docs: render equations via math code blocks

lib/node_modules/@stdlib/stats/incr/apcorr/README.md

@@ -28,10 +28,14 @@ The [Pearson product-moment correlation coefficient][pearson-correlation] betwee
 
 <!-- <equation class="equation" label="eq:pearson_correlation_coefficient" align="center" raw="\rho_{X,Y} = \frac{\operatorname{cov}(X,Y)}{\sigma_X \sigma_Y}" alt="Equation for the Pearson product-moment correlation coefficient."> -->
 
-<div class="equation" align="center" data-raw-text="\rho_{X,Y} = \frac{\operatorname{cov}(X,Y)}{\sigma_X \sigma_Y}" data-equation="eq:pearson_correlation_coefficient">
+```math
+\rho_{X,Y} = \frac{\operatorname{cov}(X,Y)}{\sigma_X \sigma_Y}
+```
+
+<!-- <div class="equation" align="center" data-raw-text="\rho_{X,Y} = \frac{\operatorname{cov}(X,Y)}{\sigma_X \sigma_Y}" data-equation="eq:pearson_correlation_coefficient">
     <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@80f96253bf726f33bc71d8eb68037ab203ae4cf9/lib/node_modules/@stdlib/stats/incr/apcorr/docs/img/equation_pearson_correlation_coefficient.svg" alt="Equation for the Pearson product-moment correlation coefficient.">
     <br>
-</div>
+</div> -->
 
 <!-- </equation> -->
 
@@ -41,10 +45,14 @@ For a sample of size `n`, the sample [Pearson product-moment correlation coeffic
 
 <!-- <equation class="equation" label="eq:sample_pearson_correlation_coefficient" align="center" raw="r = \frac{\sum_{i=0}^{n-1} (x_i - \bar{x})(y_i - \bar{y})}{\sqrt{\sum_{i=0}^{n-1} (x_i - \bar{x})^2} \sqrt{\sum_{i=0}^{n-1} (y_i - \bar{y})^2}}" alt="Equation for the sample Pearson product-moment correlation coefficient."> -->
 
-<div class="equation" align="center" data-raw-text="r = \frac{\sum_{i=0}^{n-1} (x_i - \bar{x})(y_i - \bar{y})}{\sqrt{\sum_{i=0}^{n-1} (x_i - \bar{x})^2} \sqrt{\sum_{i=0}^{n-1} (y_i - \bar{y})^2}}" data-equation="eq:sample_pearson_correlation_coefficient">
+```math
+r = \frac{\sum_{i=0}^{n-1} (x_i - \bar{x})(y_i - \bar{y})}{\sqrt{\sum_{i=0}^{n-1} (x_i - \bar{x})^2} \sqrt{\sum_{i=0}^{n-1} (y_i - \bar{y})^2}}
+```
+
+<!-- <div class="equation" align="center" data-raw-text="r = \frac{\sum_{i=0}^{n-1} (x_i - \bar{x})(y_i - \bar{y})}{\sqrt{\sum_{i=0}^{n-1} (x_i - \bar{x})^2} \sqrt{\sum_{i=0}^{n-1} (y_i - \bar{y})^2}}" data-equation="eq:sample_pearson_correlation_coefficient">
     <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@80f96253bf726f33bc71d8eb68037ab203ae4cf9/lib/node_modules/@stdlib/stats/incr/apcorr/docs/img/equation_sample_pearson_correlation_coefficient.svg" alt="Equation for the sample Pearson product-moment correlation coefficient.">
     <br>
-</div>
+</div> -->
 
 <!-- </equation> -->
 

lib/node_modules/@stdlib/stats/incr/covariance/README.md

@@ -28,21 +28,29 @@ For unknown population means, the [unbiased sample covariance][covariance] is de
 
 <!-- <equation class="equation" label="eq:unbiased_sample_covariance_unknown_means" align="center" raw="\operatorname{cov_n} = \frac{1}{n-1} \sum_{i=0}^{n-1} (x_i - \bar{x}_n)(y_i - \bar{y}_n)" alt="Equation for the unbiased sample covariance for unknown population means."> -->
 
-<div class="equation" align="center" data-raw-text="\operatorname{cov_n} = \frac{1}{n-1} \sum_{i=0}^{n-1} (x_i - \bar{x}_n)(y_i - \bar{y}_n)" data-equation="eq:unbiased_sample_covariance_unknown_means">
+```math
+\operatorname{cov_n} = \frac{1}{n-1} \sum_{i=0}^{n-1} (x_i - \bar{x}_n)(y_i - \bar{y}_n)
+```
+
+<!-- <div class="equation" align="center" data-raw-text="\operatorname{cov_n} = \frac{1}{n-1} \sum_{i=0}^{n-1} (x_i - \bar{x}_n)(y_i - \bar{y}_n)" data-equation="eq:unbiased_sample_covariance_unknown_means">
     <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@49d8cabda84033d55d7b8069f19ee3dd8b8d1496/lib/node_modules/@stdlib/stats/incr/covariance/docs/img/equation_unbiased_sample_covariance_unknown_means.svg" alt="Equation for the unbiased sample covariance for unknown population means.">
     <br>
-</div>
+</div> -->
 
 <!-- </equation> -->
 
 For known population means, the [unbiased sample covariance][covariance] is defined as
 
 <!-- <equation class="equation" label="eq:unbiased_sample_covariance_known_means" align="center" raw="\operatorname{cov_n} = \frac{1}{n} \sum_{i=0}^{n-1} (x_i - \mu_x)(y_i - \mu_y)" alt="Equation for the unbiased sample covariance for known population means."> -->
 
-<div class="equation" align="center" data-raw-text="\operatorname{cov_n} = \frac{1}{n} \sum_{i=0}^{n-1} (x_i - \mu_x)(y_i - \mu_y)" data-equation="eq:unbiased_sample_covariance_known_means">
+```math
+\operatorname{cov_n} = \frac{1}{n} \sum_{i=0}^{n-1} (x_i - \mu_x)(y_i - \mu_y)
+```
+
+<!-- <div class="equation" align="center" data-raw-text="\operatorname{cov_n} = \frac{1}{n} \sum_{i=0}^{n-1} (x_i - \mu_x)(y_i - \mu_y)" data-equation="eq:unbiased_sample_covariance_known_means">
     <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@27e2a43c70db648bb5bbc3fd0cdee050c25adc0b/lib/node_modules/@stdlib/stats/incr/covariance/docs/img/equation_unbiased_sample_covariance_known_means.svg" alt="Equation for the unbiased sample covariance for known population means.">
     <br>
-</div>
+</div> -->
 
 <!-- </equation> --> 
 

lib/node_modules/@stdlib/stats/incr/covmat/README.md

@@ -28,21 +28,29 @@ A [covariance matrix][covariance-matrix] is an M-by-M matrix whose elements spec
 
 <!-- <equation class="equation" label="eq:unbiased_sample_covariance_unknown_means" align="center" raw="\operatorname{cov_{jkn}} = \frac{1}{n-1} \sum_{i=0}^{n-1} (x_{ij} - \bar{x}_{jn})(x_{ik} - \bar{x}_{kn})" alt="Equation for the unbiased sample covariance for unknown population means."> -->
 
-<div class="equation" align="center" data-raw-text="\operatorname{cov_{jkn}} = \frac{1}{n-1} \sum_{i=0}^{n-1} (x_{ij} - \bar{x}_{jn})(x_{ik} - \bar{x}_{kn})" data-equation="eq:unbiased_sample_covariance_unknown_means">
+```math
+\operatorname{cov_{jkn}} = \frac{1}{n-1} \sum_{i=0}^{n-1} (x_{ij} - \bar{x}_{jn})(x_{ik} - \bar{x}_{kn})
+```
+
+<!-- <div class="equation" align="center" data-raw-text="\operatorname{cov_{jkn}} = \frac{1}{n-1} \sum_{i=0}^{n-1} (x_{ij} - \bar{x}_{jn})(x_{ik} - \bar{x}_{kn})" data-equation="eq:unbiased_sample_covariance_unknown_means">
     <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@49d8cabda84033d55d7b8069f19ee3dd8b8d1496/lib/node_modules/@stdlib/stats/incr/covmat/docs/img/equation_unbiased_sample_covariance_unknown_means.svg" alt="Equation for the unbiased sample covariance for unknown population means.">
     <br>
-</div>
+</div> -->
 
 <!-- </equation> -->
 
 For known population means, the [unbiased sample covariance][covariance-matrix] is defined as
 
 <!-- <equation class="equation" label="eq:unbiased_sample_covariance_known_means" align="center" raw="\operatorname{cov_{jkn}} = \frac{1}{n} \sum_{i=0}^{n-1} (x_{ij} - \mu_{j})(x_{ik} - \mu_{k})" alt="Equation for the unbiased sample covariance for known population means."> -->
 
-<div class="equation" align="center" data-raw-text="\operatorname{cov_{jkn}} = \frac{1}{n} \sum_{i=0}^{n-1} (x_{ij} - \mu_{j})(x_{ik} - \mu_{k})" data-equation="eq:unbiased_sample_covariance_known_means">
+```math
+\operatorname{cov_{jkn}} = \frac{1}{n} \sum_{i=0}^{n-1} (x_{ij} - \mu_{j})(x_{ik} - \mu_{k})
+```
+
+<!-- <div class="equation" align="center" data-raw-text="\operatorname{cov_{jkn}} = \frac{1}{n} \sum_{i=0}^{n-1} (x_{ij} - \mu_{j})(x_{ik} - \mu_{k})" data-equation="eq:unbiased_sample_covariance_known_means">
     <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@566f739b0d9a5b720546f84f74de841b8d5e0c54/lib/node_modules/@stdlib/stats/incr/covmat/docs/img/equation_unbiased_sample_covariance_known_means.svg" alt="Equation for the unbiased sample covariance for known population means.">
     <br>
-</div>
+</div> -->
 
 <!-- </equation> --> 
 

lib/node_modules/@stdlib/stats/incr/cv/README.md

@@ -28,32 +28,44 @@ The [corrected sample standard deviation][sample-stdev] is defined as
 
 <!-- <equation class="equation" label="eq:corrected_sample_standard_deviation" align="center" raw="s = \sqrt{\frac{1}{n-1} \sum_{i=0}^{n-1} ( x_i - \bar{x} )^2}" alt="Equation for the corrected sample standard deviation."> -->
 
-<div class="equation" align="center" data-raw-text="s = \sqrt{\frac{1}{n-1} \sum_{i=0}^{n-1} ( x_i - \bar{x} )^2}" data-equation="eq:corrected_sample_standard_deviation">
+```math
+s = \sqrt{\frac{1}{n-1} \sum_{i=0}^{n-1} ( x_i - \bar{x} )^2}
+```
+
+<!-- <div class="equation" align="center" data-raw-text="s = \sqrt{\frac{1}{n-1} \sum_{i=0}^{n-1} ( x_i - \bar{x} )^2}" data-equation="eq:corrected_sample_standard_deviation">
     <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@6dd7df93fd2c6e40fd0662844acf8b69b887dcec/lib/node_modules/@stdlib/stats/incr/cv/docs/img/equation_corrected_sample_standard_deviation.svg" alt="Equation for the corrected sample standard deviation.">
     <br>
-</div>
+</div> -->
 
 <!-- </equation> -->
 
 and the [arithmetic mean][arithmetic-mean] is defined as
 
 <!-- <equation class="equation" label="eq:arithmetic_mean" align="center" raw="\bar{x} = \frac{1}{n} \sum_{i=0}^{n-1} x_i" alt="Equation for the arithmetic mean."> -->
 
-<div class="equation" align="center" data-raw-text="\bar{x} = \frac{1}{n} \sum_{i=0}^{n-1} x_i" data-equation="eq:arithmetic_mean">
+```math
+\bar{x} = \frac{1}{n} \sum_{i=0}^{n-1} x_i
+```
+
+<!-- <div class="equation" align="center" data-raw-text="\bar{x} = \frac{1}{n} \sum_{i=0}^{n-1} x_i" data-equation="eq:arithmetic_mean">
     <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@6dd7df93fd2c6e40fd0662844acf8b69b887dcec/lib/node_modules/@stdlib/stats/incr/cv/docs/img/equation_arithmetic_mean.svg" alt="Equation for the arithmetic mean.">
     <br>
-</div>
+</div> -->
 
 <!-- </equation> -->
 
 The [coefficient of variation][coefficient-of-variation] (also known as **relative standard deviation**, RSD) is defined as
 
 <!-- <equation class="equation" label="eq:coefficient_of_variation" align="center" raw="c_v = \frac{s}{\bar{x}}" alt="Equation for the coefficient of variation (CV)."> -->
 
-<div class="equation" align="center" data-raw-text="c_v = \frac{s}{\bar{x}}" data-equation="eq:coefficient_of_variation">
+```math
+c_v = \frac{s}{\bar{x}}
+```
+
+<!-- <div class="equation" align="center" data-raw-text="c_v = \frac{s}{\bar{x}}" data-equation="eq:coefficient_of_variation">
     <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@6dd7df93fd2c6e40fd0662844acf8b69b887dcec/lib/node_modules/@stdlib/stats/incr/cv/docs/img/equation_coefficient_of_variation.svg" alt="Equation for the coefficient of variation (CV).">
     <br>
-</div>
+</div> -->
 
 <!-- </equation> -->
 

lib/node_modules/@stdlib/stats/incr/ewmean/README.md

@@ -28,10 +28,14 @@ An [exponentially weighted mean][moving-average] can be defined recursively as
 
 <!-- <equation class="equation" label="eq:exponentially_weighted_mean" align="center" raw="\mu_t = \begin{cases} x_0 & \textrm{if}\ t = 0 \\ \alpha x_t + (1-\alpha) \mu_{t-1} & \textrm{if}\ t > 0 \end{cases}" alt="Recursive definition for computing an exponentially weighted mean."> -->
 
-<div class="equation" align="center" data-raw-text="\mu_t = \begin{cases} x_0 &amp; \textrm{if}\ t = 0 \\ \alpha x_t + (1-\alpha) \mu_{t-1} &amp; \textrm{if}\ t &gt; 0 \end{cases}" data-equation="eq:exponentially_weighted_mean">
+```math
+\mu_t = \begin{cases} x_0 & \textrm{if}\ t = 0 \\ \alpha x_t + (1-\alpha) \mu_{t-1} & \textrm{if}\ t > 0 \end{cases}
+```
+
+<!-- <div class="equation" align="center" data-raw-text="\mu_t = \begin{cases} x_0 &amp; \textrm{if}\ t = 0 \\ \alpha x_t + (1-\alpha) \mu_{t-1} &amp; \textrm{if}\ t &gt; 0 \end{cases}" data-equation="eq:exponentially_weighted_mean">
     <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@1445ad5c454bc3c1a86bde2be87d6cec87781174/lib/node_modules/@stdlib/stats/incr/ewmean/docs/img/equation_exponentially_weighted_mean.svg" alt="Recursive definition for computing an exponentially weighted mean.">
     <br>
-</div>
+</div> -->
 
 <!-- </equation> -->
 

lib/node_modules/@stdlib/stats/incr/ewstdev/README.md

@@ -28,10 +28,14 @@ An [exponentially weighted variance][moving-average] can be defined recursively
 
 <!-- <equation class="equation" label="eq:exponentially_weighted_variance" align="center" raw="S_n = \begin{cases} 0 & \textrm{if}\ n = 0 \\ (1 - \alpha) (S_{n-1} + \alpha(x_n - \mu_{n-1})^2) & \textrm{if}\ n > 0 \end{cases}" alt="Recursive definition for computing an exponentially weighted variance."> -->
 
-<div class="equation" align="center" data-raw-text="S_n = \begin{cases} 0 &amp; \textrm{if}\ n = 0 \\ (1 - \alpha) (S_{n-1} + \alpha(x_n - \mu_{n-1})^2) &amp; \textrm{if}\ n &gt; 0 \end{cases}" data-equation="eq:exponentially_weighted_variance">
+```math
+S_n = \begin{cases} 0 & \textrm{if}\ n = 0 \\ (1 - \alpha) (S_{n-1} + \alpha(x_n - \mu_{n-1})^2) & \textrm{if}\ n > 0 \end{cases}
+```
+
+<!-- <div class="equation" align="center" data-raw-text="S_n = \begin{cases} 0 &amp; \textrm{if}\ n = 0 \\ (1 - \alpha) (S_{n-1} + \alpha(x_n - \mu_{n-1})^2) &amp; \textrm{if}\ n &gt; 0 \end{cases}" data-equation="eq:exponentially_weighted_variance">
     <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@b6bfc5be3086b5ddfeed2311afee7c9201fbdcbb/lib/node_modules/@stdlib/stats/incr/ewstdev/docs/img/equation_exponentially_weighted_variance.svg" alt="Recursive definition for computing an exponentially weighted variance.">
     <br>
-</div>
+</div> -->
 
 <!-- </equation> -->
 

lib/node_modules/@stdlib/stats/incr/ewvariance/README.md

@@ -28,10 +28,14 @@ An [exponentially weighted variance][moving-average] can be defined recursively
 
 <!-- <equation class="equation" label="eq:exponentially_weighted_variance" align="center" raw="S_n = \begin{cases} 0 & \textrm{if}\ n = 0 \\ (1 - \alpha) (S_{n-1} + \alpha(x_n - \mu_{n-1})^2) & \textrm{if}\ n > 0 \end{cases}" alt="Recursive definition for computing an exponentially weighted variance."> -->
 
-<div class="equation" align="center" data-raw-text="S_n = \begin{cases} 0 &amp; \textrm{if}\ n = 0 \\ (1 - \alpha) (S_{n-1} + \alpha(x_n - \mu_{n-1})^2) &amp; \textrm{if}\ n &gt; 0 \end{cases}" data-equation="eq:exponentially_weighted_variance">
+```math
+S_n = \begin{cases} 0 & \textrm{if}\ n = 0 \\ (1 - \alpha) (S_{n-1} + \alpha(x_n - \mu_{n-1})^2) & \textrm{if}\ n > 0 \end{cases}
+```
+
+<!-- <div class="equation" align="center" data-raw-text="S_n = \begin{cases} 0 &amp; \textrm{if}\ n = 0 \\ (1 - \alpha) (S_{n-1} + \alpha(x_n - \mu_{n-1})^2) &amp; \textrm{if}\ n &gt; 0 \end{cases}" data-equation="eq:exponentially_weighted_variance">
     <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@12be48682a7c25918504f886cbb80051c6ec8240/lib/node_modules/@stdlib/stats/incr/ewvariance/docs/img/equation_exponentially_weighted_variance.svg" alt="Recursive definition for computing an exponentially weighted variance.">
     <br>
-</div>
+</div> -->
 
 <!-- </equation> -->
 

lib/node_modules/@stdlib/stats/incr/gmean/README.md

@@ -28,10 +28,14 @@ The [geometric mean][geometric-mean] is defined as the nth root of a product of
 
 <!-- <equation class="equation" label="eq:geometric_mean" align="center" raw="\biggl( \prod_{i=0}^{n-1} \biggr)^{\frac{1}{n}} = \sqrt[n]{x_0 x_1 \cdots x_{n-1}}" alt="Equation for the geometric mean."> -->
 
-<div class="equation" align="center" data-raw-text="\biggl( \prod_{i=0}^{n-1} \biggr)^{\frac{1}{n}} = \sqrt[n]{x_0 x_1 \cdots x_{n-1}}" data-equation="eq:geometric_mean">
+```math
+\biggl( \prod_{i=0}^{n-1} \biggr)^{\frac{1}{n}} = \sqrt[n]{x_0 x_1 \cdots x_{n-1}}
+```
+
+<!-- <div class="equation" align="center" data-raw-text="\biggl( \prod_{i=0}^{n-1} \biggr)^{\frac{1}{n}} = \sqrt[n]{x_0 x_1 \cdots x_{n-1}}" data-equation="eq:geometric_mean">
     <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@cb802bd5cb07ef925c8a3ce9c34db0fb68040d12/lib/node_modules/@stdlib/stats/incr/gmean/docs/img/equation_geometric_mean.svg" alt="Equation for the geometric mean.">
     <br>
-</div>
+</div> -->
 
 <!-- </equation> -->