tag:crantastic.org,2005:/packages/pcalgLatest activity for pcalg2019-10-23T15:02:10Zcrantastic.orgtag:crantastic.org,2005:TimelineEvent/940072019-10-23T15:02:11Z2019-10-23T15:02:11Zpcalg was upgraded to version 2.6-7<a href="/packages/pcalg">pcalg</a> was <span class="action">upgraded</span> to version <a href="/packages/pcalg/versions/89366">2.6-7</a><br /><h3>Package description:</h3><p>Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm, the Generalized Backdoor Criterion (GBC), the Generalized Adjustment Criterion (GAC) and some related functions are implemented. Functions for incorporating background knowledge are provided.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/927152019-09-16T16:02:39Z2019-09-16T16:02:39Zpcalg was upgraded to version 2.6-6<a href="/packages/pcalg">pcalg</a> was <span class="action">upgraded</span> to version <a href="/packages/pcalg/versions/88172">2.6-6</a><br /><h3>Package description:</h3><p>Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm, the Generalized Backdoor Criterion (GBC), the Generalized Adjustment Criterion (GAC) and some related functions are implemented. Functions for incorporating background knowledge are provided.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/920412019-08-27T12:22:32Z2019-08-27T12:22:32Zpcalg was upgraded to version 2.6-5<a href="/packages/pcalg">pcalg</a> was <span class="action">upgraded</span> to version <a href="/packages/pcalg/versions/87529">2.6-5</a><br /><h3>Package description:</h3><p>Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm, the Generalized Backdoor Criterion (GBC), the Generalized Adjustment Criterion (GAC) and some related functions are implemented. Functions for incorporating background knowledge are provided.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/876912019-04-25T23:02:21Z2019-04-25T23:02:21Zpcalg was upgraded to version 2.6-2<a href="/packages/pcalg">pcalg</a> was <span class="action">upgraded</span> to version <a href="/packages/pcalg/versions/83370">2.6-2</a><br /><h3>Package description:</h3><p>Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm, the Generalized Backdoor Criterion (GBC), the Generalized Adjustment Criterion (GAC) and some related functions are implemented. Functions for incorporating background knowledge are provided.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/759492018-06-04T18:42:02Z2018-06-04T18:42:02Zpcalg was upgraded to version 2.6-0<a href="/packages/pcalg">pcalg</a> was <span class="action">upgraded</span> to version <a href="/packages/pcalg/versions/72474">2.6-0</a><br /><h3>Package description:</h3><p>Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm, the Generalized Backdoor Criterion (GBC), the Generalized Adjustment Criterion (GAC) and some related functions are implemented. Functions for incorporating background knowledge are provided.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/644712017-07-12T11:01:47Z2017-07-12T11:01:47Zpcalg was upgraded to version 2.5-0<a href="/packages/pcalg">pcalg</a> was <span class="action">upgraded</span> to version <a href="/packages/pcalg/versions/61669">2.5-0</a><br /><h3>Package description:</h3><p>Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm, the Generalized Backdoor Criterion (GBC), the Generalized Adjustment Criterion (GAC) and some related functions are implemented. Functions for incorporating background knowledge are provided.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/600482017-03-13T18:10:21Z2017-03-13T18:10:21Zpcalg was upgraded to version 2.4-5<a href="/packages/pcalg">pcalg</a> was <span class="action">upgraded</span> to version <a href="/packages/pcalg/versions/57483">2.4-5</a><br /><h3>Package description:</h3><p>Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm, the Generalized Backdoor Criterion (GBC) and the Generalized Adjustment Criterion (GAC) are implemented.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/554002016-09-28T15:21:28Z2016-09-28T15:21:28Zpcalg was upgraded to version 2.4-3<a href="/packages/pcalg">pcalg</a> was <span class="action">upgraded</span> to version <a href="/packages/pcalg/versions/53178">2.4-3</a><br /><h3>Package description:</h3><p>Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm, the Generalized Backdoor Criterion (GBC) and the Generalized Adjustment Criterion (GAC) are implemented.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/553462016-09-26T15:01:23Z2016-09-26T15:01:23Zpcalg was upgraded to version 2.4-2<a href="/packages/pcalg">pcalg</a> was <span class="action">upgraded</span> to version <a href="/packages/pcalg/versions/53124">2.4-2</a><br /><h3>Package description:</h3><p>Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm, the Generalized Backdoor Criterion (GBC) and the Generalized Adjustment Criterion (GAC) are implemented.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/424332015-07-23T07:31:42Z2015-07-23T07:31:42Zpcalg was upgraded to version 2.2-4<a href="/packages/pcalg">pcalg</a> was <span class="action">upgraded</span> to version <a href="/packages/pcalg/versions/42231">2.2-4</a><br /><h3>Package description:</h3><p>Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm, the Generalized Backdoor Criterion (GBC) and the Generalized Adjustment Criterion (GAC) are implemented.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/421402015-07-12T21:51:39Z2015-07-12T21:51:39Zpcalg was upgraded to version 2.2-3<a href="/packages/pcalg">pcalg</a> was <span class="action">upgraded</span> to version <a href="/packages/pcalg/versions/41939">2.2-3</a><br /><h3>Package description:</h3><p>Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm, the Generalized Backdoor Criterion (GBC) and the Generalized Adjustment Criterion (GAC) are implemented.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/416132015-06-24T09:31:38Z2015-06-24T09:31:38Zpcalg was upgraded to version 2.2-2<a href="/packages/pcalg">pcalg</a> was <span class="action">upgraded</span> to version <a href="/packages/pcalg/versions/41413">2.2-2</a><br /><h3>Package description:</h3><p>Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm, the Generalized Backdoor Criterion (GBC) and the Generalized Adjustment Criterion (GAC) are implemented.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/407482015-05-13T15:31:56Z2015-05-13T15:31:56Zpcalg was upgraded to version 2.2-0<a href="/packages/pcalg">pcalg</a> was <span class="action">upgraded</span> to version <a href="/packages/pcalg/versions/40548">2.2-0</a><br /><h3>Package description:</h3><p>Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm and the generalized backdoor criterion is implemented.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/396802015-03-19T10:31:23Z2015-03-19T10:31:23Zpcalg was upgraded to version 2.0-10<a href="/packages/pcalg">pcalg</a> was <span class="action">upgraded</span> to version <a href="/packages/pcalg/versions/39504">2.0-10</a><br /><h3>Package description:</h3><p>Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm and the generalized backdoor criterion is implemented.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/308462013-03-22T14:50:52Z2013-03-22T14:50:52Zpcalg was upgraded to version 1.1-6<a href="/packages/pcalg">pcalg</a> was <span class="action">upgraded</span> to version <a href="/packages/pcalg/versions/26232">1.1-6</a><br /><h3>Package description:</h3><p>Standard and robust estimation of the equivalence class of a Directed Acyclic Graph (DAG) via the PC-Algorithm. The equivalence class is represented by its (unique) Completete Partially Directed Acyclic Graph (CPDAG). Furthermore, a PAG instead of a CPDAG can be estimated if latent variables and/or selection variables are assumed to be present. FCI and RFCI are available for estimating PAGs. Functions for causal inference using the IDA algorithm (based on do-calculus of Judea Pearl) are provided for CPDAGs.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/220462012-04-26T17:50:50Z2012-04-26T17:50:50Zpcalg was upgraded to version 1.1-5<a href="/packages/pcalg">pcalg</a> was <span class="action">upgraded</span> to version <a href="/packages/pcalg/versions/18389">1.1-5</a><br /><h3>Package description:</h3><p>Standard and robust estimation of the equivalence class of a Directed Acyclic Graph (DAG) via the PC-Algorithm. The equivalence class is represented by its (unique) Completete Partially Directed Acyclic Graph (CPDAG). Furthermore, a PAG instead of a CPDAG can be estimated if latent variables and/or selection variables are assumed to be present. FCI and RFCI are available for estimating PAGs. Functions for causal inference using the IDA algorithm (based on do-calculus of Judea Pearl) are provided for CPDAGs.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/134592011-05-09T15:50:36Z2011-05-09T15:50:36Zpcalg was upgraded to version 1.1-4<a href="/packages/pcalg">pcalg</a> was <span class="action">upgraded</span> to version <a href="/packages/pcalg/versions/12274">1.1-4</a><br /><h3>Package description:</h3><p>Standard and robust estimation of the equivalence class of a Directed Acyclic Graph (DAG) via the PC-Algorithm. The equivalence class is represented by its (unique) Completete Partially Directed Acyclic Graph (CPDAG). Furthermore, a PAG instead of a CPDAG can be estimated if latent variables and/or selection variables are assumed to be present. FCI and RFCI are available for estimating PAGs. Functions for causal inference using the IDA algorithm (based on do-calculus of Judea Pearl) are provided for CPDAGs.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/95552010-10-27T11:21:09Z2010-10-27T11:21:09Zpcalg was upgraded to version 1.1-2<a href="/packages/pcalg">pcalg</a> was <span class="action">upgraded</span> to version <a href="/packages/pcalg/versions/9303">1.1-2</a><br /><h3>Package description:</h3><p>Standard and robust estimation of the equivalence class of a Directed Acyclic Graph (DAG) via the PC-Algorithm. The equivalence class is represented by its (unique) Completete Partially Directed Acyclic Graph (CPDAG). Furthermore, a PAG instead of a CPDAG can be estimated if latent variables and/or selection variables are assumed to be present. Functions for causal inference using the IDA algorithm (based on do-calculus of Judea Pearl) are provided for CPDAGs.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/73882010-07-16T10:55:41Z2010-07-16T10:55:41Zpcalg was upgraded to version 1.0-2<a href="/packages/pcalg">pcalg</a> was <span class="action">upgraded</span> to version <a href="/packages/pcalg/versions/7837">1.0-2</a><br /><h3>Package description:</h3><p>Standard and robust estimation of the equivalence class of a Directed Acyclic Graph (DAG) via the PC-Algorithm. The equivalence class is represented by its (unique) Completete Partially Directed Acyclic Graph (CPDAG). Furthermore, a PAG instead of a CPDAG can be estimated if latent variables and/or selection variables are assumed to be present. Functions for causal inference using the IDA algorithm (based on do-calculus of Judea Pearl) are provided for CPDAGs.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/17322009-09-23T21:14:58Z2009-09-23T21:14:58Zpcalg was upgraded to version 0.1-9<a href="/packages/pcalg">pcalg</a> was <span class="action">upgraded</span> to version <a href="/packages/pcalg/versions/4837">0.1-9</a><br /><h3>Package description:</h3><p>Standard and robust estimation the equivalence class of a Directed Acyclic Graph (DAG) via the PC-Algorithm. The equivalence class is represented by its (unique) Completetd Partially Directed Acyclic Graph (CPDAG). The function pcSelect implements variable selection techniques based on the PC-algorithm. Furthermore, a PAG instead of a CPDAG can be estimated if latent variables and/or selection variables are assumed to be present. Functions for causal inference (using do-calculus of Judea Pearl) are provided for CPDAGs.</p>crantastic.org