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-<tr data-index="0" data-listing-date-sort="1733266800000" data-listing-file-modified-sort="1733419692911" data-listing-date-modified-sort="NaN" data-listing-reading-time-sort="5" data-listing-word-count-sort="837" data-listing-title-sort="Heteroscedastic Linear Mixed Models with
+<tr data-index="0" data-listing-date-sort="1733266800000" data-listing-file-modified-sort="1733419754799" data-listing-date-modified-sort="NaN" data-listing-reading-time-sort="5" data-listing-word-count-sort="847" data-listing-title-sort="Heteroscedastic Linear Mixed Models with
 `glmmTMB(dispformula=)` and nlme::lme(weights=varIdent(form=))" data-listing-filename-sort="index.qmd">
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 <span class="listing-date">Dec 4, 2024</span>

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 <p><strong>Example</strong>:</p>
 <pre><code>glmmTMB(y ~ trt + (1|id), data = D, dispformula = ~trt)
-nlme::lme(y ~ trt, random = ~1|id, weights = varIdent(form = ~1|trt))</code></pre>
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+nlme::lme(y ~ trt, random = ~1|id, 
+          weights = varIdent(form = ~1|trt))</code></pre>
 <p><strong>Note</strong>: Before modelling heteroscedastic LMM try:</p>
 <ul>
 <li>fixing heteroscedasticity by transforming the response variable (log, sqrt, etc.)</li>

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