From db9ae6a0c9cecb92048fde6f06af1da183c09b5f Mon Sep 17 00:00:00 2001
From: Johannes Ranke <jranke@uni-bremen.de>
Date: Thu, 19 Nov 2020 15:41:24 +0100
Subject: Depend on parallel, doc improvements

By depending on parallel instead of importing it, functions to set up
and stop a cluster are always available when mkin is loaded.

The use of multicore processing in mmkin on Windows is now documented in
the help file, which brings mkin closer to a version 1.0 #9.
---
 vignettes/FOCUS_L.html | 143 +++++++++++++++++++++++++++++++------------------
 1 file changed, 91 insertions(+), 52 deletions(-)

(limited to 'vignettes/FOCUS_L.html')

diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html
index 56965324..1c03f484 100644
--- a/vignettes/FOCUS_L.html
+++ b/vignettes/FOCUS_L.html
@@ -11,7 +11,7 @@
 
 <meta name="author" content="Johannes Ranke" />
 
-<meta name="date" content="2020-10-14" />
+<meta name="date" content="2020-11-19" />
 
 <title>Example evaluation of FOCUS Laboratory Data L1 to L3</title>
 
@@ -1283,6 +1283,45 @@ color: #d14;
 }
 </style>
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@@ -1291,7 +1330,7 @@ color: #d14;
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   div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
   ul.task-list{list-style: none;}
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+    </style>
 
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@@ -1527,7 +1566,7 @@ div.tocify {
 
 <h1 class="title toc-ignore">Example evaluation of FOCUS Laboratory Data L1 to L3</h1>
 <h4 class="author">Johannes Ranke</h4>
-<h4 class="date">2020-10-14</h4>
+<h4 class="date">2020-11-19</h4>
 
 </div>
 
@@ -1546,10 +1585,10 @@ FOCUS_2006_L1_mkin &lt;- mkin_wide_to_long(FOCUS_2006_L1)</code></pre>
 <p>Since mkin version 0.9-32 (July 2014), we can use shorthand notation like <code>&quot;SFO&quot;</code> for parent only degradation models. The following two lines fit the model and produce the summary report of the model fit. This covers the numerical analysis given in the FOCUS report.</p>
 <pre class="r"><code>m.L1.SFO &lt;- mkinfit(&quot;SFO&quot;, FOCUS_2006_L1_mkin, quiet = TRUE)
 summary(m.L1.SFO)</code></pre>
-<pre><code>## mkin version used for fitting:    0.9.50.3 
+<pre><code>## mkin version used for fitting:    0.9.50.4 
 ## R version used for fitting:       4.0.3 
-## Date of fit:     Wed Oct 14 16:00:04 2020 
-## Date of summary: Wed Oct 14 16:00:04 2020 
+## Date of fit:     Thu Nov 19 14:46:14 2020 
+## Date of summary: Thu Nov 19 14:46:14 2020 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent * parent
@@ -1647,17 +1686,17 @@ summary(m.L1.SFO)</code></pre>
 <pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre>
 <pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is
 ## doubtful</code></pre>
-<pre><code>## mkin version used for fitting:    0.9.50.3 
+<pre><code>## mkin version used for fitting:    0.9.50.4 
 ## R version used for fitting:       4.0.3 
-## Date of fit:     Wed Oct 14 16:00:04 2020 
-## Date of summary: Wed Oct 14 16:00:05 2020 
+## Date of fit:     Thu Nov 19 14:46:15 2020 
+## Date of summary: Thu Nov 19 14:46:15 2020 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 380 model solutions performed in 0.084 s
+## Fitted using 380 model solutions performed in 0.085 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -1752,10 +1791,10 @@ plot(m.L2.FOMC, show_residuals = TRUE,
      main = &quot;FOCUS L2 - FOMC&quot;)</code></pre>
 <p><img 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" /><!-- --></p>
 <pre class="r"><code>summary(m.L2.FOMC, data = FALSE)</code></pre>
-<pre><code>## mkin version used for fitting:    0.9.50.3 
+<pre><code>## mkin version used for fitting:    0.9.50.4 
 ## R version used for fitting:       4.0.3 
-## Date of fit:     Wed Oct 14 16:00:05 2020 
-## Date of summary: Wed Oct 14 16:00:05 2020 
+## Date of fit:     Thu Nov 19 14:46:15 2020 
+## Date of summary: Thu Nov 19 14:46:15 2020 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
@@ -1828,12 +1867,12 @@ plot(m.L2.FOMC, show_residuals = TRUE,
 <pre class="r"><code>m.L2.DFOP &lt;- mkinfit(&quot;DFOP&quot;, FOCUS_2006_L2_mkin, quiet = TRUE)
 plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
      main = &quot;FOCUS L2 - DFOP&quot;)</code></pre>
-<p><img 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" 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" /><!-- --></p>
 <pre class="r"><code>summary(m.L2.DFOP, data = FALSE)</code></pre>
-<pre><code>## mkin version used for fitting:    0.9.50.3 
+<pre><code>## mkin version used for fitting:    0.9.50.4 
 ## R version used for fitting:       4.0.3 
-## Date of fit:     Wed Oct 14 16:00:05 2020 
-## Date of summary: Wed Oct 14 16:00:05 2020 
+## Date of fit:     Thu Nov 19 14:46:15 2020 
+## Date of summary: Thu Nov 19 14:46:15 2020 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -1842,7 +1881,7 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 572 model solutions performed in 0.137 s
+## Fitted using 581 model solutions performed in 0.134 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -1860,7 +1899,7 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
 ## parent_0 93.950000  -Inf   Inf
 ## log_k1   -2.302585  -Inf   Inf
 ## log_k2   -4.605170  -Inf   Inf
-## g_ilr     0.000000  -Inf   Inf
+## g_qlogis  0.000000  -Inf   Inf
 ## 
 ## Fixed parameter values:
 ## None
@@ -1872,19 +1911,19 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
 ## 
 ## Optimised, transformed parameters with symmetric confidence intervals:
 ##          Estimate Std. Error      Lower     Upper
-## parent_0  93.9500  9.998e-01    91.5900   96.3100
-## log_k1     3.1370  2.376e+03 -5615.0000 5622.0000
-## log_k2    -1.0880  6.285e-02    -1.2370   -0.9394
-## g_ilr     -0.2821  7.033e-02    -0.4484   -0.1158
-## sigma      1.4140  2.886e-01     0.7314    2.0960
+## parent_0   93.950  9.998e-01    91.5900   96.3100
+## log_k1      3.117  1.929e+03 -4558.0000 4564.0000
+## log_k2     -1.088  6.285e-02    -1.2370   -0.9394
+## g_qlogis   -0.399  9.946e-02    -0.6342   -0.1638
+## sigma       1.414  2.886e-01     0.7314    2.0960
 ## 
 ## Parameter correlation:
-##            parent_0     log_k1     log_k2      g_ilr      sigma
-## parent_0  1.000e+00  5.157e-07  2.376e-09  2.665e-01 -6.837e-09
-## log_k1    5.157e-07  1.000e+00  8.434e-05 -1.659e-04 -7.786e-06
-## log_k2    2.376e-09  8.434e-05  1.000e+00 -7.903e-01 -1.263e-08
-## g_ilr     2.665e-01 -1.659e-04 -7.903e-01  1.000e+00  3.248e-08
-## sigma    -6.837e-09 -7.786e-06 -1.263e-08  3.248e-08  1.000e+00
+##           parent_0     log_k1     log_k2   g_qlogis      sigma
+## parent_0 1.000e+00  6.459e-07  9.147e-11  2.665e-01  8.413e-11
+## log_k1   6.459e-07  1.000e+00  1.061e-04 -2.087e-04 -9.802e-06
+## log_k2   9.147e-11  1.061e-04  1.000e+00 -7.903e-01 -2.429e-09
+## g_qlogis 2.665e-01 -2.087e-04 -7.903e-01  1.000e+00  4.049e-09
+## sigma    8.413e-11 -9.802e-06 -2.429e-09  4.049e-09  1.000e+00
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
@@ -1892,7 +1931,7 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
 ## for estimators of untransformed parameters.
 ##          Estimate   t value    Pr(&gt;t)   Lower   Upper
 ## parent_0  93.9500 9.397e+01 2.036e-12 91.5900 96.3100
-## k1        23.0400 4.303e-04 4.998e-01  0.0000     Inf
+## k1        22.5800 5.303e-04 4.998e-01  0.0000     Inf
 ## k2         0.3369 1.591e+01 4.697e-07  0.2904  0.3909
 ## g          0.4016 1.680e+01 3.238e-07  0.3466  0.4591
 ## sigma      1.4140 4.899e+00 8.776e-04  0.7314  2.0960
@@ -1904,7 +1943,7 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
 ## 
 ## Estimated disappearance times:
 ##          DT50  DT90 DT50back DT50_k1 DT50_k2
-## parent 0.5335 5.311    1.599 0.03009   2.058</code></pre>
+## parent 0.5335 5.311    1.599  0.0307   2.058</code></pre>
 <p>Here, the DFOP model is clearly the best-fit model for dataset L2 based on the chi^2 error level criterion. However, the failure to calculate the covariance matrix indicates that the parameter estimates correlate excessively. Therefore, the FOMC model may be preferred for this dataset.</p>
 </div>
 </div>
@@ -1930,10 +1969,10 @@ plot(mm.L3)</code></pre>
 <p>The objects returned by mmkin are arranged like a matrix, with models as a row index and datasets as a column index.</p>
 <p>We can extract the summary and plot for <em>e.g.</em> the DFOP fit, using square brackets for indexing which will result in the use of the summary and plot functions working on mkinfit objects.</p>
 <pre class="r"><code>summary(mm.L3[[&quot;DFOP&quot;, 1]])</code></pre>
-<pre><code>## mkin version used for fitting:    0.9.50.3 
+<pre><code>## mkin version used for fitting:    0.9.50.4 
 ## R version used for fitting:       4.0.3 
-## Date of fit:     Wed Oct 14 16:00:05 2020 
-## Date of summary: Wed Oct 14 16:00:06 2020 
+## Date of fit:     Thu Nov 19 14:46:16 2020 
+## Date of summary: Thu Nov 19 14:46:16 2020 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -1942,7 +1981,7 @@ plot(mm.L3)</code></pre>
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 373 model solutions performed in 0.084 s
+## Fitted using 376 model solutions performed in 0.081 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -1960,7 +1999,7 @@ plot(mm.L3)</code></pre>
 ## parent_0 97.800000  -Inf   Inf
 ## log_k1   -2.302585  -Inf   Inf
 ## log_k2   -4.605170  -Inf   Inf
-## g_ilr     0.000000  -Inf   Inf
+## g_qlogis  0.000000  -Inf   Inf
 ## 
 ## Fixed parameter values:
 ## None
@@ -1975,16 +2014,16 @@ plot(mm.L3)</code></pre>
 ## parent_0  97.7500    1.01900 94.5000 101.000000
 ## log_k1    -0.6612    0.10050 -0.9812  -0.341300
 ## log_k2    -4.2860    0.04322 -4.4230  -4.148000
-## g_ilr     -0.1229    0.03727 -0.2415  -0.004343
+## g_qlogis  -0.1739    0.05270 -0.3416  -0.006142
 ## sigma      1.0170    0.25430  0.2079   1.827000
 ## 
 ## Parameter correlation:
-##            parent_0     log_k1     log_k2      g_ilr      sigma
-## parent_0  1.000e+00  1.732e-01  2.282e-02  4.009e-01 -6.868e-07
-## log_k1    1.732e-01  1.000e+00  4.945e-01 -5.809e-01  3.175e-07
-## log_k2    2.282e-02  4.945e-01  1.000e+00 -6.812e-01  7.631e-07
-## g_ilr     4.009e-01 -5.809e-01 -6.812e-01  1.000e+00 -8.694e-07
-## sigma    -6.868e-07  3.175e-07  7.631e-07 -8.694e-07  1.000e+00
+##            parent_0     log_k1     log_k2   g_qlogis      sigma
+## parent_0  1.000e+00  1.732e-01  2.282e-02  4.009e-01 -9.671e-08
+## log_k1    1.732e-01  1.000e+00  4.945e-01 -5.809e-01  7.148e-07
+## log_k2    2.282e-02  4.945e-01  1.000e+00 -6.812e-01  1.022e-06
+## g_qlogis  4.009e-01 -5.809e-01 -6.812e-01  1.000e+00 -7.929e-07
+## sigma    -9.671e-08  7.148e-07  1.022e-06 -7.929e-07  1.000e+00
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
@@ -2038,17 +2077,17 @@ plot(mm.L4)</code></pre>
 <p><img 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N6hElRmhmUHC/rPK97jXX3VJwrKO1SCytwnyeGCnhvmPfGag7/rGCgo71AJKnOfJIqCnhniM9mViqKgvMPdDMunB/u87jpF+/mG0+Nb/ht8FJQtTGdYLhxunPe+fhBVl04P8p6YrRzGhPy/WFBQLiO1oF2sWlBQOZQELd20Bdb0mFY2gbJhjfHOIa3CKTt1eoj3eK3eHoxG0GSRasnJkmYUVA4lQUfGPtL1yS39pOeoMSjo2eE1X7X7DRi30Ag6zytt/fpa68s+F+c3Nn6EeMCPRcc0CpWgAfklXv+D3EBJM5O/+AujvYefYZDH1VAN8Qdarofy81WfNX6CSGpA2ykNQyVoUHZelYNwpY6kmdGQdDnV+4U/mGRyJXSfQfP6P289lR0O8XIoHqgPCH4tNq2D9HQKZgW9Pt2371FGuVwF7U7SuiSrJhRUDsW9+F+OwXfj10hPN2VY0Ly5AU//wCybK8DDTGzh5GQRKZ/0STHfo7FwSVgc+/9z54GCsoVPQedEffRB+FrzC/2HTaM38DBPDhEoKFv4FNT3NMAPzcteln7Rtt6yQtqkrgEFZQuXghZXFd4wL/uWb9r7tP8b1pdccggKyhYuBYWHPwKY2+3Otl+f9x6lgQOjKChb+BT0SGib6MZWNl4Y5/Pcz7SpnQ0KyhY+BYXC7/frbTTnzK/z2Fe83WvpTlBQtnAqqCwlHzZvku72GXPsgIKyRWuCCnzTxX86v+c6oaBs0aCgAL8NrjGIt9sxWEBB2aJJQQGuzgx4nM8PoygoWzQqKEDxmpiG7+W5YEUqQUHZollBBb7v4T3mL9esihwUlC1aFhTgzDifxG/4GulRULZoW1CAgvebNl7C00iPgrJF64IK7O7u/cr/XLpGe6CgbPEAQQHOTvR7/DNOTshDQdniEYICFK1tHTKbi9uAoqBs8RBBBX5+sUZfd84mbsZVgv6+ZPUN5mviEM8RFODfdyKaLnb3/5qLBP3Yf2iv2tq75lU9niQoQOmuXjVePOCutRtxjaCltX4DeLcX81Xxh2cJKnBpdljM+268jzKNoAVTkzcL/6RImm3U84I4WcZvEY6vSjN4nKAAhu3P1BjstrdRGkFTuqTHrAaoIm22rqfhodMAK7s6virN4IGCClx8I7zFEvd8GqUR1D8XroZdvC1oydtpItEh1qFLQ6a87HvE8VVpBs8UVHiL+bpXjf7u2KmnETTsGkBG19uCFk8ZL9IkuHyQ/qTxZNj90+e5464TLoebmyiw58pbEY3m2bq7oVOhEfRtn8kAfTtVljTfUc99oQ1rPmfn5queBkc3UXAC+wbW6PaFrYubnAfVXvzhz4X3/k2jJK3l66mvsw0Ku6ZRrENjcHUTBTbccXZT3gdtA8adcOHanXyY6VhT4eHrTszXwS2Kgqaakb5NirC/iQI9n9a/u1nmHS0nxgW0We6yPSYnC3q+9i2AtRXhAKgZRUHT09NfbJw2t+mbNpY74yYKlByuvd+wzf/CnY36rd0fem6Ha25N7+wD9QnP/bCxzrfM18EtJEN8i38BcqJtBUhuolCQGC8SUJtlD9UxS9jNgJQ1Vu3Zi2KDUl0x1Dtb0LyJrZ/6mvkq+IVE0MBCgKJQG8v1K/NzJyWlFVtel+7eKRJfl20f1TB7gvDQ9yNbi46Pq/2fxU6/fYiSoEkWyFPys9PpBkgEHf7Yps2dRtpY3i++sGf3tQlDJM3uLOjxgF0FGwNkDi7d2v7cQ0mbnDvtg5KgmRbIU6KgcpgF1S9/tu8KWwdr/IvANx/ynDRHvWN81bzaI1nyi3MzHvUe8r0TL2MiGOKL92zf/kUseUoUVA6zoIb5vfXv2TphPfooxJyGK9JzFjgv6Lk5TUMn/eqs7ASCvtDSJylkPnlKzuvpXEgEfbVPpKH7UBvL94f0Smw8osEqSTP/BT0yNqj5vHNOSU0gaD39G/svJJKn5L+eToRE0Lr5caCXjuNGirbMn7LklLRVCwU17HqhZtxyJ9wUlEBQ/8Ltb0ET8pRaqKfTIBE05EocXGtInlMjBS3a0svr6XX5jLMSCDo6LjtiZAx5So3U0zmQCLqwWej0iKXkObVT0Nw1Xbx6b2G6W09yHPQE7J99mjylYj0/jg561jkfWNwP0Xfxu15PUzOzsXYEFch+v2PN/l+ZTw/S/3aKNh+BoOkin5CnVKrn9vA956a04OSya9aQCJqp8qiMpgQV+Ofdtj4vfq0X9p3qRwR2yqFLRiBoamrqiLAZ5CmV6tlP3EuN9NCzl0kEjQ2ffkpNTq0JKnD27Va1Bu+MXAe3ho6gy0T4Vee1p8hTKtWz10bh4eH95Am1BNEQ/+fs5h1Xk+fUoKACp+fH3DVoR8lxFbvXtiAUVK/iDsZK9VzT8h9YF8rzvOgUkJ0PWrIjrip5Tm0KClBc7Y1W3p0j6f6nCQTtI9C4P3lKxXpOe6B6DPe3P3EQEkG3pPh1/VjF4RitCgrDHt+1zKdRjeQtNx3PQSDodoHMYsWwMpTraWB9sMxRrjG/GIVE0M4Z6k731aygJfM7JnwGF5c85tVznaP7SkqC2jsBXAbN1PNQ05rVxzE+z4FA0Gzp/eCV0ExBZcn+IOHBLssdmoxMSVB7J4DLoJV6loRuhOxHlrNNSiCoocVJdTm1UlC75G7o/VC7t9TPME4wxMufAG4bSz1/6dq4Nz8ToVrzSwvh4b8qTnQlgWSI71a9Y0JCAnlOjxBUoOirwf5Rrx8kGLP2xAV3/c30lEBQ2RPAZTDX86z/B78uDHbC6QOs+F994eHDvmyTkgiaZYQ8p6cIKmD4YXzDOsO+VtijOeG/+czi4H+NzwkElT0B3P48A+8MFx56rVNM7zYMrV/785swxpe8UJ0PKoMHCSpyck7bGj0/tPfONWui8NB1s/E5gaCyJ4Dbn2fAeLnVkGUkXXYTlwbWbaXiK1wiqM4HlcHDBAXx4sBnvNrPlb3mbuJs4eE504VQioJG/pFgxMYiq3kGLvwlkhQuPNyErNDv/9rhe1R4w/jb+Lpi/BtPdT6obTxPUIHCL4fWqTtqp83B/vvwv+AHf9NUSYqCfpoj+5FJOs9AfqNwkQfuER7eBnjlrsp3+wr/fhtuel0h/m1Ucc8HVc8vs/7zUI+VNq7IW+rtE/KV6SnhV53/2mpUmmeg5KyHnrJkh4p9Pqh6Lq/qVSN26n7pJBCGq5ZnBIJuGQBtqy+ytUQyz4AZT66nIhX+fFD16HePbeKb8rHMFfYEgobsy+x2LZx8hR5eT/uQTh5mc0iSoQIU9PSyrl6tplu9kQKRoAGlqSsgkHxlFaCe8hCdLCI7JNmmYhS0+JvXIn36rpF+IiUQNLFHcM6rbcnXVDHqKQPRRXM4JMlwLr1HzRapu8vv2hMImrv0CMw8S76WilNPG5AIikOSHW7tm9LKK+Hdsi/tCQSt4F98qINEUBySFLj+yaCyGTsJBMUvPlRAIigOSSogEBS/+FABiaA3xrVqP9PmSeZc30TBPZAcZsIvPsghEbRH3wMHnxtgYzn3N1FwAwSC4hcfKiCawFb4QH/L1vmLfN5Ewb2QfNWJX3yQQyJoh4sA51vbWM7jTRTcjaKgp34sBMg5+S55SqynHOa7fDzrP2SI70Qbyzm8iYLbURJ04V0hvl8Fe9XpQJ4S6ymHKOgyM+ttBUhObshvEyPiHcC4k1pCSdDav8Ae3VpVlz6ioHKYL5o7teuEnbu1dSn3/MhBkS71mXRNmygJWkP8UXdHHBRUDqOgn4bXbhMWYH1jF4FkkWrJyZJmLKgdfIQff3UpsZ5yiIJuC90tPB5s+LmN5fO80tavr7VeOvpjQe1QMzs721f4UXE/HKynHKKgbU33tP6pja2AAy3Xg/XxJyyoHapYIE+J9ZRDFLSW6fNSqe1hKa//89Z7RFhQtmA95RAFjTxjfPqP3KyE66xnksCCsgXrKYcoaNrjecJj0TMMZwT2aFBQtigKWjrEb9DM4cF97BxokoIFZQvWUw7TcdBj741doGp+aSyogxRMTRanJ0mRNGM95dDZWWYHLKiDpHRJj1kt7OhLm7GeMqCg6qER1D8XroZdREHLg4IyhkbQsGsAGV1R0PKgoIyhEfRtn8kAfTtVtrwu6BIvEoAn38iAgqqHai/+8OcAhk2jLC9Lv9spEl+PQb+0CgrKGNrDTH9aN2E95UBB1UMraKx1E9ZTDhRUPSgoW1BQxtAKauMuLlhPOVBQ9eBXnWxBQRmDgrIFBWUMCsoWFJQxKChbUFDGoKBsQUEZg4KyBQVlDArKFhSUMSgoW1BQxqCgbEFBGYOCsgUFZQwKyhYUlDEoKFtQUMagoGyhExRvomAFCsoWKkHxJgrWoKBsoRIUb6JgDQrKFipB8SYK1qCgbKESFG+iYA0Kyha6nSTpTRQiwkUe8GPRM41CLWgXqxYUVA4lQa0muzr3l0hSAxY90yg0guKc/9ZQCYqTXVlDIyjO+W8NlaA42ZU1VEM8zvlvBd1hJpzsygq6z6B3zvlvWPu+SKtw2k5pGCpBpZNdmUFBKSg/5//NYYNF6gfRpdQ0dHvxksmuzKCgFODcTHeCJ4swhrepb7LHtE85RvH77gYFZQxnghZHj9zzrr+Nt2WtgIIyhrO5mfa0Fh4mTXE8gbtBQRnD2Vedm7oLD0uGMeuLy0FBGcOZoOf9TkDOwxvZdcbVoKCM4UxQWOcb7TOaWVdcDwrKGN4EhbyfrygH8QsKyhjuBNU4KChjUFC2oKCMQUHZgoIyBgVlCwrKGBSULSgoYzxD0Gvb9xtcvlKboKCM8QhBt/p1jmyV4+q12gQFZYwnCKr3OwClw15z8Vptg4IyxhMEPREhPOx9xMVrtQ0KyhhPEPTGQ3kAH/R28Vptg4IyxhMEhWHtPprn+5Or12oTFJQxHiGoIaPvcE5Ow0dBGeMRgnIECsoYFJQtKChjUFC2oKCMoRFUvzI/d1JSWrGkGespBwqqHhpB+8UX9uy+NmGIpBnrKQcKqh4aQf2LwDcf8upImrGecqCg6qERNPooxJyGKxGSZqynHCioemgE3R/SK7HxiAarLK/zq+uM+LPomEZBQRlDtRdftGX+lCWnpK3LB1Gk1Dp4nyTGOGHyMBRUDrxPknqcMDcTCiqH2vsk5QfUELkPPzM5Dgp6J2zvk5R3XWQhFtRxbEwehoLK4eB9krCgbMF6yqH2PklmsKBswXrK4eBhJiwoW7CecqCg6kFB2YKCMgYFZYszBM3wiTERXbkqEXdrP+xe8ybHPPitY0VjVc9771MMuZ8gzT1VlNPco5ym8v2KIVW8Yuxht54OClp80Mze+z4kYUUVlmHLqxKFLatGFLa0OlHYEi/LNh9hPyOHqno+NkAxZL6fcproVxVDJjVSThM+QzFk2GMH7WG3ng4KWkZBVaKwvOpEYbkPEIXd8CIKu16DKCzbmyjsSi2iMEpI6vnSEsWQP+opp0n8TDEks4NymtgDiiEf9VVOIwcKioLKg4JKQEFRUAkoKAoqDwoqAQVFQSXQCloSRxRW3JEs7FGisKLHiMJuxhOFFXQiCsvvTBRGCUk95yqbdbmbcppX9yuG/DpYOU3K34ohu15XTiMHraAI4lRQUIRrUFCEa1BQhGtQUIRrUFCEa1BQhGtQUIRrUFCEaygF3dgwfL5SzIKwgP4FJJFzkwkS7o/x739LOWxeUO2RpUph2U3BskZ7kcYw4q2gQjE/UTeUC0lQReUKEpSPvnR0gl7xO5PT0GpenDs57J2d02UaQeRBr2TlhEUhR4riMxTDDgdk58duUghbEBVq2QR7kcYw4q2gQnm7SLqhXEiCKipXkKB8DEpHJ2hGCsC0yfZjPheWpycrR+a1TEtWTrilF0BhgWLY0eC8kkc2K4TtTA+1bIK9SGMY6VbQoZifpBsEhSSoonIFCcrHoHR0gs6aBLByoGLY1ZjNypH9169PVk44v2usX69c5Wxj7vfqqpjtfKhlE+xGimGkW0EHSX7FbhAUkqSKyhUkKB996egEnSmutb9S1KqwVcqR654Hoa6KYVMjTuV3n6wY9l3sgZNPfKgUJpbPFGM30lRloq2ghCC/YjdICklQRYIKEpSPvnR0goprnDHRfowhOfEiQWRSQGital0Uw5aMAFibpBg2fqYwjvVQChPLZ4qxGymGEW4FJYr5CbpBUkiCKhJUkKB89KWjE/Ry4JXCSIVPvpvjSSOFP3zFsFMN/85LSFMMW9Piam6/aUphYvlMMXYjxTDyraBBMT9ZNxQLSVBFggoSlI++dJSHmdZHRSodOxhduUqVKikkkUJdlcMywgIHFyuGlY4PrP38TaUw4wBkirEXKYaRbwUVSvnJuqFcSOUqElSQoHz0pcMD9QjXoKAI16CgCNegoAjXoKAI16CgCNegoAjXoKAI16CgCNegoAjXoKAI16CgCNegoAjXoKAI16CgCNegoAjXoKAI16CgCNegoAjXoKAI16CgCNegoAjXcCvon/GJi9zdB8T9cCvohO+gnbv7gLgfbgXNKz2X5O4+IO7HpYLqvH18fN6C0rlRtdrtADiue0dovHLXZCh9o6lP3NE7gz9PuKCYMNOGw7baEM3iWkHzjf8M7XTasCdkExyvJt7BM6PaZBj19OXS9JDC8rE7xpUqJyQS1DTBL1imAX41oPYoc+q0nao3AXExbhD0uF+e8LirruF4s/rXAbp1m3zB+4bQkvR9+dgXnuqRfOdvl2abfm6/IhLUNMEvWCYD3tr0ek6TrX+2jpoLV/uw2CiuUTVmlSc9tfwrN45KbhD0vUHGF7VPH28+6kMoqjd78qdP2f21d8LqjTFkPdMiVfyBWeFhrxlfgbFwxoWdtwDE7jE9lxbTNMEvWCYDPrIX9E9uG/MttLk56pgzNpIr1IxZZj6NFB8rqKDC33M4jJ1pfNFm1/Hmu3vAV4PSJi98sXzUsLgSeHbZ7de7oy7n95mfVe0kiD/bm+cUxa8Wn4FYONNCwb2/wktNz43FHNNVZLY5w9WYzXB7+uI59z4Df7aJnvP7S67YZvdCNGbdHpZEcv4QHyuooMZyLRhufBF+4njzWyFFL32RNnldV7Hh+HFT1MEL/y2PS5kAABkkSURBVN3dutwH0NdD4+Iie2cJf/viz7gFABsGZJnuP5+ZZFp4w69k9nRzoHUxxQl+odz0xYeiMsTXKRedtJkcQTBmGQcj49iTlxQQ+l/IStC/HNTixdTtwgeg1HQYE+DbuzgzybTMHVvg0pUZy7U/uEh4PBSgP94cUr5scDNt8v98xL/wJ1Za4jY0K//pKG0GgEGflSDUUvgZuxDgkxTxGYiCmhZC1x3N/zYHSgU1TfALljl+N2QCLBLnSt87FSY063jWiZvLAQRjljgYmcaepT1K9w0Qirz8scLrEWZBT8QV6lvuyUwyLXPHFrh0ZaZPRH26X4IDDVeDIOin/+kKaZMhpcc1+NT3qiXulZfL/9ahiOv67u9ZBN0ak1fceWWZoKaF8HFcR0ugdIg3TfBbelJvmuN3WZz+RjfxO6qkvPOdYNMM12y5uyAYs8TByDT2HKkzYY9Y5Gc3AbxpeQfN3vGGz/bMJNMyd2yBS1dmElQ/LcKv9WcgCppX5QNRUP34Bn6P/GQJ+6n5yjt+bUmDoCFl76AwvW746FtlgpoWQn7VVZZA6TuoaYLfIt1p0xy/+oFhgSP0ABvfBxgfHfePUzfY7RCMWWIpzePQjRXd2gqv+wqf2OcZBR2Rvr/x/KwkQVDTMndsgTtWah99ix+O7PnZ3b3wDAjGLFFQ09gzcwxcr/5vVsKKTkW5UamZTfTXw9Jnj4SLoV9kJpmWuWML3LBOBdKGAgx92t298AwIxizjYGQce860rh36rriTNCIkevZbxf0bdJn+yanoJk/Pi81MMi1zxxa4Y6UIQgoKinANCopwDQqKcA0KinCNoqAHJg/sP8mJd/9DEHsoCTqv4bSVK2dEvuWSziCIFCVBw3LFx8JISXPOzgrMN0XM/xuwnnIoCVrfeDbFlWaS5vTA+IqL99dUMtoC6ymHkqDr/VMmTeofuEHSvHwQ5f+Ilum8nXlKrKccijtJVzJmzlp5RdqKBWUL1lMOB/fisaBswXrK4eBePBaULVhPOdTuxRc8ZfxcG9aYRc80CgrKFipBpXvxpXuMRwbi61kaKsCFPVJQULZQCSqzF5/SyPykNDjd8Z5pFBSULc7Yiy8TtOi7sHcc7ZhWQUHZQieobSyCvvtQWA3feZemD1tjcCyRFqERtHTrNsgYuFgvaUZB5aAT9Mewc3DSL7z66Pc6POdYIi1CI+jIJk90j1rU7WVJMwoqh5KgTUNNSJrNgs6cLDwMfdR/uKEo5CRFF7UFjaD+ufp7DsGNOmUN+4w7nS/3YNAvrUIlaHZU+nkRSbNZ0HeHCQ8926yM611sXEtpiePd1A5Ugv4LcwD0YZbXBU8YD9sFBLDomEahG+LftjlFoVnQcwHvH5sfMr3vzWfa1/oHiodXr5pYAQ470Qg6uulegMy4gZLmsp3Oiogzd5KOdI9M/qPwkdhnqwZegNcTrxelPu5YQi1BtRe/9QjAhmXSnSQUVA5KQU0Ydn/01+zQX6OOAtzyynMso4ZwwmEmFFQOJoIaWetX/yBAyYM3HcuoIVBQtrhIUNjzQIOTF1+oALujKChbXCUonPDx8h/sjvl7XAwKyhaXCQrXHkvIcSyfpkBB2eI6QUE/POJ3xxJqCRSULS4UFGCF31eOZdQQKChbXCoo7AuaYZ5fvvAXD51fGwVli2sFhYuPJFwX/90Z2Ny/l0d+9YmCssXFgkLJqPCDwvtnwPdQnDTXsfR8g4KyxdWCAnxSawkcihGefNXFsfR8g4KyxfWCwu8teh4P0gMs7+dYer5xh6Dnpw5fR3DzUk3iBkGhaER4u967Pgj40bH0fOMGQf/2H7u4zYv2YzSLOwQF2OIb37H3Pseyc44bBB05C+BmwBnm6+UC9wgK5zrGVcSCOoZSPRM/Ex7au+dWWk7HTYKCYY7vR45l5xw3CDrtBYAzPteYr5cL3CUowM9NemUrR2kONwia9/DDff3eZ75aPnCfoFA4JvBzx1bAM+7Yi7/1zUenmK+VE9woKMD39fpdd2wV/ILHQdniVkGh4JXamx1bB7egoGxxxvygagq6r1EPz7rU01WCFh084anH5u/AGfODqvqLL5zku9yTKu0iQX8KiQlrXwEuUGA7P6gZlUPS0VZtj6v6Ba6hEbRgarL4iSdF0myrng0+A8PLwxxflWZgOj+oGbWfmQxLao3PV/cr/EIjaEqX9JjVAFWkzdb1vOQvPByTvjN4Ik6dH5SYS8khm9T+DqfQzc0EV8Mu3hbUsOZ9kVbhVpFFDwiD15ePOr4qzeDU+UFVkBn5uGdMMEYjaNg1gIyutwUtHD5YpH6QdejLHbd+GPKZ46vSDFSClm7aAmt6TCubjSG/us5ILQc6ol9Y69UbDvweb9AI+rbPZIC+nSpLmm39wesXP9nT5sRYngaVoCNjH+n65JZ+jCa7ujzIf/kth36TJ6j24g9/Lozrm0ZJWvE4qBxKggbkl3j9D3IDJc0OF/TnuCjNvyvggXq2UAkalJ1X5SBcqSNppijo5vpPHnP4l7kABWUL3YH6gODXYtM6pEqaaQpa8o7vwHOO/7r7QUHZQrcX/8sx+G78GukXQXQFvTHBe7yGz21EQdni3pNFbHNhSK2Jqz7R6C49CsoWHgUF+KhKlcgAbX7/iYKyhU9Bm3x9tFuNRkW0adwBCqqS7NN2zxXiUtDiKkKfd90b9G4hZSI3gIKq4mb3msFN7I2VXAoKQb8CfP3wwcTAtwtoU7kaFFQV45OLIaOpnQA+Bc0ImTPTfxvA4R5+b2hsZwkFVUXMIeHB/x/5AD4Fhb2vpR41Pvmtn3eqne7zBwqqikd3ABR52TnZklNBy3FqRM0hGpqZGQVVxccNtx14RnqGdnn4FxTgylTfZzQzTw4Kqo6PO8ZOs3dzIi0IClCwuF6rjdIbsPEJCsoWbQgKYNjcPmSuFi6iR0HZohVBBQ6l1BjM/6lOKChbNCQowKXptTt+yvlIj4KyRVOCApSsbx84TXp/eq5AQdmiMUEFjg2r2W27wZlroAIFZYv2BAXIWx4bOovXt1EUlC1aFFTg0NCaT2/h8j5LKChbNCooQMGqdn5jODxlFAVli2YFFfh9YlDLxbzN0oyCskXLggLc2vHcQ0mbuTqxGQVli7YFFcjJ6Og9KJOfvXoaQZuGmrC8Lp46XqRJMIuOaRTNCypwdk5UnbE/u3adstAImh2Vfl7E8rrkrTSR6BAG/dIqniCowPGJ4Q2n/uby1dqAaoh/2+bEKjjEy6EdQQX2v1qn6Uz3z5CHn0HZoihoqhnpPN9G6OeoZ0np3lcCm05z86EnFJQtioKmp6e/2DhtbtM3bSxnMUc9Wwx7RwU3mnjAjZPeo6BsIRniW/wr7CxH21jOZo56xpT+lNoweOS37jrpCQVlC4mggYUARaE2ljOao54peW8PX1b86+yW3imf5Lpj/SgoW0gEHf7Yps2dRtpYzmyOenbkRwxYnNjhFsD5pU88+PiiUy7vgJKgSRbIU6KgcpgF1S9/tu8Km0OmZI760sydIvH1WPZQHe/3EbrR2rRReZ8O8G06/jvXztqsJGimBfKUKKgcZkEN83vr37P136xfmZ87KSmt2PK6ICFeJKA22z6qYYK4Mzd0qeWl4cfJ0TV7r77kug4QDPHFe7Zv/yKWPCUKKodZ0Ff7RBq6D7WxvF98Yc/uaxOGSJrdWdCN7W5BQf395ZsuftCjRszE71y010Qg6AstfZJC5pOnREHlMAtaNz8O9NJ5vkX8i8A3H/IYTgFOjaF7k35ho6Wt+u8nxT6UtOQPF3SAQNB6+jf2X0gkT4mCymEWNORKHFxraGN59FGIOQ1XIiTN7i3ovtWHbbZfXTcgMGzQRmefnkcgqH/h9regCXlKFFQOs6ALm4VOj1hqY/n+kF6JjUc0WCVp5regxxcmPBg9drsz77tIIOjouOyIkTHkKfmtpwsg+i5+1+tptk8WKtoyf8qSU9JWrguq3zu9ffVHpux21tSjJMdBT8D+2afJU3JdT2dDImimym8OuS9owdcTWlXvOC3TGZISCJou8gl5Su7r6UxIBI0Nn35KTU5NFDTvq/GtqrefspP1cE8gaGpq6oiwGeQpNVFPZ0E0xP85u3nH1eQ5NVPQvB2T21VvOXpL2VcNhr+oj5gSftV57SnylHfW8+yGnZxPrsIUsvNBS3bEVSXPqRlBRQq/e+NJr0YDV4pnkv7aJMzn6Ty6fISC6huQp7yjnmv9erVu9q+6PmkZEkG3pPh1/VjFUKgpQUVuHV7cN6RW4pz6y6BkoPRWriohELSPQOP+5CnL1/Om9/8ARr6mvl9ahUTQzhnq5onXnKBGzm8cWrlqy5FzVLy12YJA0O0CmcWKYWWUr+cvLYSH3R3V90urEAiaLb0XpxLaFBSg6IFre+e3v8/36Zk7Hb9zg5Kgdq9QsE35el6rmQvwzvOO9U2LEAhqaKHySh+tCgqDEvZtbbj2/KaxHR5o1G9RlkNHoZQEtXeFggx31POVlksn+nJxeaBrIBniu1XvmJCQQJ5Ts4IWvtE63nRy662jHwyNrho9ZMXPaieAIhjiZa9QkOGOepZuHDpJxUF+zUMiaJYR8pyaFVRCYdbiAZHVHh664pCKD4wEgspeoSCDp9TTIajOB5XBowpasPedfpH3R7/43g/2jmMUn7XUh0BQ2SsUOLtKVjXXR0U/zvqKF6rzQWXQTEGJufnjkkExVSP6pO2wfSB//kN1/DeZnhIIKnuFAn9XyaqitOOQQ5uCdrNNSnU+qAxaKahKSo6sfvXRmgGdx390TGLXrgYX4Bf/M8bnioJG/pFgxMYi6VWyN5N7ioRUEx42Ahzqyfe/n4n9fCmFbd5mNOeDyuChgpo4+8Wsng3vj+4//+uLZW3G60ySPzQ+VxT00xzZz/TSq2QN/90o0t5fePgdIHsj3//uChf+XZbINm87mvNBZfBoQY0U/LTilUd9fB59+f0fcoSXs8cJD099ZlxE+FWnzS8rObxKVg1FQdsgL34x26R054PaRisFpeWfnW+/0LJayJNj53gv/zWtrukyfAJBtwyAttUX2VoiuUrWjGbquTc8zOslxjNhkk4epub0BM0UlAWlf32eltLo7mopf5teEwgasi+z27Vw8lVop56Gv3NYpyQ6WUT2L9422ikoMwxlx6AIBA0oTV0BgeTJK2A9b0N00ZzH/sU7AQJBE3sE57zaljwl1lMOs6D4F68CAkFzlx6BmWfJU2I95TALin/xKiAQtEJ/M6cWEkHxL14FBILiN3MqIBH0xrhW7WfeJM+JBbUPfjOnAhJBe/Q9cPC5AeQ5saD2wW/mVEA0ga3weemWzdPDNH72jTMgEBS/mVMBiaAdLgKcb21jucbPvnEKJF914jdz5JDc5eNZ/yFDfCfaWM7lHPVuRlHQUz8WAuScfJc8JdZTDlHQZWbW21jO4xz17kZJ0IV3hfh+FexVpwN5SqynHOaL5k7tOmF7LguNn33jFJQErf0L7NGtVTXbFdZTDqOgn4bXbhMWsMZmgOTsm6JJppufqjiE4nEoCVpD/FF3xg8KKoco6LbQ3cLjwYafywV1uf1Uv8h081MVF4R5HEqC+gg//upSoqByiIK2/d749Kc2NpYni1RLTpY0Y0HtUDM7O9tX+FEx0zPWUw5R0Fqm4ajU1l/9PK+09etrrZfuP2FB7VDFAnlKrKccoqCRpmvB/rE5p/qBluvBejzHgrIF6ymHKGja4+J8hEXP2J5wNa//8wFWjVhQtmA95RAFLR3iN2jm8OA+cpOmrrO+px8WlC1YTzlMx0GPvTd2wX47UVZgQdmC9ZRDZ2eZHbCgbKGqZ+GC5yZcVA7jFhSUMTSCNg01IWmmqafhsR5rxwX+43gCd4OCMoZG0Oyo9PMikmaaev7Q3AAwapbjCdwNCsoYqiH+7Z13vMxvHSPirfKbp/J80lN4WCa93a+GQEEZw/Qz6NGDIl3qO57hr4B/QB+fwaxHLgcFZQytoH9aN1HVc4FfYt2ejKejcSUoKGNoBY21bqKr59nPjtD8urtBQRnDnaAaBwVlDK2gy62bsJ5yoKDq4e1AvdZBQRmDgrIFBWUMCsoWFJQxKChbUFDGoKBsQUEZg4KyBQVlDArKFhSUMSgoW1BQxqCgbEFBGYOCsgUFZQwKyhYUlDEoKFtQUMagoGxBQRmDgrKFTlCco94KFJQtVILiHPXWoKBsoRIU56i3BgVlC5WgOEe9NSgoW6gExTnqraERtGBq8mbhnxRJM9ZTDsWdJMkc9fmNw0Ue8GPRM41CI2hKl/SY1QDSuW1RUDmUBLX6iz/7l0hSAxY90yg0gvrnwtWwiyhoeagExb94a2gEDbsGkNEV61keKkHxL94aGkHf9pkM0LdTZcvr/IAaIvfWYtExjUJ3mAn/4q2g2os//DmAYdOoste510V6Yz1lUBJU+hdvBgWlgPXcTFqHbi9e+hdvAgtKAU59cyd4sghjUFC2oKCMwbmZ2IKCMga/6mQLCsoYFJQtKChjUFC2oKCMQUHZgoIyBgVlCwrKGBSULSgoY5wp6Ikv/mKenHdQUMY4UdCXghMCXmeenXNQUMY4T9AvowvhRt2fmKfnGxSUMc4TdOp04WHEIubpbXF23bYSl6xIERSUMc4TdHlf4aHTFubpbfCx37PtG191xZoUQUEZ4zxBc+q+sm5giyLm6a0p9j4BMO5lF6xJGRSUMU7cScqe8uzsXObZbfBbY+FhXxtXrEoRFJQxnnAcNM/rOsB7z7l4rbZBQRnjCYJCavNFE32PuXqtNkFBGeMRgsJnIyafcvlKbYKCMsYzBOUHFJQxNIKWbt0GGQMX6yXNWE85UFD10Ag6sskT3aMWdZMe38F6yoGCqodu6hv9PYfgRh1JM9ZTDhRUPVSC/gtzAPRhkmaspxwoqHpoBB3ddC9AZtzAsoYc08wiDRn0S6ugoIyh2ovfegRgw7KynaT8QOPcTPdJx/yKBArKGCdMfbN8EF1KTYN3+WCME2YWQUHlwLt8qAcFZQvbu3ycwxmWnTD1DQoqh9q7fOQ3Ms5R/2Awi55pFCd81YmCyuHgXT6woGzBesqh9i4fZrCgbMF6yuHgYSYsKFuwnnKgoOpBQdmCgjIGBWWLUwTtd93Mhb8rCpcsm/yoEwT17Hpeu24Pu/V0UNAtNSzoKhHhAWGVyraZ/dQfquvJYJtI1sQozd017GKvng4KWkZBVaKwvOpEYbkPEIXd8CIKu16DKCzbmyjsikvutUVSz5eWKIb8UU85TeJniiGZHZTTxB5QDPmor3IaOVBQFFQeFFQCCoqCSkBBUVB5UFAJKCgKKgEFRUHl8QBBDWOJwm6NYxmmH08UVpJKFFY8gWUYJST13JilGJI7TTnNst8VQ84tUE4z57JiyNFVymnkoBUUQZwKCopwDQqKcA0KinANCopwDQqKcA0KinANCopwDQqKcA2loBsbhs9XilkQFtC/gCRybjJBwv0x/v1vKYfNC6o9slQpLLspWNZoL9IYRrwVVCjmJ+qGciEJqqhcQYLy0ZeOTtArfmdyGlpN3HQnh72zc7pMI4g86JWsnLAo5EhRfIZi2OGA7PzYTQphC6JCLZtgL9IYRrwVVChvF0k3lAtJUEXlChKUj0Hp6ATNSAGYNtl+zOfC8vRk5ci8lmnJygm39AIoLFAMOxqcV/LIZoWwnemhlk2wF2kMI90KOhTzk3SDoJAEVVSuIEH5GJSOTtBZkwBWDlQMuxqzWTmy//r1ycoJ53eN9euVq5xtzP1eXRWznQ+1bILdSDGMdCvoIMmv2A2CQpJUUbmCBOWjLx2doDPFtfZXiloVtko5ct3zINRVMWxqxKn87pMVw76LPXDyiQ+VwsTymWLsRpqqTLQVlBDkV+wGSSEJqkhQQYLy0ZeOTlBxjTMm2o8xJCdeJIhMCgitVa2LYtiSEQBrkxTDxs8UxrEeSmFi+UwxdiPFMMKtoEQxP0E3SApJUEWCChKUj750dIJeDrxSGKnwyXdzPGmk8IevGHaq4d95CWmKYWtaXM3tN00pTCyfKcZupBhGvhU0KOYn64ZiIQmqSFBBgvLRl47yMNP6qEilYwejK1epUiWFJFKoq3JYRljg4GLFsNLxgbWfv6kUZhyATDH2IsUw8q2gQik/WTeUC6lcRYIKEpSPvnR4oB7hGhQU4RoUFOEaFBThGhQU4RoUFOEaFBThGhQU4RoUFOEaFBThGhQU4RoUFOEaFBThGhQU4RoUFOEaFBThGhQU4RoUFOEa7Qj6lE9NnY/PUHd3w2PQSD21IyhAtnirkE8j3d0Nj0ET9dScoDl/uLsbHoMm6qk5QbMSshIGths6s2vTi/BOWL0xBnf3Srtoop5aFPTBvFs+6TD23d1Rl/P7OPUqYM9GE/XUoqCdAJqfgcXTXg+Ni4vs7e5eaRdN1FOLgj4hFPS8UNC0GQAGvbt7pV00UU8tC3oo4rq++3vu7pV20UQ9tSwoLGkQNIS3v3gNoYl6aknQOynlrZQah9N6aldQpEKAgiJcg4IiXIOCIlyDgiJcg4IiXIOCIlyDgiJcg4IiXIOCIlyDgiJcg4IiXIOCIlyDgiJcg4IiXIOCIlzzf7AuXhMGmiKYAAAAAElFTkSuQmCC" /><!-- --></p>
 <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error level of 3.3% as well as the plot suggest that the SFO model fits very well. The error level at which the <span class="math inline"><em>χ</em><sup>2</sup></span> test passes is slightly lower for the FOMC model. However, the difference appears negligible.</p>
 <pre class="r"><code>summary(mm.L4[[&quot;SFO&quot;, 1]], data = FALSE)</code></pre>
-<pre><code>## mkin version used for fitting:    0.9.50.3 
+<pre><code>## mkin version used for fitting:    0.9.50.4 
 ## R version used for fitting:       4.0.3 
-## Date of fit:     Wed Oct 14 16:00:06 2020 
-## Date of summary: Wed Oct 14 16:00:06 2020 
+## Date of fit:     Thu Nov 19 14:46:16 2020 
+## Date of summary: Thu Nov 19 14:46:16 2020 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 142 model solutions performed in 0.029 s
+## Fitted using 142 model solutions performed in 0.03 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -2102,10 +2141,10 @@ plot(mm.L4)</code></pre>
 ##        DT50 DT90
 ## parent  106  352</code></pre>
 <pre class="r"><code>summary(mm.L4[[&quot;FOMC&quot;, 1]], data = FALSE)</code></pre>
-<pre><code>## mkin version used for fitting:    0.9.50.3 
+<pre><code>## mkin version used for fitting:    0.9.50.4 
 ## R version used for fitting:       4.0.3 
-## Date of fit:     Wed Oct 14 16:00:06 2020 
-## Date of summary: Wed Oct 14 16:00:06 2020 
+## Date of fit:     Thu Nov 19 14:46:16 2020 
+## Date of summary: Thu Nov 19 14:46:16 2020 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
@@ -2191,7 +2230,7 @@ plot(mm.L4)</code></pre>
 
 // add bootstrap table styles to pandoc tables
 function bootstrapStylePandocTables() {
-  $('tr.header').parent('thead').parent('table').addClass('table table-condensed');
+  $('tr.odd').parent('tbody').parent('table').addClass('table table-condensed');
 }
 $(document).ready(function () {
   bootstrapStylePandocTables();
-- 
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