docs

felixleopoldo · felixleopoldo · commit 9fd9910a178d · 2024-12-20T03:36:52.000+01:00
diff --git a/docs/source/available_structure_learning_algorithms.rst b/docs/source/available_structure_learning_algorithms.rst
@@ -300,19 +300,19 @@ To add new modules, see :ref:`new_modules`.
      -  :ref:`sklearn_glasso <sklearn_glasso>`
    * - :ref:`BOSS <tetrad_boss>`
      - `CPDAG <https://search.r-project.org/CRAN/refmans/pcalg/html/dag2cpdag.html>`__
-     - `causal-cmd <https://github.com/bd2kccd/causal-cmd>`__
+     - `TETRAD <https://github.com/bd2kccd/causal-cmd>`__
      -  :ref:`tetrad_boss <tetrad_boss>`
    * - :ref:`FASK <tetrad_fask>`
      - `DAG <https://en.wikipedia.org/wiki/Directed_acyclic_graph>`__
-     - `causal-cmd <https://github.com/bd2kccd/causal-cmd>`__
+     - `TETRAD <https://github.com/bd2kccd/causal-cmd>`__
      -  :ref:`tetrad_fask <tetrad_fask>`
    * - :ref:`FGES <tetrad_fges>`
      - `CPDAG <https://search.r-project.org/CRAN/refmans/pcalg/html/dag2cpdag.html>`__
-     - `causal-cmd <https://github.com/bd2kccd/causal-cmd>`__
+     - `TETRAD <https://github.com/bd2kccd/causal-cmd>`__
      -  :ref:`tetrad_fges <tetrad_fges>`
    * - :ref:`FOFC <tetrad_fofc>`
      - `DAG <https://en.wikipedia.org/wiki/Directed_acyclic_graph>`__
-     - `causal-cmd <https://github.com/bd2kccd/causal-cmd>`__
+     - `TETRAD <https://github.com/bd2kccd/causal-cmd>`__
      -  :ref:`tetrad_fofc <tetrad_fofc>`
    * - :ref:`FTFC <tetrad_ftfc>`
      - `DAG <https://en.wikipedia.org/wiki/Directed_acyclic_graph>`__
diff --git a/docs/source/structure_learning_algorithms/causallearn_ges.rst b/docs/source/structure_learning_algorithms/causallearn_ges.rst
@@ -0,0 +1,66 @@
+
+
+
+:og:description: This package implements the tPC algorithm for causal discovery. The 't' stands for 'temporal' or 'tiers' and indicates that background knowledge in the form of a partial node/variable ordering is available.
+:og:image:alt: Benchpress logo
+:og:sitename: Benchpress causal discovery platform
+:og:title: temporal PC (causallearn_ges)
+ 
+.. meta::
+    :title: temporal PC 
+    :description: This package implements the tPC algorithm for causal discovery. The 't' stands for 'temporal' or 'tiers' and indicates that background knowledge in the form of a partial node/variable ordering is available.
+
+
+.. _causallearn_ges: 
+
+tPC (tpc) 
+**********
+
+
+
+.. list-table:: 
+
+   * - Module name
+     - `causallearn_ges <https://github.com/felixleopoldo/benchpress/tree/master/workflow/rules/structure_learning_algorithms/causallearn_ges>`__
+   * - Package
+     - `tpc <https://github.com/bips-hb/tpc>`__
+   * - Version
+     - 137e18b
+   * - Language
+     - `R <https://www.r-project.org/>`__
+   * - Docs
+     - `here <https://github.com/bips-hb/tpc>`__
+   * - Paper
+     - :footcite:t:`meek1997graphical`, :footcite:t:`chickering2002optimal`, :footcite:t:`ramsey2017million`
+   * - Graph type
+     - `CPDAG <https://search.r-project.org/CRAN/refmans/pcalg/html/dag2cpdag.html>`__
+   * - Docker 
+     - `bpimages/causal-learn:0.1.3.4-arm64 <https://hub.docker.com/r/bpimages/causal-learn/tags>`__
+
+
+
+
+temporal PC 
+---------------
+
+
+Some text of the algorithm/module.
+
+
+
+.. rubric:: Example JSON
+
+
+.. code-block:: json
+
+
+    [
+      {
+        "id": "myalg",
+        "cutoff": 0.5,
+        "timeout": null
+      }
+    ]
+
+.. footbibliography::
+
diff --git a/docs/source/structure_learning_algorithms/mice.rst b/docs/source/structure_learning_algorithms/mice.rst
@@ -48,11 +48,7 @@ The mice package implements a method to deal with missing data. The package crea
 
 .. important::
 
-<<<<<<< HEAD
-    This is not a structure learning algorithm, just a workaround to use imputed data for the :ref:`bips_tpc` module.
-=======
     This is not a structure learning algorithm, just a workaround to use imputed data for the :ref:`bips_tpc <bips_tpc>` module.
->>>>>>> dev
 
 
 
diff --git a/docs/source/structure_learning_algorithms/tetrad_boss.rst b/docs/source/structure_learning_algorithms/tetrad_boss.rst
@@ -13,8 +13,8 @@
 
 .. _tetrad_boss: 
 
-BOSS (causal-cmd) 
-******************
+BOSS (TETRAD) 
+**************
 
 
 
@@ -23,7 +23,7 @@ BOSS (causal-cmd)
    * - Module name
      - `tetrad_boss <https://github.com/felixleopoldo/benchpress/tree/master/workflow/rules/structure_learning_algorithms/tetrad_boss>`__
    * - Package
-     - `causal-cmd <https://github.com/bd2kccd/causal-cmd>`__
+     - `TETRAD <https://github.com/bd2kccd/causal-cmd>`__
    * - Version
      - 1.10.0
    * - Language
diff --git a/docs/source/structure_learning_algorithms/tetrad_fas.rst b/docs/source/structure_learning_algorithms/tetrad_fas.rst
@@ -0,0 +1,70 @@
+
+
+
+:og:description: This package implements the tPC algorithm for causal discovery. The 't' stands for 'temporal' or 'tiers' and indicates that background knowledge in the form of a partial node/variable ordering is available.
+:og:image:alt: Benchpress logo
+:og:sitename: Benchpress causal discovery platform
+:og:title: temporal PC (tetrad_fas)
+ 
+.. meta::
+    :title: temporal PC 
+    :description: This package implements the tPC algorithm for causal discovery. The 't' stands for 'temporal' or 'tiers' and indicates that background knowledge in the form of a partial node/variable ordering is available.
+
+
+.. _tetrad_fas: 
+
+tPC (tpc) 
+**********
+
+
+
+.. list-table:: 
+
+   * - Module name
+     - `tetrad_fas <https://github.com/felixleopoldo/benchpress/tree/master/workflow/rules/structure_learning_algorithms/tetrad_fas>`__
+   * - Package
+     - `tpc <https://github.com/bips-hb/tpc>`__
+   * - Version
+     - 137e18b
+   * - Language
+     - `R <https://www.r-project.org/>`__
+   * - Docs
+     - `here <https://github.com/bips-hb/tpc>`__
+   * - Paper
+     - 
+   * - Graph type
+     - `CPDAG <https://search.r-project.org/CRAN/refmans/pcalg/html/dag2cpdag.html>`__
+   * - Docker 
+     - `bpimages/causal-cmd:1.10.0 <https://hub.docker.com/r/bpimages/causal-cmd/tags>`__
+
+
+
+
+temporal PC 
+---------------
+
+
+From the Tetrad manual: This is just the adjacency search of the PC algorithm, included here for times when just the adjacency search is needed, as when one is subsequently just going to orient variables pairwise.
+
+.. rubric:: Some fields described 
+* ``edgeConstraints`` Name of the JSON file containing background knowledge 
+
+
+.. rubric:: Example JSON
+
+
+.. code-block:: json
+
+
+    [
+      {
+        "id": "fas-fisher-z",
+        "test": "fisher-z-test",
+        "datatype": "continuous",
+        "timeout": null,
+        "edgeConstraints": "edgeConstraints.json"
+      }
+    ]
+
+.. footbibliography::
+
diff --git a/docs/source/structure_learning_algorithms/tetrad_fask.rst b/docs/source/structure_learning_algorithms/tetrad_fask.rst
@@ -13,8 +13,8 @@
 
 .. _tetrad_fask: 
 
-FASK (causal-cmd) 
-******************
+FASK (TETRAD) 
+**************
 
 
 
@@ -23,7 +23,7 @@ FASK (causal-cmd)
    * - Module name
      - `tetrad_fask <https://github.com/felixleopoldo/benchpress/tree/master/workflow/rules/structure_learning_algorithms/tetrad_fask>`__
    * - Package
-     - `causal-cmd <https://github.com/bd2kccd/causal-cmd>`__
+     - `TETRAD <https://github.com/bd2kccd/causal-cmd>`__
    * - Version
      - 1.10.0
    * - Language
diff --git a/docs/source/structure_learning_algorithms/tetrad_fges.rst b/docs/source/structure_learning_algorithms/tetrad_fges.rst
@@ -13,8 +13,8 @@
 
 .. _tetrad_fges: 
 
-FGES (causal-cmd) 
-******************
+FGES (TETRAD) 
+**************
 
 
 
@@ -23,7 +23,7 @@ FGES (causal-cmd)
    * - Module name
      - `tetrad_fges <https://github.com/felixleopoldo/benchpress/tree/master/workflow/rules/structure_learning_algorithms/tetrad_fges>`__
    * - Package
-     - `causal-cmd <https://github.com/bd2kccd/causal-cmd>`__
+     - `TETRAD <https://github.com/bd2kccd/causal-cmd>`__
    * - Version
      - 1.10.0
    * - Language
diff --git a/docs/source/structure_learning_algorithms/tetrad_fofc.rst b/docs/source/structure_learning_algorithms/tetrad_fofc.rst
@@ -13,8 +13,8 @@
 
 .. _tetrad_fofc: 
 
-FOFC (causal-cmd) 
-******************
+FOFC (TETRAD) 
+**************
 
 
 
@@ -23,7 +23,7 @@ FOFC (causal-cmd)
    * - Module name
      - `tetrad_fofc <https://github.com/felixleopoldo/benchpress/tree/master/workflow/rules/structure_learning_algorithms/tetrad_fofc>`__
    * - Package
-     - `causal-cmd <https://github.com/bd2kccd/causal-cmd>`__
+     - `TETRAD <https://github.com/bd2kccd/causal-cmd>`__
    * - Version
      - 1.10.0
    * - Language
diff --git a/workflow/rules/structure_learning_algorithms/huge_glasso/docs.rst b/workflow/rules/structure_learning_algorithms/huge_glasso/docs.rst
@@ -0,0 +1 @@
+Abstract: We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm—the graphical lasso—that is remarkably fast: It solves a 1000-node problem (∼500000 parameters) in at most a minute and is 30–4000 times faster than competing methods. It also provides a conceptual link between the exact problem and the approximation suggested by Meinshausen and Bühlmann (2006). We illustrate the method on some cell-signaling data from proteomics.

Original file line number	Diff line number	Diff line change
`@@ -0,0 +1 @@`
	`1`	+Abstract: We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm—the graphical lasso—that is remarkably fast: It solves a 1000-node problem (∼500000 parameters) in at most a minute and is 30–4000 times faster than competing methods. It also provides a conceptual link between the exact problem and the approximation suggested by Meinshausen and Bühlmann (2006). We illustrate the method on some cell-signaling data from proteomics.