Achemkhi/constant_columns_check_bug_fix

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: miic
 Title: Learning Causal or Non-Causal Graphical Models Using Information Theory
-Version: 2.0.3
+Version: 2.1.0
 Authors@R:
     c(person(given = "Franck",
              family = "Simon",
@@ -44,7 +44,7 @@ Authors@R:
              family = "Isambert",
              role = "aut",
              email = "herve.isambert@curie.fr"))
-Description: Multivariate Information-based Inductive Causation, better known 
+Description: Multivariate Information-based Inductive Causation, known 
     by its acronym MIIC, is a causal discovery method, based on information 
     theory principles, which learns a large class of causal or non-causal 
     graphical models from purely observational data, while including the effects 
@@ -62,21 +62,24 @@ Description: Multivariate Information-based Inductive Causation, better known
     genomic alterations in tumors, live-cell time-lapse imaging data 
     (CausalXtract), as well as medical records of patients. MIIC brings unique 
     insights based on causal interpretation and could be used in a broad range 
-    of other data science domains (technology, climatology, economy, ...). 
+    of other data science domains (technology, climatology, economy, ...).
+    The latest version 2.1 introduces methods to perform features selection 
+    around variable(s) of interest or on the path between variables of interest.
     For more information, you can refer to: 
-    Simon et al., eLife 2024, <doi:10.1101/2024.02.06.579177>, 
+    Simon et al., eLife 2024, <doi:10.7554/eLife.95485.3>, 
     Ribeiro-Dantas et al., iScience 2024, <doi:10.1016/j.isci.2024.109736>, 
     Cabeli et al., NeurIPS 2021, <https://why21.causalai.net/papers/WHY21_24.pdf>, 
     Cabeli et al., Comput. Biol. 2020, <doi:10.1371/journal.pcbi.1007866>, 
     Li et al., NeurIPS 2019, <https://papers.nips.cc/paper/9573-constraint-based-causal-structure-learning-with-consistent-separating-sets>, 
     Verny et al., PLoS Comput. Biol. 2017, <doi:10.1371/journal.pcbi.1005662>, 
     Affeldt et al., UAI 2015, <https://auai.org/uai2015/proceedings/papers/293.pdf>. 
-    Changes from the previous 1.5.3 release on CRAN are available at 
+    Changes from the previous 2.0.3 release on CRAN are available at 
     <https://github.com/miicTeam/miic_R_package/blob/master/NEWS.md>.
 License: GPL (>= 2)
 URL: https://github.com/miicTeam/miic_R_package
 BugReports: https://github.com/miicTeam/miic_R_package/issues
 Imports:
+    lifecycle,
     ppcor,
     Rcpp,
     scales,

NAMESPACE

@@ -9,9 +9,13 @@ export(discretizeMutual)
 export(estimateTemporalDynamic)
 export(export)
 export(miic)
+export(selectFeatures)
+export(selectFeaturesPath)
+export(tMiicStat)
 export(writeCytoscapeNetwork)
 export(writeCytoscapeStyle)
 import(Rcpp)
+importFrom(lifecycle,deprecate_warn)
 importFrom(stats,density)
 importFrom(stats,sd)
 useDynLib(miic)

NEWS.md

@@ -1,3 +1,23 @@
+# v2.1.0
+
+## Features
+
+* new featuresSelection() and featuresSelectionPath() to perform features 
+  selection around variable(s) of interest or on the path between variables 
+  of interest.
+
+* introduction of tMiicStat() dedicated to causal discovery on stationary 
+  time series. 
+
+* miic() is refocused on non temporal datasets. 
+  The mode and associated temporal parameters are deprecated.
+
+## Fixes and improvements
+
+* miic() verbosity extended with no display level.  
+
+* estimateTemporalDynamic() has more detailed returned value.
+
 # v2.0.3
 
 ## Features