tag:crantastic.org,2005:/authors/3350Latest activity for Andrew Karl2019-04-23T05:02:31Zcrantastic.orgtag:crantastic.org,2005:TimelineEvent/875212019-04-23T05:02:32Z2019-04-23T05:02:32ZRealVAMS was upgraded to version 0.4-3<a href="/packages/RealVAMS">RealVAMS</a> was <span class="action">upgraded</span> to version <a href="/packages/RealVAMS/versions/83206">0.4-3</a><br /><h3>Package description:</h3><p>Fits a multivariate value-added model (VAM), see Broatch, Green, and Karl (2018) <doi:10.32614/RJ-2018-033> and Broatch and Lohr (2012) <doi:10.3102/1076998610396900>, with normally distributed test scores and a binary outcome indicator. A pseudo-likelihood approach, Wolfinger (1993) <doi:10.1080/00949659308811554>, is used for the estimation of this joint generalized linear mixed model. The inner loop of the pseudo-likelihood routine (estimation of a linear mixed model) occurs in the framework of the EM algorithm presented by Karl, Yang, and Lohr (2013) <DOI:10.1016/j.csda.2012.10.004>. This material is based upon work supported by the National Science Foundation under grants DRL-1336027 and DRL-1336265.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/789202018-08-24T05:02:07Z2018-08-24T05:02:07ZmvglmmRank was upgraded to version 1.2-2<a href="/packages/mvglmmRank">mvglmmRank</a> was <span class="action">upgraded</span> to version <a href="/packages/mvglmmRank/versions/75263">1.2-2</a><br /><h3>Package description:</h3><p>Maximum likelihood estimates are obtained via an EM algorithm with either a first-order or a fully exponential Laplace approximation.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/740442018-04-20T16:22:34Z2018-04-20T16:22:34ZRealVAMS was upgraded to version 0.4-1<a href="/packages/RealVAMS">RealVAMS</a> was <span class="action">upgraded</span> to version <a href="/packages/RealVAMS/versions/70680">0.4-1</a><br /><h3>Package description:</h3><p>Fits a multivariate value-added model (VAM), see Broatch and Lohr (2012) <doi:10.3102/1076998610396900>, with normally distributed test scores and a binary outcome indicator. A pseudo-likelihood approach, Wolfinger (1993) <doi:10.1080/00949659308811554>, is used for the estimation of this joint generalized linear mixed model. The inner loop of the pseudo-likelihood routine (estimation of a linear mixed model) occurs in the framework of the EM algorithm presented by Karl, Yang, and Lohr (2013) <DOI:10.1016/j.csda.2012.10.004>. This material is based upon work supported by the National Science Foundation under grants DRL-1336027 and DRL-1336265.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/739662018-04-19T21:02:03Z2018-04-19T21:02:03ZmvglmmRank was upgraded to version 1.2-1<a href="/packages/mvglmmRank">mvglmmRank</a> was <span class="action">upgraded</span> to version <a href="/packages/mvglmmRank/versions/70618">1.2-1</a><br /><h3>Package description:</h3><p>Maximum likelihood estimates are obtained via an EM algorithm with either a first-order or a fully exponential Laplace approximation.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/739282018-04-18T22:41:23Z2018-04-18T22:41:23ZGPvam was upgraded to version 3.0-5<a href="/packages/GPvam">GPvam</a> was <span class="action">upgraded</span> to version <a href="/packages/GPvam/versions/70580">3.0-5</a><br /><h3>Package description:</h3><p>An EM algorithm, Karl et al. (2013) <doi:10.1016/j.csda.2012.10.004>, is used to estimate the generalized, variable, and complete persistence models, Mariano et al. (2010) <doi:10.3102/1076998609346967>. These are multiple-membership linear mixed models with teachers modeled as "G-side" effects and students modeled with either "G-side" or "R-side" effects.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/654252017-08-14T04:42:08Z2017-08-14T04:42:08ZRealVAMS was upgraded to version 0.4-0<a href="/packages/RealVAMS">RealVAMS</a> was <span class="action">upgraded</span> to version <a href="/packages/RealVAMS/versions/62577">0.4-0</a><br /><h3>Package description:</h3><p>Fits a multivariate value-added model (VAM), see Broatch and Lohr (2012) <doi:10.3102/1076998610396900>, with normally distributed test scores and a binary outcome indicator. A pseudo-likelihood approach, Wolfinger (1993) <doi:10.1080/00949659308811554>, is used for the estimation of this joint generalized linear mixed model. The inner loop of the pseudo-likelihood routine (estimation of a linear mixed model) occurs in the framework of the EM algorithm presented by Karl, Yang, and Lohr (2013) <DOI:10.1016/j.csda.2012.10.004>. This material is based upon work supported by the National Science Foundation under grants DRL-1336027 and DRL-1336265.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/603302017-03-15T04:40:50Z2017-03-15T04:40:50ZGPvam was upgraded to version 3.0-4<a href="/packages/GPvam">GPvam</a> was <span class="action">upgraded</span> to version <a href="/packages/GPvam/versions/57750">3.0-4</a><br /><h3>Package description:</h3><p>An EM algorithm, Karl et al. (2013) <doi:10.1016/j.csda.2012.10.004>, is used to estimate the generalized, variable, and complete persistence models, Mariano et al. (2010) <doi:10.3102/1076998609346967>. These are multiple-membership linear mixed models with teachers modeled as "G-side" effects and students modeled with either "G-side" or "R-side" effects.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/603172017-03-14T16:21:26Z2017-03-14T16:21:26ZRealVAMS was upgraded to version 0.3-3<a href="/packages/RealVAMS">RealVAMS</a> was <span class="action">upgraded</span> to version <a href="/packages/RealVAMS/versions/57737">0.3-3</a><br /><h3>Package description:</h3><p>Fits a multivariate value-added model (VAM), see Broatch and Lohr (2012) <doi:10.3102/1076998610396900>, with normally distributed test scores and a binary outcome indicator. A pseudo-likelihood approach, Wolfinger (1993) <doi:10.1080/00949659308811554>, is used for the estimation of this joint generalized linear mixed model. This material is based upon work supported by the National Science Foundation under grants DRL-1336027 and DRL-1336265.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/451162015-11-13T08:11:44Z2015-11-13T08:11:44ZmvglmmRank was upgraded to version 1.1-2<a href="/packages/mvglmmRank">mvglmmRank</a> was <span class="action">upgraded</span> to version <a href="/packages/mvglmmRank/versions/44893">1.1-2</a><br /><h3>Package description:</h3><p>Maximum likelihood estimates are obtained via an EM algorithm with either a first-order or a fully exponential Laplace approximation.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/423722015-07-21T06:11:59Z2015-07-21T06:11:59ZRealVAMS was upgraded to version 0.3-2<a href="/packages/RealVAMS">RealVAMS</a> was <span class="action">upgraded</span> to version <a href="/packages/RealVAMS/versions/42171">0.3-2</a><br /><h3>Package description:</h3><p>The RealVAMs package fits a multivariate value-added model (VAM) (see Broatch and Lohr 2012) with normally distributed test scores and a binary outcome indicator. This material is based upon work supported by the National Science Foundation under grants DRL-1336027 and DRL-1336265.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/423402015-07-20T08:11:09Z2015-07-20T08:11:09ZGPvam was upgraded to version 3.0-3<a href="/packages/GPvam">GPvam</a> was <span class="action">upgraded</span> to version <a href="/packages/GPvam/versions/42139">3.0-3</a><br /><h3>Package description:</h3><p>Maximum likelihood estimators are obtained via an EM algorithm.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/423342015-07-19T17:51:25Z2015-07-19T17:51:25ZmvglmmRank was upgraded to version 1.1-1<a href="/packages/mvglmmRank">mvglmmRank</a> was <span class="action">upgraded</span> to version <a href="/packages/mvglmmRank/versions/42133">1.1-1</a><br /><h3>Package description:</h3><p>Maximum likelihood estimates are obtained via an EM algorithm with either a first-order or a fully exponential Laplace approximation.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/375722014-11-01T06:32:10Z2014-11-01T06:32:10ZRealVAMS was released<a href="/packages/RealVAMS">RealVAMS</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>Fits a multivariate value-added model (VAM), see Broatch, Green, and Karl (2018) <doi:10.32614/RJ-2018-033> and Broatch and Lohr (2012) <doi:10.3102/1076998610396900>, with normally distributed test scores and a binary outcome indicator. A pseudo-likelihood approach, Wolfinger (1993) <doi:10.1080/00949659308811554>, is used for the estimation of this joint generalized linear mixed model. The inner loop of the pseudo-likelihood routine (estimation of a linear mixed model) occurs in the framework of the EM algorithm presented by Karl, Yang, and Lohr (2013) <DOI:10.1016/j.csda.2012.10.004>. This material is based upon work supported by the National Science Foundation under grants DRL-1336027 and DRL-1336265.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/364482014-07-10T05:51:29Z2014-07-10T05:51:29ZmvglmmRank was released<a href="/packages/mvglmmRank">mvglmmRank</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>Maximum likelihood estimates are obtained via an EM algorithm with either a first-order or a fully exponential Laplace approximation.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/190902012-02-19T21:50:28Z2012-02-19T21:50:28ZGPvam was released<a href="/packages/GPvam">GPvam</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>An EM algorithm, Karl et al. (2013) <doi:10.1016/j.csda.2012.10.004>, is used to estimate the generalized, variable, and complete persistence models, Mariano et al. (2010) <doi:10.3102/1076998609346967>. These are multiple-membership linear mixed models with teachers modeled as "G-side" effects and students modeled with either "G-side" or "R-side" effects.</p>crantastic.org