tag:crantastic.org,2005:/authors/941Latest activity for Xuefei Mi2016-10-09T16:21:40Zcrantastic.orgtag:crantastic.org,2005:TimelineEvent/556732016-10-09T16:21:40Z2016-10-09T16:21:40Zselectiongain was upgraded to version 2.0.591<a href="/packages/selectiongain">selectiongain</a> was <span class="action">upgraded</span> to version <a href="/packages/selectiongain/versions/53441">2.0.591</a><br /><h3>Package description:</h3><p>Multi-stage selection is practiced in numerous fields of life and social sciences and particularly in breeding. A special characteristic of multi-stage selection is that candidates are evaluated in successive stages with increasing intensity and effort, and only a fraction of the superior candidates is selected and promoted to the next stage. For the optimum design of such selection programs, the selection gain plays a crucial role. It can be calculated by integration of a truncated multivariate normal (MVN) distribution. While mathematical formulas for calculating the selection gain and the variance among selected candidates were developed long time ago, solutions for numerical calculation were not available. This package can also be used for optimizing multi-stage selection programs for a given total budget and different costs of evaluating the candidates in each stage.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/495062016-03-14T12:21:23Z2016-03-14T12:21:23Zselectiongain was upgraded to version 2.0.50.1<a href="/packages/selectiongain">selectiongain</a> was <span class="action">upgraded</span> to version <a href="/packages/selectiongain/versions/47868">2.0.50.1</a><br /><h3>Package description:</h3><p>Multi-stage selection is practiced in numerous fields of life and social sciences and particularly in breeding. A special characteristic of multi-stage selection is that candidates are evaluated in successive stages with increasing intensity and effort, and only a fraction of the superior candidates is selected and promoted to the next stage. For the optimum design of such selection programs, the selection gain plays a crucial role. It can be calculated by integration of a truncated multivariate normal (MVN) distribution. While mathematical formulas for calculating the selection gain and the variance among selected candidates were developed long time ago, solutions for numerical calculation were not available. This package can also be used for optimizing multi-stage selection programs for a given total budget and different costs of evaluating the candidates in each stage.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/380712014-12-29T13:52:18Z2014-12-29T13:52:18Zselectiongain was upgraded to version 2.0.40<a href="/packages/selectiongain">selectiongain</a> was <span class="action">upgraded</span> to version <a href="/packages/selectiongain/versions/36373">2.0.40</a><br /><h3>Package description:</h3><p>Multi-stage selection is practiced in numerous fields of life and social sciences and particularly in breeding. A special characteristic of multi-stage selection is that candidates are evaluated in successive stages with increasing intensity and effort, and only a fraction of the superior candidates is selected and promoted to the next stage. For the optimum design of such selection programs, the selection gain plays a crucial role. It can be calculated by integration of a truncated multivariate normal (MVN) distribution. While mathematical formulas for calculating the selection gain and the variance among selected candidates were developed long time ago, solutions for numerical calculation were not available. This package can also be used for optimizing multi-stage selection programs for a given total budget and different costs of evaluating the candidates in each stage.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/323042013-05-02T06:31:12Z2013-05-02T06:31:12Zselectiongain was upgraded to version 2.0.6<a href="/packages/selectiongain">selectiongain</a> was <span class="action">upgraded</span> to version <a href="/packages/selectiongain/versions/27494">2.0.6</a><br /><h3>Package description:</h3><p>A tool for efficient calculation and optimization of the expected gain from multi-stage selection</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/236732012-07-12T13:51:27Z2012-07-12T13:51:27Zselectiongain was upgraded to version 2.0.1<a href="/packages/selectiongain">selectiongain</a> was <span class="action">upgraded</span> to version <a href="/packages/selectiongain/versions/19787">2.0.1</a><br /><h3>Package description:</h3><p>Multi-stage selection is practised in numerous fields of the life and social sciences and particularly in breeding. A special characteristic of multi-stage selection is that candidates are evaluated in successive stages with increasing intensity and efforts, and only a fraction of the superior candidates is selected and promoted to the next stage. For the optimum design of such selection programs, the selection gain plays a crucial role. It can be calculated by integration of a truncated multivariate normal (MVN) distribution. While mathematical formulas for calculating the selection gain and the variance among selected candidates, were developed long time ago, solutions for numerical calculation were not available. This package can also be used for optimizing multi-stage selection programs for a given total budget and different costs of evaluating the candidates in each stage.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/181792011-12-15T19:30:01Z2011-12-15T19:30:01Zselectiongain was upgraded to version 1.2.1.0<a href="/packages/selectiongain">selectiongain</a> was <span class="action">upgraded</span> to version <a href="/packages/selectiongain/versions/16225">1.2.1.0</a><br /><h3>Package description:</h3><p>This package calculate the gain from selection, which is described by Cochran (1951). For one-stage selection the gain is defined as \eqn{\Delta G (y) = i \rho_{y} \rho_{1}}, where \eqn{i} is the selection intensity, \eqn{\rho_{1}} is the correlation between the true breeding value and the selection index \eqn{y} (Utz1969). The numerical calculation is based on Tallis' algorithm (Tallis1961).</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/178562011-11-28T14:10:38Z2011-11-28T14:10:38Zselectiongain was upgraded to version 1.1.8.0<a href="/packages/selectiongain">selectiongain</a> was <span class="action">upgraded</span> to version <a href="/packages/selectiongain/versions/15975">1.1.8.0</a><br /><h3>Package description:</h3><p>This package calculate the gain from selection, which is described by Cochran (1951). For one-stage selection the gain is defined as \eqn{\Delta G (y) = i \rho_{y} \rho_{1}}, where \eqn{i} is the selection intensity, \eqn{\rho_{1}} is the correlation between the true breeding value and the selection index \eqn{y} (Utz1969). The numerical calculation is based on Tallis' algorithm (Tallis1961).</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/168882011-10-23T10:31:02Z2011-10-23T10:31:02Zselectiongain was upgraded to version 1.1.7.0<a href="/packages/selectiongain">selectiongain</a> was <span class="action">upgraded</span> to version <a href="/packages/selectiongain/versions/15058">1.1.7.0</a><br /><h3>Package description:</h3><p>This package calculate the gain from selection, which is described by Cochran (1951). For one-stage selection the gain is defined as \eqn{\Delta G (y) = i \rho_{y} \rho_{1}}, where \eqn{i} is the selection intensity, \eqn{\rho_{1}} is the correlation between the true breeding value and the selection index \eqn{y} (Utz1969). The numerical calculation is based on Tallis' algorithm (Tallis1961).</p>crantastic.org