tag:crantastic.org,2005:/packages/nlmrtLatest activity for nlmrt2016-05-26T23:15:19Zcrantastic.orgtag:crantastic.org,2005:TimelineEvent/515712016-05-26T23:15:19Z2016-05-26T23:15:19Zcrantastic_production tagged nlmrt with Optimization<a href="/users/146">crantastic_production</a> <span class="action">tagged</span> <a href="/packages/nlmrt">nlmrt</a> with <a href="/task_views/Optimization">Optimization</a>crantastic_productiontag:crantastic.org,2005:TimelineEvent/478692016-03-04T23:12:01Z2016-03-04T23:12:01Znlmrt was upgraded to version 2016.3.2<a href="/packages/nlmrt">nlmrt</a> was <span class="action">upgraded</span> to version <a href="/packages/nlmrt/versions/47623">2016.3.2</a><br /><h3>Package description:</h3><p>Replacement for nls() tools for working with nonlinear least squares problems. The calling structure is similar to, but much simpler than, that of the nls() function. Moreover, where nls() specifically does NOT deal with small or zero residual problems, nlmrt is quite happy to solve them. It also attempts to be more robust in finding solutions, thereby avoiding 'singular gradient' messages that arise in the Gauss-Newton method within nls(). The Marquardt-Nash approach in nlmrt generally works more reliably to get a solution, though this may be one of a set of possibilities, and may also be statistically unsatisfactory. Added print and summary as of August 28, 2012.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/349872013-08-11T17:11:04Z2013-08-11T17:11:04Znlmrt was upgraded to version 2013-8.10<a href="/packages/nlmrt">nlmrt</a> was <span class="action">upgraded</span> to version <a href="/packages/nlmrt/versions/30005">2013-8.10</a><br /><h3>Package description:</h3><p>nlmrt provides tools for working with nonlinear least squares problems using a calling structure similar to, but much simpler than, that of the nls() function. Moreover, where nls() specifically does NOT deal with small or zero residual problems, nlmrt is quite happy to solve them. It also attempts to be more robust in finding solutions, thereby avoiding 'singular gradient' messages that arise in the Gauss-Newton method within nls(). The Marquardt-Nash approach in nlmrt generally works more reliably to get a solution, though this may be one of a set of possibilities, and may also be statistically unsatisfactory. Added print and summary as of August 28, 2012.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/277992012-12-17T09:52:08Z2012-12-17T09:52:08Znlmrt was upgraded to version 2012-12.16<a href="/packages/nlmrt">nlmrt</a> was <span class="action">upgraded</span> to version <a href="/packages/nlmrt/versions/23617">2012-12.16</a><br /><h3>Package description:</h3><p>nlmrt provides tools for working with nonlinear least squares problems using a calling structure similar to, but much simpler than, that of the nls() function. Moreover, where nls() specifically does NOT deal with small or zero residual problems, nlmrt is quite happy to solve them. It also attempts to be more robust in finding solutions, thereby avoiding 'singular gradient' messages that arise in the Gauss-Newton method within nls(). The Marquardt-Nash approach in nlmrt generally works more reliably to get a solution, though this may be one of a set of possibilities, and may also be statistically unsatisfactory. Added print and summary as of August 28, 2012.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/244872012-08-13T16:51:42Z2012-08-13T16:51:42Znlmrt was upgraded to version 2012-8.12<a href="/packages/nlmrt">nlmrt</a> was <span class="action">upgraded</span> to version <a href="/packages/nlmrt/versions/20529">2012-8.12</a><br /><h3>Package description:</h3><p>nlmrt provides tools for working with nonlinear least squares problems using a calling structure similar to, but much simpler than, that of the nls() function. Moreover, where nls() specifically does NOT deal with small or zero residual problems, nlmrt is quite happy to solve them. It also attempts to be more robust in finding solutions, thereby avoiding 'singular gradient' messages that arise in the Gauss-Newton method within nls(). The Marquardt-Nash approach in nlmrt generally works more reliably to get a solution, though this may be one of a set of possibilities, and may also be statistically unsatisfactory.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/244622012-08-12T15:31:32Z2012-08-12T15:31:32Znlmrt was released<a href="/packages/nlmrt">nlmrt</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>Replacement for nls() tools for working with nonlinear least squares problems. The calling structure is similar to, but much simpler than, that of the nls() function. Moreover, where nls() specifically does NOT deal with small or zero residual problems, nlmrt is quite happy to solve them. It also attempts to be more robust in finding solutions, thereby avoiding 'singular gradient' messages that arise in the Gauss-Newton method within nls(). The Marquardt-Nash approach in nlmrt generally works more reliably to get a solution, though this may be one of a set of possibilities, and may also be statistically unsatisfactory. Added print and summary as of August 28, 2012.</p>crantastic.org