May 7, 2021 Software Open Access

Dan Warren; Russell Dinnage; Nicholas J. Matzke; Nicholas Huron; John Baumgartner

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    "description": "<p>I forgot to create GitHub releases for versions 1.0.2 and 1.0.3, but I'm going to try to be good about it from now on.  Here are the intervening changes:</p>\nENMTools 1.0.4\nBug Fixes\n<ul>\n<li>Raes and ter Steege-style tests were returning incorrect evaluate objects, although plots and p values were correct</li>\n<li>test.prop = \"block\" wasn't working correctly</li>\n</ul>\nEnhancements\n<ul>\n<li>Suppressing maxent startup messages by default</li>\n<li>Added support for bias layers to modeling functions</li>\n<li>Added check.env function to homogenize raster stacks so NAs propagate across layers</li>\n</ul>\nENMTools 1.0.3\nEnhancements\n<ul>\n<li>Brought up to date for new <strong>spatstat</strong> changes</li>\n<li>Removed <strong>ppmlasso</strong> until it is brought back on CRAN</li>\n</ul>\nENMTools 1.0.2\nEnhancements\n<ul>\n<li>Added a new general-purpose function for making background layers from point data.  It can do both circular buffers and buffered convex hulls, and can return points, a polygon, or a raster.  Converted the existing background buffer functions to just call this one, and will eventually deprecate the single-application functions.</li>\n<li>Added ability to select which corner you want for \"block\" validation.</li>\n<li>Added a function called <em>multi.variogram</em> which takes a raster stack, builds a variogram for each layer, and then plots the gamma for each variable as a function of distance, scaled by the maximum gama for that variable.  This allows users to get some idea of the level of spatial autocorrelation in each predictor variable.</li>\n</ul>", 
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