Deploying to gh-pages from @ c757422 🚀

man/gamlss2.html

@@ -635,7 +635,7 @@ <h3 class="anchored" data-anchor-id="examples">Examples</h3>
 *--------
 n = 610 df =  13.12 res.df =  596.88
 Deviance = 4770.1554 Null Dev. Red. = 33.39%
-AIC = 4796.3966 elapsed =  0.78sec</code></pre>
+AIC = 4796.3966 elapsed =  0.76sec</code></pre>
 </div>
 <div class="sourceCode cell-code" id="cb5"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="do">## plot estimated effects</span></span>
 <span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(b, <span class="at">which =</span> <span class="st">"effects"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>

man/prodist.gamlss2.html

@@ -543,10 +543,10 @@ <h3 class="anchored" data-anchor-id="examples">Examples</h3>
 <div class="sourceCode cell-code" id="cb16"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="do">## simulate random numbers</span></span>
 <span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a><span class="fu">random</span>(d, <span class="dv">5</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
-<pre><code>        r_1      r_2       r_3      r_4      r_5
-1  28.10917 -5.39473  24.25124 10.67233 38.25751
-2  55.95634 68.65509  80.59765 73.81860 72.21825
-3 137.13595 82.36866 165.37240 38.93205 98.98676</code></pre>
+<pre><code>       r_1       r_2      r_3      r_4       r_5
+1 38.73663  31.49471 18.97005 43.18781  21.97848
+2 66.80077  53.49317 84.58699 38.98205  46.07852
+3 27.33536 143.16652 90.51953 89.71648 121.49293</code></pre>
 </div>
 <div class="sourceCode cell-code" id="cb18"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a><span class="do">## density and distribution</span></span>
 <span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a><span class="fu">pdf</span>(d, <span class="dv">50</span> <span class="sc">*</span> <span class="sc">-</span><span class="dv">2</span><span class="sc">:</span><span class="dv">2</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>

vignettes/mixture.html

@@ -510,16 +510,16 @@ <h2 data-number="2" class="anchored" data-anchor-id="two-component-normal-mixtur
 </section>
 <section id="gamlss-framework-for-the-mixture-model" class="level2" data-number="3">
 <h2 data-number="3" class="anchored" data-anchor-id="gamlss-framework-for-the-mixture-model"><span class="header-section-number">3</span> GAMLSS Framework for the Mixture Model</h2>
-<p>In the GAMLSS framework, each parameter <span class="math inline">\(\theta_{jk}(\mathbf{x}_i)\)</span>, as well as the mixing probabilities <span class="math inline">\(\pi_k(\mathbf{x}_i)\)</span>, are modeled using GAM-type predictors given by</p>
+<p>In the GAMLSS framework, each parameter <span class="math inline">\(\boldsymbol{\theta}_{k}(\mathbf{x}_i) = (\boldsymbol{\theta}_{1k}(\mathbf{x}_i), \ldots, \boldsymbol{\theta}_{J_kk}(\mathbf{x}_i))^{\top}\)</span>, as well as the mixing probabilities <span class="math inline">\(\pi_k(\mathbf{x}_i)\)</span>, are modeled using GAM-type predictors given by</p>
 <p><span class="math display">\[
-\eta_{jk}(\mathbf{x}_i) = f_1(\mathbf{x}_i) + \dots + f_{L_{jk}}(\mathbf{x}_i),
+\eta_{jk}(\mathbf{x}_i) = f_{1k}(\mathbf{x}_i) + \dots + f_{L_{jk}k}(\mathbf{x}_i),
 \]</span></p>
-<p>which are linked to the parameters through <span class="math inline">\(h_{jk}(\theta_{jk}(\mathbf{x}_i)) = \eta_{jk}(\mathbf{x}_i)\)</span> using suitable link functions <span class="math inline">\(h_{jk}(\cdot)\)</span>. The functions <span class="math inline">\(f_l(\cdot)\)</span>, <span class="math inline">\(l = 1, \ldots, L_{jk}\)</span>, can be nonlinear smooth functions (typically estimated using regression splines), linear effects, or random effects, among others.</p>
+<p>which are linked to the parameters through <span class="math inline">\(h_{jk}(\theta_{jk}(\mathbf{x}_i)) = \eta_{jk}(\mathbf{x}_i)\)</span> using suitable link functions <span class="math inline">\(h_{jk}(\cdot)\)</span>. The functions <span class="math inline">\(f_{lk}(\cdot)\)</span>, <span class="math inline">\(l = 1, \ldots, L_{jk}\)</span>, can be nonlinear smooth functions (typically estimated using regression splines), linear effects, or random effects, among others.</p>
 <p>For the two-component normal mixture, we use</p>
 <ol type="1">
 <li>Identity link for the means: <span class="math inline">\(\mu_k(\mathbf{x}_i) = \eta_{1k}(\mathbf{x}_i)\)</span>,</li>
 <li>Log-link for the standard deviations: <span class="math inline">\(\log(\sigma_k(\mathbf{x}_i)) = \eta_{2k}(\mathbf{x}_i)\)</span>,</li>
-<li>Logit-link for the mixing probability: <span class="math inline">\(\pi_k(\mathbf{x}_i) = \frac{1}{1 + \exp(-\eta_{\pi k}(\mathbf{x}_i))}\)</span>.</li>
+<li>Logit-link for the mixing probability: <span class="math inline">\(\log\left(\frac{\pi_k(\mathbf{x}_i)}{1 - \pi_k(\mathbf{x}_i)}\right) = \eta_{\pi k}(\mathbf{x}_i)\)</span>.</li>
 </ol>
 </section>
 <section id="example-using-simulated-data" class="level2" data-number="4">

vignettes/spatial.html

@@ -601,7 +601,7 @@ <h2 data-number="5" class="anchored" data-anchor-id="estimating-the-model"><span
 *--------
 n = 3494 df =  98.97 res.df =  3395.03
 Deviance = 16484.181 Null Dev. Red. = 14.46%
-AIC = 16682.1271 elapsed =  3.93sec</code></pre>
+AIC = 16682.1271 elapsed =  3.89sec</code></pre>
 </div>
 </div>
 <p>Model calibration is checked using histogram, Q-Q plot, wormplot etc.</p>