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(v1.4.0.9041) updates based on review

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</button>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9032</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9041</span>
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@ -90,14 +90,14 @@
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@ -304,7 +304,7 @@
<p>If <code>x</code> is a matrix with one row or column, or if <code>x</code> is a vector and <code>y</code> is not given, then a <em>goodness-of-fit test</em> is performed (<code>x</code> is treated as a one-dimensional contingency table). The entries of <code>x</code> must be non-negative integers. In this case, the hypothesis tested is whether the population probabilities equal those in <code>p</code>, or are all equal if <code>p</code> is not given.</p>
<p>If <code>x</code> is a matrix with at least two rows and columns, it is taken as a two-dimensional contingency table: the entries of <code>x</code> must be non-negative integers. Otherwise, <code>x</code> and <code>y</code> must be vectors or factors of the same length; cases with missing values are removed, the objects are coerced to factors, and the contingency table is computed from these. Then Pearson's chi-squared test is performed of the null hypothesis that the joint distribution of the cell counts in a 2-dimensional contingency table is the product of the row and column marginals.</p>
<p>The p-value is computed from the asymptotic chi-squared distribution of the test statistic.</p>
<p>In the contingency table case simulation is done by random sampling from the set of all contingency tables with given marginals, and works only if the marginals are strictly positive. Note that this is not the usual sampling situation assumed for a chi-squared test (like the <em>G</em>-test) but rather that for Fisher's exact test.</p>
<p>In the contingency table case simulation is done by random sampling from the set of all contingency tables with given marginals, and works only if the marginals are strictly positive. Note that this is not the usual sampling situation assumed for a chi-squared test (such as the <em>G</em>-test) but rather that for Fisher's exact test.</p>
<p>In the goodness-of-fit case simulation is done by random sampling from the discrete distribution specified by <code>p</code>, each sample being of size <code>n = sum(x)</code>. This simulation is done in <span style="R">R</span> and may be slow.</p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a><em>G</em>-test of goodness-of-fit (likelihood ratio test)</h3>