine$integer.max, size = 1),
-  max_iter = 100, conv_tol = 1e-08, iekf_iter = 0, ...)
+  max_iter = 100, conv_tol = 1e-04, iekf_iter = 0, ...)
 }
 \arguments{
 \item{object}{Model object.}
@@ -84,12 +84,12 @@ once at the start of the MCMC. Not used for non-linear models.}
 
 \item{seed}{Seed for the random number generator.}
 
-\item{...}{Ignored.}
-
 \item{max_iter}{Maximum number of iterations used in Gaussian approximation. Used psi-PF.}
 
 \item{conv_tol}{Tolerance parameter used in Gaussian approximation. Used psi-PF.}
 
+\item{...}{Ignored.}
+
 \item{iekf_iter}{If zero (default), first approximation for non-linear 
 Gaussian models is obtained from extended Kalman filter. If 
 \code{iekf_iter > 0}, iterated extended Kalman filter is used with