Docs

Author: felixleopoldo

docs/render_alg_docs.py

@@ -159,7 +159,7 @@ def info_to_small_table():
 
 #### This is the modules docs files:
 for p in sorted(algspath.iterdir()):
-    print(p.name)
+
     if not p.is_dir():
         continue
     if p.name == "docs.rst" or p.name == ".DS_Store":

docs/source/available_structure_learning_algorithms.rst

@@ -48,18 +48,18 @@ Algorithms
     structure_learning_algorithms/bidag_itsearch
     structure_learning_algorithms/huge_mb
     structure_learning_algorithms/gcastle_mcsl
+    structure_learning_algorithms/mice
     structure_learning_algorithms/bnlearn_mmhc
     structure_learning_algorithms/bnlearn_mmpc
     structure_learning_algorithms/mvpc
-    structure_learning_algorithms/causallearn_ges
     structure_learning_algorithms/gcastle_notears
     structure_learning_algorithms/gcastle_notears_low_rank
     structure_learning_algorithms/gcastle_notears_nonlinear
     structure_learning_algorithms/bidag_order_mcmc
+    structure_learning_algorithms/gcastle_pc
     structure_learning_algorithms/pcalg_pc
-    structure_learning_algorithms/bnlearn_pcstable
     structure_learning_algorithms/tetrad_pc
-    structure_learning_algorithms/gcastle_pc
+    structure_learning_algorithms/bnlearn_pcstable
     structure_learning_algorithms/paralleldg
     structure_learning_algorithms/trilearn_pgibbs
     structure_learning_algorithms/bidag_partition_mcmc
@@ -69,9 +69,8 @@ Algorithms
     structure_learning_algorithms/bnlearn_rsmax2
     structure_learning_algorithms/bnlearn_sihitonpc
     structure_learning_algorithms/huge_tiger
-    structure_learning_algorithms/bnlearn_tabu
-    structure_learning_algorithms/mice
     structure_learning_algorithms/bips_tpc
+    structure_learning_algorithms/bnlearn_tabu
 
 
 Apart from the original parameters of the underlying software, each algorithm module is equipped with an additional parameter, ``timeout``, which is the maximum time in seconds allowed for the algorithm to run.
@@ -114,7 +113,7 @@ To add new modules, see :ref:`new_modules`.
      - `DAG <https://en.wikipedia.org/wiki/Directed_acyclic_graph>`__, `CPDAG <https://search.r-project.org/CRAN/refmans/pcalg/html/dag2cpdag.html>`__
      - `BiDAG <https://cran.r-project.org/web/packages/BiDAG/index.html>`__
      -  :ref:`bidag_partition_mcmc <bidag_partition_mcmc>`
-   * - :ref:`tPC <bips_tpc>`
+   * - :ref:`TPC <bips_tpc>`
      - `CPDAG <https://search.r-project.org/CRAN/refmans/pcalg/html/dag2cpdag.html>`__
      - `tpc <https://github.com/bips-hb/tpc>`__
      -  :ref:`bips_tpc <bips_tpc>`
@@ -266,7 +265,7 @@ To add new modules, see :ref:`new_modules`.
      - `UG <https://en.wikipedia.org/wiki/Graph_(discrete_mathematics)#Graph>`__
      - `huge <https://cran.r-project.org/web/packages/huge/index.html>`__
      -  :ref:`huge_tiger <huge_tiger>`
-   * - :ref:`mice <mice>`
+   * - :ref:`MICE <mice>`
      - 
      - `mice <https://amices.org/mice/>`__
      -  :ref:`mice <mice>`

docs/source/structure_learning_algorithms/bips_tpc.rst

@@ -4,16 +4,16 @@
 :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 (bips_tpc)
+:og:title: Temporal PC (bips_tpc)
 
 .. meta::
-    :title: temporal PC 
+    :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.
 
 
 .. _bips_tpc: 
 
-tPC (tpc) 
+TPC (tpc) 
 **********
 
 
@@ -40,11 +40,11 @@ tPC (tpc)
 
 
 
-temporal PC 
+Temporal PC 
 ---------------
 
 
-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. Our implementation is a modified version of pc from the pcalg package (Kalisch et al. 2012) with the following additional options:
+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. Our implementation is a modified version of pc from the pcalg package (Kalisch et al. 2012) with more :ref:`edge_constraints` supported.
 
 It also supports the Multiple Imputation for Causal Graph Discovery (micd) package.
 Which is and add-on to the R package pcalg for handling missing data in contrataint-based causal graph discovery. Supports continuous, discrete and mixed data. Two options are available: 1) gaussCItwd, disCItwd and mixCItwd perform test-wise deletion, where missing observations are deleted as necessary on a test-by-test basis; 2) gaussMItest, disMItest and mixMItest perform conditional independence tests on multiply imputed data. 

docs/source/structure_learning_algorithms/mice.rst

@@ -13,7 +13,7 @@
 
 .. _mice: 
 
-mice (mice) 
+MICE (mice) 
 ************
 
 

docs/source/structure_learning_algorithms/tetrad_fas.rst

@@ -1,4 +1,4 @@
-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. Our implementation is a modified version of pc from the pcalg package (Kalisch et al. 2012) with the following additional options:
+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. Our implementation is a modified version of pc from the pcalg package (Kalisch et al. 2012) with more :ref:`edge_constraints` supported.
 
 It also supports the Multiple Imputation for Causal Graph Discovery (micd) package.
 Which is and add-on to the R package pcalg for handling missing data in contrataint-based causal graph discovery. Supports continuous, discrete and mixed data. Two options are available: 1) gaussCItwd, disCItwd and mixCItwd perform test-wise deletion, where missing observations are deleted as necessary on a test-by-test basis; 2) gaussMItest, disMItest and mixMItest perform conditional independence tests on multiply imputed data. 

workflow/rules/structure_learning_algorithms/bips_tpc/docs.rst

@@ -1,6 +1,6 @@
 {
-    "title": "tPC",
-    "title_full": "temporal PC",
+    "title": "TPC",
+    "title_full": "Temporal PC",
     "version": "137e18b",
     "package": {
         "title": "tpc",

workflow/rules/structure_learning_algorithms/bips_tpc/info.json

@@ -1,5 +1,5 @@
 {
-    "title": "mice",
+    "title": "MICE",
     "title_full": "Multivariate Imputation by Chained Equations",
     "version": "3.17.0",
     "package": {