From b576ec74b7a0b7d1dde1114e023fe963369831c2 Mon Sep 17 00:00:00 2001
From: helske <jouni.helske@iki.fi>
Date: Tue, 13 Apr 2021 16:50:03 +0300
Subject: [PATCH] fix SV test and remove parallel example due to Solaris

---
 DESCRIPTION        | 4 ++--
 NEWS               | 1 +
 R/run_mcmc.R       | 3 +--
 man/run_mcmc_ng.Rd | 3 +--
 man/svm.Rd         | 4 +++-
 5 files changed, 8 insertions(+), 7 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index d525a129..cc266d6d 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -2,8 +2,8 @@ Package: bssm
 Type: Package
 Title: Bayesian Inference of Non-Linear and Non-Gaussian State Space
         Models
-Version: 1.1.3-2
-Date: 2021-02-23
+Version: 1.1.4
+Date: 2021-04-13
 Authors@R: 
     c(person(given = "Jouni",
            family = "Helske",
diff --git a/NEWS b/NEWS
index 36204b6c..1f4be274 100644
--- a/NEWS
+++ b/NEWS
@@ -3,6 +3,7 @@ bssm 1.1.4 (Release date: 2021-03-)
    * Better documentation for SV model, and changed ordering of arguments to emphasise the 
      recommended parameterization.
    * Fixed predict method for SV model.
+   * Removed parallelization in one example which failed on Solaris for some unknown reason.
      
 bssm 1.1.3-2 (Release date: 2021-02-24)
 ==============
diff --git a/R/run_mcmc.R b/R/run_mcmc.R
index 0ac63ac0..a72c53dc 100644
--- a/R/run_mcmc.R
+++ b/R/run_mcmc.R
@@ -229,9 +229,8 @@ run_mcmc.gaussian <- function(model, iter, output_type = "full",
 #' 
 #' # run IS-MCMC
 #' # Note small number of iterations for CRAN checks
-#' # so no real use of parallelization
 #' fit <- run_mcmc(model, iter = 5000,
-#'   particles = 10, mcmc_type = "is2", seed = 1, threads = 2)
+#'   particles = 10, mcmc_type = "is2", seed = 1)
 #'
 #' # extract states   
 #' d_states <- as.data.frame(fit, variable = "states", time = 1:n)
diff --git a/man/run_mcmc_ng.Rd b/man/run_mcmc_ng.Rd
index 192a86bd..0a87fd14 100644
--- a/man/run_mcmc_ng.Rd
+++ b/man/run_mcmc_ng.Rd
@@ -136,9 +136,8 @@ model <- bsm_ng(y, xreg = x,
 
 # run IS-MCMC
 # Note small number of iterations for CRAN checks
-# so no real use of parallelization
 fit <- run_mcmc(model, iter = 5000,
-  particles = 10, mcmc_type = "is2", seed = 1, threads = 2)
+  particles = 10, mcmc_type = "is2", seed = 1)
 
 # extract states   
 d_states <- as.data.frame(fit, variable = "states", time = 1:n)
diff --git a/man/svm.Rd b/man/svm.Rd
index 3705e6a2..e589ff67 100644
--- a/man/svm.Rd
+++ b/man/svm.Rd
@@ -17,7 +17,9 @@ svm(y, mu, rho, sd_ar, sigma)
 
 \item{sigma}{Prior for sigma parameter of observation equation, internally denoted as phi. Ignored 
 if \code{mu} is provided. Note that typically parametrization using mu is preferred due to 
-better numerical properties and availability of better Gaussian approximation.}
+better numerical properties and availability of better Gaussian approximation. 
+Most notably the global approximation approach does not work with sigma parameterization as 
+sigma is not a parameter of the resulting approximate model.}
 }
 \value{
 Object of class \code{svm}.