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<li>The usual one-sided t-test for significant difference from zero is nevertheless shown based on estimators for the untransformed parameters.</li>
<li>Summary and plotting functions. The <code>summary</code> of an <code>mkinfit</code> object is in fact a full report that should give enough information to be able to approximately reproduce the fit with other tools.</li>
<li>The chi-squared error level as defined in the FOCUS kinetics guidance (see below) is calculated for each observed variable.</li>
-<li>Iteratively reweighted least squares fitting is implemented in a similar way as in KinGUII and CAKE (see below). Simply add the argument <code>reweight.method = &quot;obs&quot;</code> to your call to <code>mkinfit</code> and a separate variance componenent for each of the observed variables will be optimised in a second stage after the primary optimisation algorithm has converged.</li>
-<li>Iterative reweighting is also possible using a two-component error model for analytical data similar to the one proposed by <a href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">Rocke and Lorenzato</a> using the argument <code>reweight.method = &quot;tc&quot;</code>.</li>
<li>When a metabolite decline phase is not described well by SFO kinetics, SFORB kinetics can be used for the metabolite.</li>
+<li>Three different error models can be selected using the argument <code>error_model</code> to the <a href="https://pkgdown.jrwb.de/mkin/reference/mkinfit.html"><code>mkinfit</code></a> function.</li>
+<li>Iteratively reweighted least squares fitting is now obsolete, and the variance by variable error model should now be specified as <code>error_model = &quot;obs&quot;</code>.</li>
+<li>A two-component error model similar to the one proposed by <a href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">Rocke and Lorenzato</a> can be selected using the argument <code>error_model = &quot;tc&quot;</code>.</li>
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