geo-sptemp >> eda pairwise dist ex

Author: ercbk

_book/qmd/_resources/Geospatial-Spatio-Temporal.resources/eda-gen-ex-pair-dist-1.png

@@ -1088,6 +1088,12 @@
   <a href="../qmd/geospatial-general.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">General</span></a>
   </div>
+</li>
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="../qmd/geospatial-kriging.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Kriging</span></a>
+  </div>
 </li>
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
@@ -2684,7 +2690,6 @@ <h3 data-number="1.1.1" class="anchored" data-anchor-id="sec-alg-ml-trees-dt"><s
 </ul></li>
 </ul></li>
 <li><span style="color: #990000">{</span><a href="https://bonsai.tidymodels.org/" style="color: #990000">bonsai</a><span style="color: #990000">}</span> - tidymodels extension fits <span style="color: #990000">{partykit::ctree}</span></li>
-<li><span style="color: #990000">{</span><a href="https://cran.r-project.org/web/packages/partykit/index.html" style="color: #990000">partykit</a><span style="color: #990000">}</span> - model-based trees</li>
 <li><span style="color: #990000">{</span><a href="https://cran.r-project.org/web/packages/glmertree/index.html" style="color: #990000">glmertree</a><span style="color: #990000">}</span> - Generalized Linear Mixed Model Trees
 <ul>
 <li>Combines <code>lmer</code>/<code>glmer</code> from <span style="color: #990000">{lme4}</span> and <code>lmtree</code>/<code>glmtree</code> from <span style="color: #990000">{partykit}</span></li>
@@ -2696,6 +2701,11 @@ <h3 data-number="1.1.1" class="anchored" data-anchor-id="sec-alg-ml-trees-dt"><s
 <li>Peforms extremely well when the dataset contains many noise variables, and when significant high-order interactions are present alongside non-significant low-order interactions. In these scenarios, the proposed method outperforms other methods, including the random forest.</li>
 </ul></li>
 <li><span style="color: #990000">{</span><a href="https://grantmcdermott.com/parttree/" style="color: #990000">parttree</a><span style="color: #990000">}</span> - Visualize simple 2-D decision tree partitions</li>
+<li><span style="color: #990000">{</span><a href="https://cran.r-project.org/web/packages/partykit/index.html" style="color: #990000">partykit</a><span style="color: #990000">}</span> - model-based trees</li>
+<li><span style="color: #990000">{</span><a href="https://natydasilva.github.io/PPtreeExt/" style="color: #990000">PPtreeExt</a><span style="color: #990000">}</span> - Extends to the Projection Pursuit Tree (PPtree) algorithm to improve its performance in multi-class settings and under nonlinear separations
+<ul>
+<li>The PPtree classifier finds separations between classes based on linear combinations of variables by optimizing a projection pursuit index.</li>
+</ul></li>
 <li><u>BART</u>
 <ul>
 <li><span style="color: #990000">{</span><a href="https://alaninglis.github.io/bartMan/" style="color: #990000">bartMan</a><span style="color: #990000">}</span> - Investigating and visualizing Bayesian Additive Regression Tree (BART) model fits.

_book/qmd/algorithms-ml.html

@@ -1088,6 +1088,12 @@
   <a href="../qmd/geospatial-general.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">General</span></a>
   </div>
+</li>
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="../qmd/geospatial-kriging.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Kriging</span></a>
+  </div>
 </li>
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
@@ -2546,6 +2552,11 @@ <h2 class="unnumbered anchored" data-anchor-id="sec-diag-reg-res">Residuals</h2>
 <li>Also see Feasible Generalized Least Squares (FGLS) in the same note</li>
 <li>Example: <a href="https://ercbk.github.io/Domain-Knowledge-Notebook/qmd/real-estate.html#sec-rlest-apprais-ml-cmaprc-marprc" style="color: green">Real Estate &gt;&gt; Appraisal Methods &gt;&gt; CMA &gt;&gt; Market Price</a> &gt;&gt; Case-Shiller method</li>
 </ul></li>
+<li><span style="color: #990000">{</span><a href="https://cran.r-project.org/web/packages/snreg/index.html" style="color: #990000">snreg</a><span style="color: #990000">}</span> - Regression with Skew-Normally Distributed Error Term
+<ul>
+<li>Estimate regression models with skew‑normal error terms, allowing both the variance and skewness parameters to be heteroskedastic.</li>
+<li>Includes a stochastic frontier framework that accommodates both i.i.d. and heteroskedastic inefficiency terms.</li>
+</ul></li>
 <li>Scale model (<code>greybox::sm</code> ) models the variance of the residuals or <code>greybox::alm</code> will call <code>sm</code> and fit a model with estimated residual variance
 <ul>
 <li>See <a href="https://forecasting.svetunkov.ru/en/2022/01/23/introducing-scale-model-in-greybox/">article</a> for an example</li>