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diff --git a/analyze-areal-data.html b/analyze-areal-data.html
@@ -860,7 +860,7 @@ <h1 class="title"><span id="sec-analyze-areal-data" class="quarto-section-identi
 weights: nb2listw(k4.48)  
 number of simulations + 1: 500 
 
-statistic = 0.29439, observed rank = 500, p-value = 0.002
+statistic = 0.29439, observed rank = 499, p-value = 0.002
 alternative hypothesis: greater</code></pre>
 </div>
 </div>

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diff --git a/analyze-network-data.html b/analyze-network-data.html
diff --git a/analyze-network-data_files/figure-pdf/fig-active-maintainer-1.pdf b/analyze-network-data_files/figure-pdf/fig-active-maintainer-1.pdf
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diff --git a/analyze-spatial-data_files/figure-pdf/fig-rongelap-pred-1.pdf b/analyze-spatial-data_files/figure-pdf/fig-rongelap-pred-1.pdf
diff --git a/analyze-spatial-data_files/figure-pdf/fig-rongelap-pred-sp-1.pdf b/analyze-spatial-data_files/figure-pdf/fig-rongelap-pred-sp-1.pdf
diff --git a/analyze-spatial-data_files/figure-pdf/fig-rongelap-var-1.pdf b/analyze-spatial-data_files/figure-pdf/fig-rongelap-var-1.pdf
diff --git a/analyze-spatial-data_files/figure-pdf/fig-rongelap-vario-1.pdf b/analyze-spatial-data_files/figure-pdf/fig-rongelap-vario-1.pdf
diff --git a/analyze-spatial-data_files/figure-pdf/fig-rongelap-vario-norm-1.pdf b/analyze-spatial-data_files/figure-pdf/fig-rongelap-vario-norm-1.pdf
diff --git a/analyze-spatial-data_files/figure-pdf/fig-rongelap-vario-theory-1.pdf b/analyze-spatial-data_files/figure-pdf/fig-rongelap-vario-theory-1.pdf
diff --git a/analyze-survival-data.html b/analyze-survival-data.html
@@ -785,8 +785,8 @@ <h1 class="title"><span id="sec-survival-analysis" class="quarto-section-identif
 <div class="cell-output cell-output-stdout">
 <pre><code>Fixed effects:
                  mean    sd 0.025quant 0.5quant 0.975quant   mode kld
-(Intercept)    -4.172 0.376     -4.910   -4.172     -3.434 -4.172   0
-xNonmaintained  0.983 0.482      0.038    0.983      1.929  0.983   0
+(Intercept)    -4.173 0.378     -4.913   -4.173     -3.432 -4.173   0
+xNonmaintained  0.984 0.483      0.036    0.984      1.931  0.984   0
 
  is computed </code></pre>
 </div>

diff --git a/analyze-survival-data_files/figure-html/fig-aml-1.png b/analyze-survival-data_files/figure-html/fig-aml-1.png
diff --git a/analyze-survival-data_files/figure-pdf/fig-aml-1.pdf b/analyze-survival-data_files/figure-pdf/fig-aml-1.pdf
diff --git a/analyze-survival-data_files/figure-pdf/fig-leukemia-surv-1.pdf b/analyze-survival-data_files/figure-pdf/fig-leukemia-surv-1.pdf
diff --git a/analyze-text-data.html b/analyze-text-data.html
@@ -778,8 +778,8 @@ <h1 class="title"><span id="sec-analysis-text-data" class="quarto-section-identi
 <span id="cb19-10"><a href="#cb19-10"></a>    <span class="at">n_check_convergence =</span> <span class="dv">25</span>, <span class="at">progressbar =</span> <span class="cn">FALSE</span></span>
 <span id="cb19-11"><a href="#cb19-11"></a>  )</span></code><button title="复制到剪贴板" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
-<pre><code>#&gt; INFO  [06:14:42.698] early stopping at 175 iteration
-#&gt; INFO  [06:14:43.321] early stopping at 50 iteration</code></pre>
+<pre><code>#&gt; INFO  [05:04:54.962] early stopping at 175 iteration
+#&gt; INFO  [05:04:55.568] early stopping at 50 iteration</code></pre>
 </div>
 </div>
 <p>下图展示主题的分布，各个主题及其所占比例。</p>

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diff --git a/analyze-time-series-data_files/figure-pdf/fig-airpassengers-acf-1.pdf b/analyze-time-series-data_files/figure-pdf/fig-airpassengers-acf-1.pdf
diff --git a/analyze-time-series-data_files/figure-pdf/fig-airpassengers-add-fitted-1.pdf b/analyze-time-series-data_files/figure-pdf/fig-airpassengers-add-fitted-1.pdf
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diff --git a/categorical-data-analysis_files/figure-pdf/fig-titanic-alluvial-1.pdf b/categorical-data-analysis_files/figure-pdf/fig-titanic-alluvial-1.pdf
diff --git a/categorical-data-analysis_files/figure-pdf/fig-titanic-ggstats-1.pdf b/categorical-data-analysis_files/figure-pdf/fig-titanic-ggstats-1.pdf
diff --git a/categorical-data-analysis_files/figure-pdf/fig-titanic-mosaic-1.pdf b/categorical-data-analysis_files/figure-pdf/fig-titanic-mosaic-1.pdf
diff --git a/categorical-data-analysis_files/figure-pdf/fig-titanic-mosaicplot-1.pdf b/categorical-data-analysis_files/figure-pdf/fig-titanic-mosaicplot-1.pdf
diff --git a/categorical-data-analysis_files/figure-pdf/fig-ucb-admissions-fourfoldplot-1.pdf b/categorical-data-analysis_files/figure-pdf/fig-ucb-admissions-fourfoldplot-1.pdf
diff --git a/categorical-data-analysis_files/figure-pdf/fig-ucb-admissions-mosaicplot-1.pdf b/categorical-data-analysis_files/figure-pdf/fig-ucb-admissions-mosaicplot-1.pdf
diff --git a/classification-problems.html b/classification-problems.html
@@ -1010,11 +1010,11 @@ <h1 class="title"><span id="sec-classification-problems" class="quarto-section-i
  parameter : cost C = 1 
 
 Gaussian Radial Basis kernel function. 
- Hyperparameter : sigma =  0.845469789207426 
+ Hyperparameter : sigma =  0.623008360928226 
 
 Number of Support Vectors : 59 
 
-Objective Function Value : -4.658 -5.2078 -20.1766 
+Objective Function Value : -4.0414 -4.3676 -20.9506 
 Training error : 0.026667 </code></pre>
 </div>
 </div>
@@ -1051,8 +1051,8 @@ <h1 class="title"><span id="sec-classification-problems" class="quarto-section-i
 <pre><code>            iris_species
 iris_knn     setosa versicolor virginica
   setosa         25          0         0
-  versicolor      0         23         4
-  virginica       0          2        21</code></pre>
+  versicolor      0         23         3
+  virginica       0          2        22</code></pre>
 </div>
 </div>
 </section><section id="sec-neural-networks" class="level2" data-number="40.7"><h2 data-number="40.7" class="anchored" data-anchor-id="sec-neural-networks">
@@ -1064,20 +1064,20 @@ <h1 class="title"><span id="sec-classification-problems" class="quarto-section-i
 <div class="cell-output cell-output-stdout">
 <pre><code>a 4-4-3 network with 35 weights
 options were - softmax modelling 
- b-&gt;h1 i1-&gt;h1 i2-&gt;h1 i3-&gt;h1 i4-&gt;h1 
-  0.14   0.26   0.60   0.76   1.01 
- b-&gt;h2 i1-&gt;h2 i2-&gt;h2 i3-&gt;h2 i4-&gt;h2 
-  0.72   3.10  11.52 -13.44  -6.45 
- b-&gt;h3 i1-&gt;h3 i2-&gt;h3 i3-&gt;h3 i4-&gt;h3 
--71.48 -86.52  38.38  86.85  52.74 
- b-&gt;h4 i1-&gt;h4 i2-&gt;h4 i3-&gt;h4 i4-&gt;h4 
-  3.07  -0.60   2.10  -0.40  -2.95 
- b-&gt;o1 h1-&gt;o1 h2-&gt;o1 h3-&gt;o1 h4-&gt;o1 
- -0.13  -0.24  30.32 -10.63   5.79 
- b-&gt;o2 h1-&gt;o2 h2-&gt;o2 h3-&gt;o2 h4-&gt;o2 
- 21.04  -0.40 -28.63 -18.75  16.31 
- b-&gt;o3 h1-&gt;o3 h2-&gt;o3 h3-&gt;o3 h4-&gt;o3 
--19.47   0.37  -1.86  29.78 -20.79 </code></pre>
+   b-&gt;h1   i1-&gt;h1   i2-&gt;h1   i3-&gt;h1   i4-&gt;h1 
+  148.05   169.39   702.94 -1218.15  -537.34 
+   b-&gt;h2   i1-&gt;h2   i2-&gt;h2   i3-&gt;h2   i4-&gt;h2 
+  -51.93  -119.60   -91.22   276.26   130.38 
+   b-&gt;h3   i1-&gt;h3   i2-&gt;h3   i3-&gt;h3   i4-&gt;h3 
+-1371.99   -76.88  -144.45   349.89   337.51 
+   b-&gt;h4   i1-&gt;h4   i2-&gt;h4   i3-&gt;h4   i4-&gt;h4 
+  -24.41  -191.75   -58.38  -229.31   -91.16 
+   b-&gt;o1   h1-&gt;o1   h2-&gt;o1   h3-&gt;o1   h4-&gt;o1 
+   71.09   512.43  -293.22  -543.92     4.49 
+   b-&gt;o2   h1-&gt;o2   h2-&gt;o2   h3-&gt;o2   h4-&gt;o2 
+   -3.08  -325.66   368.75    16.87    37.73 
+   b-&gt;o3   h1-&gt;o3   h2-&gt;o3   h3-&gt;o3   h4-&gt;o3 
+  -67.71  -185.66   -74.37   528.55   -42.73 </code></pre>
 </div>
 </div>
 <p>size 隐藏层中的神经元数量</p>

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@@ -1,4 +1,4 @@
-% Created by tikzDevice version 0.12.6 on 2024-02-01 06:11:37
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@@ -1,4 +1,4 @@
-% Created by tikzDevice version 0.12.6 on 2024-02-01 06:11:50
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diff --git a/gaussian-processes-regression_files/figure-pdf/fig-rongelap-poisson-dens-1.pdf b/gaussian-processes-regression_files/figure-pdf/fig-rongelap-poisson-dens-1.pdf
diff --git a/gaussian-processes-regression_files/figure-pdf/fig-rongelap-poisson-fitted-1.pdf b/gaussian-processes-regression_files/figure-pdf/fig-rongelap-poisson-fitted-1.pdf
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diff --git a/gaussian-processes-regression_files/figure-pdf/fig-rongelap-poisson-pred-1.pdf b/gaussian-processes-regression_files/figure-pdf/fig-rongelap-poisson-pred-1.pdf
diff --git a/gaussian-processes-regression_files/figure-pdf/fig-rongelap-poisson-trace-1.pdf b/gaussian-processes-regression_files/figure-pdf/fig-rongelap-poisson-trace-1.pdf
diff --git a/gaussian-processes-regression_files/figure-pdf/fig-trace-dens-1.pdf b/gaussian-processes-regression_files/figure-pdf/fig-trace-dens-1.pdf
diff --git a/gaussian-processes-regression_files/figure-pdf/fig-trace-dens-2.pdf b/gaussian-processes-regression_files/figure-pdf/fig-trace-dens-2.pdf
diff --git a/gaussian-processes-regression_files/figure-pdf/fig-trivar-bayes-1.pdf b/gaussian-processes-regression_files/figure-pdf/fig-trivar-bayes-1.pdf
diff --git a/generalized-additive-models.html b/generalized-additive-models.html
@@ -1354,14 +1354,14 @@ <h1 class="title"><span id="sec-generalized-additive-models" class="quarto-secti
 <span id="cb58-2"><a href="#cb58-2"></a>res<span class="sc">$</span>summary.fixed</span></code><button title="复制到剪贴板" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>#&gt;        mean         sd 0.025quant 0.5quant 0.975quant     mode          kld
-#&gt; b0 1.828027 0.06147354   1.706422 1.828284   1.948169 1.828279 1.782545e-08</code></pre>
+#&gt; b0 1.828027 0.06147357   1.706422 1.828283   1.948169 1.828279 1.782558e-08</code></pre>
 </div>
 <div class="sourceCode cell-code" id="cb60"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb60-1"><a href="#cb60-1"></a><span class="co"># 超参数</span></span>
 <span id="cb60-2"><a href="#cb60-2"></a>res<span class="sc">$</span>summary.hyperpar</span></code><button title="复制到剪贴板" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
-<pre><code>#&gt;                   mean        sd 0.025quant  0.5quant 0.975quant      mode
-#&gt; Theta1 for s  2.000684 0.0623506   1.876512  2.001169   2.122006  2.003209
-#&gt; Theta2 for s -4.851258 0.1297349  -5.105062 -4.851807  -4.594250 -4.854094</code></pre>
+<pre><code>#&gt;                   mean         sd 0.025quant  0.5quant 0.975quant      mode
+#&gt; Theta1 for s  2.000684 0.06235036   1.876512  2.001169   2.122006  2.003209
+#&gt; Theta2 for s -4.851258 0.12973214  -5.105057 -4.851807  -4.594256 -4.854095</code></pre>
 </div>
 </div>
 <p>提取预测数据，并整理数据。</p>

diff --git a/generalized-additive-models_files/figure-html/fig-mcycle-smooths-2.png b/generalized-additive-models_files/figure-html/fig-mcycle-smooths-2.png
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diff --git a/generalized-additive-models_files/figure-pdf/fig-city-hg-1.pdf b/generalized-additive-models_files/figure-pdf/fig-city-hg-1.pdf
diff --git a/generalized-additive-models_files/figure-pdf/fig-city-topo-1.pdf b/generalized-additive-models_files/figure-pdf/fig-city-topo-1.pdf
diff --git a/generalized-additive-models_files/figure-pdf/fig-mcycle-1.pdf b/generalized-additive-models_files/figure-pdf/fig-mcycle-1.pdf
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diff --git a/generalized-additive-models_files/figure-pdf/fig-mcycle-viz-1.pdf b/generalized-additive-models_files/figure-pdf/fig-mcycle-viz-1.pdf
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diff --git a/generalized-additive-models_files/figure-pdf/fig-rongelap-mgcv-gam-1.pdf b/generalized-additive-models_files/figure-pdf/fig-rongelap-mgcv-gam-1.pdf
diff --git a/generalized-additive-models_files/figure-pdf/fig-rongelap-mgcv-ginla-1.pdf b/generalized-additive-models_files/figure-pdf/fig-rongelap-mgcv-ginla-1.pdf
diff --git a/generalized-additive-models_files/figure-pdf/fig-rongelap-scatter3d-1.pdf b/generalized-additive-models_files/figure-pdf/fig-rongelap-scatter3d-1.pdf
diff --git a/generalized-linear-models_files/figure-pdf/fig-cdplot-orings-1.pdf b/generalized-linear-models_files/figure-pdf/fig-cdplot-orings-1.pdf
diff --git a/generalized-linear-models_files/figure-pdf/fig-density-filled-1.pdf b/generalized-linear-models_files/figure-pdf/fig-density-filled-1.pdf
diff --git a/generalized-linear-models_files/figure-pdf/fig-esoph-1.pdf b/generalized-linear-models_files/figure-pdf/fig-esoph-1.pdf
diff --git a/generalized-linear-models_files/figure-pdf/fig-stan-areas-1.pdf b/generalized-linear-models_files/figure-pdf/fig-stan-areas-1.pdf
diff --git a/generalized-linear-models_files/figure-pdf/fig-stan-dens-1.pdf b/generalized-linear-models_files/figure-pdf/fig-stan-dens-1.pdf
diff --git a/generalized-linear-models_files/figure-pdf/fig-stan-hex-1.pdf b/generalized-linear-models_files/figure-pdf/fig-stan-hex-1.pdf
diff --git a/generalized-linear-models_files/figure-pdf/fig-stan-nuts-1.pdf b/generalized-linear-models_files/figure-pdf/fig-stan-nuts-1.pdf
diff --git a/generalized-linear-models_files/figure-pdf/fig-stan-ppcheck-dens-1.pdf b/generalized-linear-models_files/figure-pdf/fig-stan-ppcheck-dens-1.pdf
diff --git a/generalized-linear-models_files/figure-pdf/fig-stan-ppcheck-intervals-1.pdf b/generalized-linear-models_files/figure-pdf/fig-stan-ppcheck-intervals-1.pdf
diff --git a/generalized-linear-models_files/figure-pdf/fig-stan-ridges-1.pdf b/generalized-linear-models_files/figure-pdf/fig-stan-ridges-1.pdf
diff --git a/generalized-linear-models_files/figure-pdf/fig-stan-scatter-1.pdf b/generalized-linear-models_files/figure-pdf/fig-stan-scatter-1.pdf
diff --git a/generalized-linear-models_files/figure-pdf/fig-stan-trace-1.pdf b/generalized-linear-models_files/figure-pdf/fig-stan-trace-1.pdf
diff --git a/hierarchical-normal-models.html b/hierarchical-normal-models.html
@@ -790,7 +790,7 @@ <h1 class="title"><span id="sec-hierarchical-normal-models" class="quarto-sectio
 #&gt; theta[8]   8.45    0.19 8.00  -7.45   4.02   8.13  12.69  26.56  1728    1
 #&gt; lp__     -50.67    0.11 2.64 -56.69 -52.25 -50.40 -48.78 -46.34   584    1
 #&gt; 
-#&gt; Samples were drawn using NUTS(diag_e) at Thu Feb  1 06:32:23 2024.
+#&gt; Samples were drawn using NUTS(diag_e) at Mon Feb  5 05:18:50 2024.
 #&gt; For each parameter, n_eff is a crude measure of effective sample size,
 #&gt; and Rhat is the potential scale reduction factor on split chains (at 
 #&gt; convergence, Rhat=1).</code></pre>
@@ -807,7 +807,7 @@ <h1 class="title"><span id="sec-hierarchical-normal-models" class="quarto-sectio
 #&gt;     mean se_mean   sd 2.5% 97.5% n_eff Rhat
 #&gt; tau 6.43     0.2 5.35 0.22 19.73   733    1
 #&gt; 
-#&gt; Samples were drawn using NUTS(diag_e) at Thu Feb  1 06:32:23 2024.
+#&gt; Samples were drawn using NUTS(diag_e) at Mon Feb  5 05:18:50 2024.
 #&gt; For each parameter, n_eff is a crude measure of effective sample size,
 #&gt; and Rhat is the potential scale reduction factor on split chains (at 
 #&gt; convergence, Rhat=1).</code></pre>
@@ -825,21 +825,21 @@ <h1 class="title"><span id="sec-hierarchical-normal-models" class="quarto-sectio
 <div class="sourceCode cell-code" id="cb8"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1"></a><span class="fu">str</span>(eight_schools_sim)</span></code><button title="复制到剪贴板" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>#&gt; List of 5
-#&gt;  $ mu   : num [1:2000(1d)] 10.9 7.32 11.08 6.01 7.17 ...
+#&gt;  $ mu   : num [1:2000(1d)] 4.379 8.171 12.021 -0.284 7.584 ...
 #&gt;   ..- attr(*, "dimnames")=List of 1
 #&gt;   .. ..$ iterations: NULL
-#&gt;  $ tau  : num [1:2000(1d)] 0.0611 13.9378 0.2101 10.2247 8.0898 ...
+#&gt;  $ tau  : num [1:2000(1d)] 14.523 0.742 13.614 14.05 10.707 ...
 #&gt;   ..- attr(*, "dimnames")=List of 1
 #&gt;   .. ..$ iterations: NULL
-#&gt;  $ eta  : num [1:2000, 1:8] -0.0263 1.2116 0.6171 0.4923 1.2238 ...
+#&gt;  $ eta  : num [1:2000, 1:8] 0.8887 1.7902 -0.3404 1.1472 0.0448 ...
 #&gt;   ..- attr(*, "dimnames")=List of 2
 #&gt;   .. ..$ iterations: NULL
 #&gt;   .. ..$           : NULL
-#&gt;  $ theta: num [1:2000, 1:8] 10.9 24.2 11.2 11 17.1 ...
+#&gt;  $ theta: num [1:2000, 1:8] 17.29 9.5 7.39 15.83 8.06 ...
 #&gt;   ..- attr(*, "dimnames")=List of 2
 #&gt;   .. ..$ iterations: NULL
 #&gt;   .. ..$           : NULL
-#&gt;  $ lp__ : num [1:2000(1d)] -54 -46.3 -54 -48.5 -51 ...
+#&gt;  $ lp__ : num [1:2000(1d)] -48.4 -51.8 -49.8 -46.1 -49 ...
 #&gt;   ..- attr(*, "dimnames")=List of 1
 #&gt;   .. ..$ iterations: NULL</code></pre>
 </div>
@@ -2267,7 +2267,7 @@ <h1 class="title"><span id="sec-hierarchical-normal-models" class="quarto-sectio
 #&gt; alpha0       106.3     0.1  3.6   99.5  103.9  106.4  108.8  113.2  1965    1
 #&gt; lp__        -438.0     0.3  7.0 -452.8 -442.6 -437.4 -433.2 -425.4   558    1
 #&gt; 
-#&gt; Samples were drawn using NUTS(diag_e) at Thu Feb  1 06:34:04 2024.
+#&gt; Samples were drawn using NUTS(diag_e) at Mon Feb  5 05:20:20 2024.
 #&gt; For each parameter, n_eff is a crude measure of effective sample size,
 #&gt; and Rhat is the potential scale reduction factor on split chains (at 
 #&gt; convergence, Rhat=1).</code></pre>
@@ -2679,11 +2679,11 @@ <h1 class="title"><span id="sec-hierarchical-normal-models" class="quarto-sectio
 #&gt; 
 #&gt; Model hyperparameters:
 #&gt;                                            mean     sd 0.025quant 0.5quant
-#&gt; Precision for the Gaussian observations 2414.80 311.28    1852.68  2397.51
-#&gt; Precision for rats                       888.43 244.91     495.43   858.84
+#&gt; Precision for the Gaussian observations 2414.67 311.30    1852.63  2397.34
+#&gt; Precision for rats                       888.04 244.24     494.56   859.03
 #&gt;                                         0.975quant    mode
-#&gt; Precision for the Gaussian observations    3076.02 2368.15
-#&gt; Precision for rats                         1451.76  804.50
+#&gt; Precision for the Gaussian observations    3076.07 2367.82
+#&gt; Precision for rats                         1448.25  806.15
 #&gt; 
 #&gt;  is computed</code></pre>
 </div>
@@ -2709,13 +2709,13 @@ <h1 class="title"><span id="sec-hierarchical-normal-models" class="quarto-sectio
 #&gt; 
 #&gt; Model hyperparameters:
 #&gt;                                             mean      sd 0.025quant 0.5quant
-#&gt; Precision for the Gaussian observations 4522.632 666.547   3334.879 4480.318
+#&gt; Precision for the Gaussian observations 4522.633 666.549   3334.882 4480.317
 #&gt; Precision for rats (component 1)          32.097   7.956     18.939   31.260
 #&gt; Precision for rats (component 2)          33.234   8.227     19.603   32.377
 #&gt; Rho1:2 for rats                           -0.001   0.172     -0.335   -0.001
 #&gt;                                         0.975quant     mode
-#&gt; Precision for the Gaussian observations   5952.967 4407.841
-#&gt; Precision for rats (component 1)            50.049   29.769
+#&gt; Precision for the Gaussian observations   5952.976 4407.835
+#&gt; Precision for rats (component 1)            50.049   29.770
 #&gt; Precision for rats (component 2)            51.773   30.859
 #&gt; Rho1:2 for rats                              0.334   -0.001
 #&gt; 

diff --git a/hierarchical-normal-models_files/figure-html/fig-8schools-funnels-1.pdf b/hierarchical-normal-models_files/figure-html/fig-8schools-funnels-1.pdf
diff --git a/hierarchical-normal-models_files/figure-html/fig-8schools-funnels-1.tex b/hierarchical-normal-models_files/figure-html/fig-8schools-funnels-1.tex
@@ -1,4 +1,4 @@
-% Created by tikzDevice version 0.12.6 on 2024-02-01 06:33:07
+% Created by tikzDevice version 0.12.6 on 2024-02-05 05:19:25
 % !TEX encoding = UTF-8 Unicode
 \documentclass[tikz]{standalone}
 

diff --git a/hierarchical-normal-models_files/figure-pdf/fig-8schools-bayesplot-posterior-1.pdf b/hierarchical-normal-models_files/figure-pdf/fig-8schools-bayesplot-posterior-1.pdf
diff --git a/hierarchical-normal-models_files/figure-pdf/fig-8schools-funnels-1.pdf b/hierarchical-normal-models_files/figure-pdf/fig-8schools-funnels-1.pdf
diff --git a/hierarchical-normal-models_files/figure-pdf/fig-8schools-funnels-1.png b/hierarchical-normal-models_files/figure-pdf/fig-8schools-funnels-1.png
diff --git a/hierarchical-normal-models_files/figure-pdf/fig-8schools-funnels-1.tex b/hierarchical-normal-models_files/figure-pdf/fig-8schools-funnels-1.tex
@@ -1,4 +1,4 @@
-% Created by tikzDevice version 0.12.6 on 2024-02-01 06:36:08
+% Created by tikzDevice version 0.12.6 on 2024-02-05 05:22:18
 % !TEX encoding = UTF-8 Unicode
 \documentclass[tikz]{standalone}
 

diff --git a/hierarchical-normal-models_files/figure-pdf/fig-8schools-mu-base-1.pdf b/hierarchical-normal-models_files/figure-pdf/fig-8schools-mu-base-1.pdf
diff --git a/hierarchical-normal-models_files/figure-pdf/fig-8schools-mu-ggplot2-1.pdf b/hierarchical-normal-models_files/figure-pdf/fig-8schools-mu-ggplot2-1.pdf
diff --git a/hierarchical-normal-models_files/figure-pdf/fig-8schools-rstan-posterior-1.pdf b/hierarchical-normal-models_files/figure-pdf/fig-8schools-rstan-posterior-1.pdf
diff --git a/hierarchical-normal-models_files/figure-pdf/fig-rats-1.pdf b/hierarchical-normal-models_files/figure-pdf/fig-rats-1.pdf
diff --git a/hierarchical-normal-models_files/figure-pdf/fig-rats-2.pdf b/hierarchical-normal-models_files/figure-pdf/fig-rats-2.pdf
diff --git a/hierarchical-normal-models_files/figure-pdf/fig-rats-alpha-1.pdf b/hierarchical-normal-models_files/figure-pdf/fig-rats-alpha-1.pdf
diff --git a/hierarchical-normal-models_files/figure-pdf/fig-rats-beta-1.pdf b/hierarchical-normal-models_files/figure-pdf/fig-rats-beta-1.pdf
diff --git a/hierarchical-normal-models_files/figure-pdf/fig-rats-lm-1.pdf b/hierarchical-normal-models_files/figure-pdf/fig-rats-lm-1.pdf
diff --git a/index.html b/index.html
@@ -601,7 +601,7 @@ <h1 class="title">R 语言数据分析实战</h1>
     <div>
     <div class="quarto-title-meta-heading">发布日期</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">2024年2月1日</p>
+      <p class="date">2024年2月5日</p>
     </div>
   </div>
 
@@ -628,7 +628,7 @@ <h1 class="title">R 语言数据分析实战</h1>
 <p>本书初稿是在 RStudio IDE 内使用 <a href="https://quarto.org/">Quarto</a> 编辑的，Quarto 是继<a href="https://rmarkdown.rstudio.com/">R Markdown</a>之后，一个新的开源的科学和技术发布系统，它基于 <a href="https://pandoc.org/">Pandoc</a>支持输出多种格式的书稿，比如 HTML 网页、EPUB 电子书、DOCX 文档和 PDF 便携式文档等。Quarto 吸收了过去 10 年 R Markdown 取得的经验和教训，在书籍写作、创建博客、制作简历和幻灯片等系列场景中支持更加统一的使用语法，一份源文档输出多种格式，使文档内容在不同场景中的迁移成本更低。了解更多 Quarto 特性，请访问 <a href="https://quarto.org/" class="uri">https://quarto.org/</a>。</p>
 <p>书中的代码字体采用美观的 <a href="https://ctan.org/pkg/sourcecodepro">Source Code Pro</a> 字体， 为方便跨操作系统编译书籍电子版，正文的中文字体采用开源的 <a href="https://ctan.org/pkg/fandol">fandol</a> 字体。此外，考虑到美观性，本书图形使用了 Noto 系列中英文字体，它们来自 <a href="https://fonts.google.com/">Google Fonts 字体库</a>，分别是 Noto Sans 无衬线英文字体和 Noto Serif SC 宋体中文字体。</p>
 <p>书中 R 包名以粗体表示，如 <strong>knitr</strong> 包，函数名以等宽体表示，如 <code><a href="https://rdrr.io/r/graphics/plot.default.html">plot()</a></code>，函数的参数名同理。代码块内注释用 <code>#</code> 表示，运行结果每一行开头以 <code>#&gt;</code> 标记。本书写作过程中，依赖 <strong>knitr</strong> <span class="citation" data-cites="xie2015">(<a href="references.html#ref-xie2015" role="doc-biblioref">Xie 2015</a>)</span>、<strong>ggplot2</strong> <span class="citation" data-cites="Hadley2016">(<a href="references.html#ref-Hadley2016" role="doc-biblioref">Wickham 2016</a>)</span> 和 <strong>lattice</strong> <span class="citation" data-cites="Deepayan2008">(<a href="references.html#ref-Deepayan2008" role="doc-biblioref">Sarkar 2008</a>)</span> 等众多 R 包。考虑到要同时支持 DOCX、EPUB、PDF 和 HTML 四种书籍格式，书中使用 <strong>knitr</strong> 包和 <a href="https://github.com/rstudio/gt"><strong>gt</strong></a> 包制作静态的表格。</p>
-<p>为方便测试贡献者提供的 PR，本书托管在 Github 上，同时启用 Github Action 服务，为书籍自定义了一个可复现全书内容的运行环境，包括 R 软件、扩展包和系统软件依赖，详见仓库中的 DESCRIPTION 文件。你现在看到的是在线编译版本，使用 Quarto 1.4.549，最新一次编译时间是 2024-02-01 14:00:55。</p>
+<p>为方便测试贡献者提供的 PR，本书托管在 Github 上，同时启用 Github Action 服务，为书籍自定义了一个可复现全书内容的运行环境，包括 R 软件、扩展包和系统软件依赖，详见仓库中的 DESCRIPTION 文件。你现在看到的是在线编译版本，使用 Quarto 1.4.549，最新一次编译时间是 2024-02-05 12:50:52。</p>
 <div class="cell">
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1"></a>xfun<span class="sc">::</span><span class="fu">session_info</span>(<span class="at">packages =</span> <span class="fu">c</span>(</span>
 <span id="cb1-2"><a href="#cb1-2"></a>  <span class="st">"ggplot2"</span>, <span class="st">"gganimate"</span>, <span class="st">"ggrepel"</span>, <span class="st">"ggdensity"</span>,</span>
@@ -653,18 +653,18 @@ <h1 class="title">R 语言数据分析实战</h1>
 #&gt;   gganimate_1.0.8   ggbeeswarm_0.7.2  ggdensity_1.0.0   ggeffects_1.3.4  
 #&gt;   ggforce_0.4.1     ggnewscale_0.4.9  ggplot2_3.5.0     ggraph_2.1.0.9000
 #&gt;   ggrepel_0.9.5     ggridges_0.5.6    ggsignif_0.6.4    gifski_1.12.0.2  
-#&gt;   glmnet_4.1.8      gt_0.10.1         httr_1.4.7        igraph_2.0.1     
+#&gt;   glmnet_4.1.8      gt_0.10.1         httr_1.4.7        igraph_2.0.1.1   
 #&gt;   INLA_23.9.9       knitr_1.45        lattice_0.21.9    lme4_1.1.35.1    
 #&gt;   magick_2.8.2      mgcv_1.9.0        patchwork_1.2.0   plotly_4.10.4    
-#&gt;   purrr_1.0.2       rmarkdown_2.25    rsconnect_1.2.0   sf_1.0.15        
+#&gt;   purrr_1.0.2       rmarkdown_2.25    rsconnect_1.2.1   sf_1.0.15        
 #&gt;   shiny_1.8.0       showtext_0.9.6    spaMM_4.4.16      stars_0.6.4      
 #&gt;   terra_1.7.71      tidygraph_1.3.1   tidyr_1.3.1       tinytex_0.49     
 #&gt;   xgboost_1.7.7.1  
 #&gt; 
 #&gt; Pandoc version: 3.1.11
 #&gt; 
 #&gt; LaTeX version used: 
-#&gt;   TeX Live 2023 (TinyTeX) with tlmgr 2024-01-13</code></pre>
+#&gt;   TeX Live 2023 (TinyTeX) with tlmgr 2024-01-31</code></pre>
 </div>
 </div>