Bump version for new release, rebuild static docs

The test test_FOMC_ill-defined leads to errors on several
architectures/distributions, as apparent from CRAN checks,
so we need a new release.

Static documentation rebuilt by staticdocs::build_site()

1 files changed, 19 insertions, 19 deletions

diff --git a/vignettes/compiled_models.html b/vignettes/compiled_models.html
index cec76ef9..12289676 100644
--- a/vignettes/compiled_models.html
+++ b/vignettes/compiled_models.html
@@ -250,21 +250,21 @@ mb.1 <- microbenchmark(
 print(mb.1)</code></pre>
 <pre><code>## Unit: seconds
 ##                   expr       min        lq      mean    median        uq
-##  deSolve, not compiled 25.422123 25.889685 26.065978 26.357247 26.387905
-##       Eigenvalue based  2.243667  2.254539  2.277770  2.265412  2.294821
-##      deSolve, compiled  1.849468  1.865343  1.871339  1.881219  1.882274
-##       max neval cld
-##  26.41856     3   b
-##   2.32423     3  a 
-##   1.88333     3  a</code></pre>
+##  deSolve, not compiled 25.120822 25.185794 25.345704 25.250766 25.458146
+##       Eigenvalue based  2.246793  2.255533  2.258865  2.264274  2.264901
+##      deSolve, compiled  1.861661  1.893380  1.930436  1.925098  1.964823
+##        max neval cld
+##  25.665525     3   b
+##   2.265527     3  a 
+##   2.004547     3  a</code></pre>
 <pre class="r"><code>autoplot(mb.1)</code></pre>
-<p><img src="data:image/png;base64,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" title alt width="672" /></p>
-<p>We see that using the compiled model is by a factor of 14 faster than using the R version with the default ode solver, and it is even faster than the Eigenvalue based solution implemented in R which does not need iterative solution of the ODEs:</p>
+<p><img 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" title alt width="672" /></p>
+<p>We see that using the compiled model is by a factor of 13.1 faster than using the R version with the default ode solver, and it is even faster than the Eigenvalue based solution implemented in R which does not need iterative solution of the ODEs:</p>
 <pre class="r"><code>rownames(smb.1) &lt;- smb.1$expr
 smb.1[&quot;median&quot;]/smb.1[&quot;deSolve, compiled&quot;, &quot;median&quot;]</code></pre>
 <pre><code>##                          median
-## deSolve, not compiled 14.010730
-## Eigenvalue based       1.204226
+## deSolve, not compiled 13.116611
+## Eigenvalue based       1.176186
 ## deSolve, compiled      1.000000</code></pre>
 </div>
 <div id="benchmark-for-a-model-that-can-not-be-solved-with-eigenvalues" class="section level2">
@@ -285,20 +285,20 @@ smb.1[&quot;median&quot;]/smb.1[&quot;deSolve, compiled&quot;, &quot;median&quot
 <pre class="r"><code>smb.2 &lt;- summary(mb.2)
 print(mb.2)</code></pre>
 <pre><code>## Unit: seconds
-##                   expr       min       lq      mean    median        uq
-##  deSolve, not compiled 54.386189 54.39423 54.477986 54.402271 54.523884
-##      deSolve, compiled  3.424205  3.53522  3.574587  3.646236  3.649778
+##                   expr       min        lq      mean    median        uq
+##  deSolve, not compiled 54.536624 54.617928 54.690830 54.699231 54.767933
+##      deSolve, compiled  3.690661  3.693247  3.720722  3.695833  3.735753
 ##        max neval cld
-##  54.645498     3   b
-##   3.653319     3  a</code></pre>
+##  54.836635     3   b
+##   3.775673     3  a</code></pre>
 <pre class="r"><code>smb.2[&quot;median&quot;]/smb.2[&quot;deSolve, compiled&quot;, &quot;median&quot;]</code></pre>
 <pre><code>##   median
 ## 1     NA
 ## 2     NA</code></pre>
 <pre class="r"><code>autoplot(mb.2)</code></pre>
-<p><img 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" title alt width="672" /></p>
-<p>Here we get a performance benefit of a factor of 14.9 using the version of the differential equation model compiled from C code!</p>
-<p>This vignette was built with mkin 0.9.43.9000 on</p>
+<p><img 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" title alt width="672" /></p>
+<p>Here we get a performance benefit of a factor of 14.8 using the version of the differential equation model compiled from C code!</p>
+<p>This vignette was built with mkin 0.9.44 on</p>
 <pre><code>## R version 3.3.1 (2016-06-21)
 ## Platform: x86_64-pc-linux-gnu (64-bit)
 ## Running under: Debian GNU/Linux 8 (jessie)</code></pre>