tag:crantastic.org,2005:/packages/cgamLatest activity for cgam2019-05-01T17:40:36Zcrantastic.orgtag:crantastic.org,2005:TimelineEvent/878572019-05-01T17:40:36Z2019-05-01T17:40:36Zcgam was upgraded to version 1.14<a href="/packages/cgam">cgam</a> was <span class="action">upgraded</span> to version <a href="/packages/cgam/versions/83535">1.14</a><br /><h3>Package description:</h3><p>A constrained generalized additive model is fitted by the cgam routine. Given a set of predictors, each of which may have a shape or order restrictions, the maximum likelihood estimator for the constrained generalized additive model is found using an iteratively re-weighted cone projection algorithm. The ShapeSelect routine chooses a subset of predictor variables and describes the component relationships with the response. For each predictor, the user needs only specify a set of possible shape or order restrictions. A model selection method chooses the shapes and orderings of the relationships as well as the variables. The cone information criterion (CIC) is used to select the best combination of variables and shapes. A genetic algorithm may be used when the set of possible models is large. In addition, the cgam routine implements a two-dimensional isotonic regression using warped-plane splines without additivity assumptions. It can also fit a convex or concave regression surface with triangle splines without additivity assumptions. See Mary C. Meyer (2013)<doi:10.1080/10485252.2013.797577> for more details.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/875192019-04-23T04:21:00Z2019-04-23T04:21:00Zcgam was upgraded to version 1.13<a href="/packages/cgam">cgam</a> was <span class="action">upgraded</span> to version <a href="/packages/cgam/versions/83204">1.13</a><br /><h3>Package description:</h3><p>A constrained generalized additive model is fitted by the cgam routine. Given a set of predictors, each of which may have a shape or order restrictions, the maximum likelihood estimator for the constrained generalized additive model is found using an iteratively re-weighted cone projection algorithm. The ShapeSelect routine chooses a subset of predictor variables and describes the component relationships with the response. For each predictor, the user needs only specify a set of possible shape or order restrictions. A model selection method chooses the shapes and orderings of the relationships as well as the variables. The cone information criterion (CIC) is used to select the best combination of variables and shapes. A genetic algorithm may be used when the set of possible models is large. In addition, the cgam routine implements a two-dimensional isotonic regression using warped-plane splines without additivity assumptions. It can also fit a convex or concave regression surface with triangle splines without additivity assumptions. See Mary C. Meyer (2013)<doi:10.1080/10485252.2013.797577> for more details.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/814892018-11-08T19:20:33Z2018-11-08T19:20:33Zcgam was upgraded to version 1.12<a href="/packages/cgam">cgam</a> was <span class="action">upgraded</span> to version <a href="/packages/cgam/versions/77579">1.12</a><br /><h3>Package description:</h3><p>A constrained generalized additive model is fitted by the cgam routine. Given a set of predictors, each of which may have a shape or order restrictions, the maximum likelihood estimator for the constrained generalized additive model is found using an iteratively re-weighted cone projection algorithm. The ShapeSelect routine chooses a subset of predictor variables and describes the component relationships with the response. For each predictor, the user needs only specify a set of possible shape or order restrictions. A model selection method chooses the shapes and orderings of the relationships as well as the variables. The cone information criterion (CIC) is used to select the best combination of variables and shapes. A genetic algorithm may be used when the set of possible models is large. In addition, the cgam routine implements a two-dimensional isotonic regression using warped-plane splines without additivity assumptions. It can also fit a convex or concave regression surface with triangle splines without additivity assumptions. See Mary C. Meyer (2013)<doi:10.1080/10485252.2013.797577> for more details.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/769022018-06-28T20:00:39Z2018-06-28T20:00:39Zcgam was upgraded to version 1.11<a href="/packages/cgam">cgam</a> was <span class="action">upgraded</span> to version <a href="/packages/cgam/versions/73384">1.11</a><br /><h3>Package description:</h3><p>A constrained generalized additive model is fitted by the cgam routine. Given a set of predictors, each of which may have a shape or order restrictions, the maximum likelihood estimator for the constrained generalized additive model is found using an iteratively re-weighted cone projection algorithm. The ShapeSelect routine chooses a subset of predictor variables and describes the component relationships with the response. For each predictor, the user needs only specify a set of possible shape or order restrictions. A model selection method chooses the shapes and orderings of the relationships as well as the variables. The cone information criterion (CIC) is used to select the best combination of variables and shapes. A genetic algorithm may be used when the set of possible models is large. In addition, the cgam routine implements a two-dimensional isotonic regression using warped-plane splines without additivity assumptions. It can also fit a convex or concave regression surface with triangle splines without additivity assumptions. See Mary C. Meyer (2013)<doi:10.1080/10485252.2013.797577> for more details.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/736452018-04-11T18:00:41Z2018-04-11T18:00:41Zcgam was upgraded to version 1.10<a href="/packages/cgam">cgam</a> was <span class="action">upgraded</span> to version <a href="/packages/cgam/versions/70307">1.10</a><br /><h3>Package description:</h3><p>A constrained generalized additive model is fitted by the cgam routine. Given a set of predictors, each of which may have a shape or order restrictions, the maximum likelihood estimator for the constrained generalized additive model is found using an iteratively re-weighted cone projection algorithm. The ShapeSelect routine chooses a subset of predictor variables and describes the component relationships with the response. For each predictor, the user needs only specify a set of possible shape or order restrictions. A model selection method chooses the shapes and orderings of the relationships as well as the variables. The cone information criterion (CIC) is used to select the best combination of variables and shapes. A genetic algorithm may be used when the set of possible models is large. In addition, the cgam routine implements a two-dimensional isotonic regression using warped-plane splines without additivity assumptions. It can also fit a convex or concave regression surface with triangle splines without additivity assumptions. See Mary C. Meyer (2013)<doi:10.1080/10485252.2013.797577> for more details.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/716312018-02-15T18:40:32Z2018-02-15T18:40:32Zcgam was upgraded to version 1.9<a href="/packages/cgam">cgam</a> was <span class="action">upgraded</span> to version <a href="/packages/cgam/versions/68411">1.9</a><br /><h3>Package description:</h3><p>A constrained generalized additive model is fitted by the cgam routine. Given a set of predictors, each of which may have a shape or order restrictions, the maximum likelihood estimator for the constrained generalized additive model is found using an iteratively re-weighted cone projection algorithm. The ShapeSelect routine chooses a subset of predictor variables and describes the component relationships with the response. For each predictor, the user needs only specify a set of possible shape or order restrictions. A model selection method chooses the shapes and orderings of the relationships as well as the variables. The cone information criterion (CIC) is used to select the best combination of variables and shapes. A genetic algorithm may be used when the set of possible models is large. In addition, the cgam routine implements a two-dimensional isotonic regression using warped-plane splines without additivity assumptions. It can also fit a convex or concave regression surface with triangle splines without additivity assumptions. See Mary C. Meyer (2013)<doi:10.1080/10485252.2013.797577> for more details.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/669542017-10-02T07:40:28Z2017-10-02T07:40:28Zcgam was upgraded to version 1.8<a href="/packages/cgam">cgam</a> was <span class="action">upgraded</span> to version <a href="/packages/cgam/versions/64041">1.8</a><br /><h3>Package description:</h3><p>A constrained generalized additive model is fitted by the cgam routine. Given a set of predictors, each of which may have a shape or order restrictions, the maximum likelihood estimator for the constrained generalized additive model is found using an iteratively re-weighted cone projection algorithm. The ShapeSelect routine chooses a subset of predictor variables and describes the component relationships with the response. For each predictor, the user needs only specify a set of possible shape or order restrictions. A model selection method chooses the shapes and orderings of the relationships as well as the variables. The cone information criterion (CIC) is used to select the best combination of variables and shapes. A genetic algorithm may be used when the set of possible models is large. In addition, the cgam routine implements a two-dimensional isotonic regression using warped-plane splines without additivity assumptions. It can also fit a convex or concave regression surface with triangle splines without additivity assumptions. See Mary C. Meyer (2013)<doi:10.1080/10485252.2013.797577> for more details.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/641022017-07-02T22:20:25Z2017-07-02T22:20:25Zcgam was upgraded to version 1.7<a href="/packages/cgam">cgam</a> was <span class="action">upgraded</span> to version <a href="/packages/cgam/versions/61325">1.7</a><br /><h3>Package description:</h3><p>A constrained generalized additive model is fitted by the cgam routine. Given a set of predictors, each of which may have a shape or order restrictions, the maximum likelihood estimator for the constrained generalized additive model is found using an iteratively re-weighted cone projection algorithm. The ShapeSelect routine chooses a subset of predictor variables and describes the component relationships with the response. For each predictor, the user needs only specify a set of possible shape or order restrictions. A model selection method chooses the shapes and orderings of the relationships as well as the variables. The cone information criterion (CIC) is used to select the best combination of variables and shapes. A genetic algorithm may be used when the set of possible models is large. In addition, the cgam routine implements a two-dimensional isotonic regression using warped-plane splines without additivity assumptions. It can also fit a convex or concave regression surface with triangle splines without additivity assumptions.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/563962016-11-02T19:00:22Z2016-11-02T19:00:22Zcgam was upgraded to version 1.6<a href="/packages/cgam">cgam</a> was <span class="action">upgraded</span> to version <a href="/packages/cgam/versions/54076">1.6</a><br /><h3>Package description:</h3><p>A constrained generalized additive model is fitted by the cgam routine. Given a set of predictors, each of which may have a shape or order restrictions, the maximum likelihood estimator for the constrained generalized additive model is found using an iteratively re-weighted cone projection algorithm. The ShapeSelect routine chooses a subset of predictor variables and describes the component relationships with the response. For each predictor, the user need only specify a set of possible shape or order restrictions. A model selection method chooses the shapes and orderings of the relationships as well as the variables. The cone information criterion (CIC) is used to select the best combination of variables and shapes. A genetic algorithm may be used when the set of possible models is large. In addition, the wps routine implements a two-dimensional isotonic regression without additivity assumptions.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/517792016-06-05T17:20:17Z2016-06-05T17:20:17Zcgam was upgraded to version 1.5<a href="/packages/cgam">cgam</a> was <span class="action">upgraded</span> to version <a href="/packages/cgam/versions/50010">1.5</a><br /><h3>Package description:</h3><p>A constrained generalized additive model is fitted by the cgam routine. Given a set of predictors with or without shape or order restrictions, the maximum likelihood estimator for the constrained generalized additive model is found using an iteratively re-weighted cone projection algorithm. The cone information criterion (CIC) may be used to select the best combination of variables and shapes. One extension of this package is isotonic regression in two dimensions using warped-plane splines without using additivity assumptions, which is implemented by the wps routine.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/504022016-04-14T23:00:22Z2016-04-14T23:00:22Zcgam was upgraded to version 1.4<a href="/packages/cgam">cgam</a> was <span class="action">upgraded</span> to version <a href="/packages/cgam/versions/48732">1.4</a><br /><h3>Package description:</h3><p>A constrained generalized additive model is fitted by the cgam routine. Given a set of predictors with or without shape or order restrictions, the maximum likelihood estimator for the constrained generalized additive model is found using an iteratively re-weighted cone projection algorithm. The cone information criterion (CIC) may be used to select the best combination of variables and shapes. One extension of this package is isotonic regression in two dimensions using warped-plane splines without using additivity assumptions, which is implemented by the wps routine.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/440492015-10-01T11:30:39Z2015-10-01T11:30:39Zcgam was upgraded to version 1.3<a href="/packages/cgam">cgam</a> was <span class="action">upgraded</span> to version <a href="/packages/cgam/versions/43841">1.3</a><br /><h3>Package description:</h3><p>A constrained generalized additive model is fitted by the cgam routine. Given a set of predictors with or without shape or order restrictions, the maximum likelihood estimator for the constrained generalized additive model is found using an iteratively re-weighted cone projection algorithm. The cone information criterion (CIC) may be used to select the best combination of variables and shapes. This package depends on the R package coneproj.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/358002014-03-05T17:30:21Z2014-03-05T17:30:21Zcgam was released<a href="/packages/cgam">cgam</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>A constrained generalized additive model is fitted by the cgam routine. Given a set of predictors, each of which may have a shape or order restrictions, the maximum likelihood estimator for the constrained generalized additive model is found using an iteratively re-weighted cone projection algorithm. The ShapeSelect routine chooses a subset of predictor variables and describes the component relationships with the response. For each predictor, the user needs only specify a set of possible shape or order restrictions. A model selection method chooses the shapes and orderings of the relationships as well as the variables. The cone information criterion (CIC) is used to select the best combination of variables and shapes. A genetic algorithm may be used when the set of possible models is large. In addition, the cgam routine implements a two-dimensional isotonic regression using warped-plane splines without additivity assumptions. It can also fit a convex or concave regression surface with triangle splines without additivity assumptions. See Mary C. Meyer (2013)<doi:10.1080/10485252.2013.797577> for more details.</p>crantastic.org