Docs

Author: felixleopoldo

docs/docs_utils.py

@@ -14,6 +14,10 @@ def str2link(s):
         return "`PAG <https://cmu-phil.github.io/tetrad/manual/#appendix>`__"
     if s == "CPDAG":
         return "`CPDAG <https://search.r-project.org/CRAN/refmans/pcalg/html/dag2cpdag.html>`__"
+    if s == "MPDAG":
+        return "`MPDAG <https://auai.org/uai2017/proceedings/papers/120.pdf>`__"
+    if s == "TMPDAG":
+        return "`TMPDAG <https://proceedings.mlr.press/v216/bang23a.html>`__"
     if s == "R":
         return "`R <https://www.r-project.org/>`__"
     if s == "Python":

docs/source/available_structure_learning_algorithms.rst

@@ -581,7 +581,7 @@ Modules for MCMC algorithms can be used seamlessly with the other modules. Howev
      - 
    * - :ref:`TPC <bips_tpc>`
      - `tpc <https://github.com/bips-hb/tpc>`__
-     - `CPDAG <https://search.r-project.org/CRAN/refmans/pcalg/html/dag2cpdag.html>`__
+     - `CPDAG <https://search.r-project.org/CRAN/refmans/pcalg/html/dag2cpdag.html>`__, `MPDAG <https://auai.org/uai2017/proceedings/papers/120.pdf>`__, `TMPDAG <https://proceedings.mlr.press/v216/bang23a.html>`__
      - C, D, M
      - 
      - :ref:`Yes <edge_constraints>`

docs/source/structure_learning_algorithms/bips_tpc.rst

@@ -33,7 +33,7 @@ TPC (tpc)
    * - Paper
      - :footcite:t:`andrews2023practicalguidecausaldiscovery`
    * - Graph type
-     - `CPDAG <https://search.r-project.org/CRAN/refmans/pcalg/html/dag2cpdag.html>`__
+     - `CPDAG <https://search.r-project.org/CRAN/refmans/pcalg/html/dag2cpdag.html>`__, `MPDAG <https://auai.org/uai2017/proceedings/papers/120.pdf>`__, `TMPDAG <https://proceedings.mlr.press/v216/bang23a.html>`__
    * - MCMC
      - No
    * - Edge constraints

workflow/rules/structure_learning_algorithms/bips_tpc/info.json

@@ -9,7 +9,7 @@
     "docs_url": "https://github.com/bips-hb/tpc",
     "outputs": "adjmat",
     "graph_types": [
-        "CPDAG"
+        "CPDAG", "MPDAG", "TMPDAG"
     ],
     "meta_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.",
     "data_types": [