v2.0.3: CRAN release

* MIIC v2.0.2: changes required by CRAN

* MIIC v2.0.3: CRAN release

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: miic
 Title: Learning Causal or Non-Causal Graphical Models Using Information Theory
-Version: 2.0.1
+Version: 2.0.3
 Authors@R:
     c(person(given = "Franck",
              family = "Simon",
@@ -44,34 +44,34 @@ Authors@R:
              family = "Isambert",
              role = "aut",
              email = "[email protected]"))
-Description: MIIC (Multivariate Information-based Inductive Causation) 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 of unobserved latent 
-    variables. Starting from a complete graph, the method iteratively removes 
-    dispensable edges, by uncovering significant information contributions from 
-    indirect paths, and assesses edge-specific confidences from randomization 
-    of available data. The remaining edges are then oriented based on the 
-    signature of causality in observational data. The recent more interpretable 
-    MIIC extension (iMIIC) further distinguishes genuine causes from putative 
-    and latent causal effects, while scaling to very large datasets (hundreds
-    of thousands of samples).Since the version 2.0, MIIC also includes a 
-    temporal mode (tMIIC) to learn temporal causal graphs from stationary time
-    series data. MIIC has been applied to a wide range of biological and 
-    biomedical data, such as single cell gene expression data, 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, ...).  
-    For more information, you can refer to:
-    Simon et al., eLife 2024, <doi:10.1101/2024.02.06.579177>,
-    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 available on CRAN are available at 
+Description: Multivariate Information-based Inductive Causation, better 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 
+    of unobserved latent variables. Starting from a complete graph, the method 
+    iteratively removes dispensable edges, by uncovering significant information 
+    contributions from indirect paths, and assesses edge-specific confidences 
+    from randomization of available data. The remaining edges are then oriented 
+    based on the signature of causality in observational data. The recent more 
+    interpretable MIIC extension (iMIIC) further distinguishes genuine causes 
+    from putative and latent causal effects, while scaling to very large 
+    datasets (hundreds of thousands of samples). Since the version 2.0, MIIC 
+    also includes a temporal mode (tMIIC) to learn temporal causal graphs from 
+    stationary time series data. MIIC has been applied to a wide range of 
+    biological and biomedical data, such as single cell gene expression data, 
+    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, ...). 
+    For more information, you can refer to: 
+    Simon et al., eLife 2024, <doi:10.1101/2024.02.06.579177>, 
+    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 
     <https://github.com/miicTeam/miic_R_package/blob/master/NEWS.md>.
 License: GPL (>= 2)
 URL: https://github.com/miicTeam/miic_R_package
@@ -88,7 +88,6 @@ Suggests:
     gridExtra
 LinkingTo:
     Rcpp
-SystemRequirements: C++14
 LazyData: true
 Encoding: UTF-8
 RoxygenNote: 7.3.2

NEWS.md

@@ -1,24 +1,37 @@
-# v2.0.1
+# v2.0.3
 
 ## Features
 
 * Release to CRAN.
 
+## Known issues
+
+* Conditioning on a (very) large number of contributors can lead to a memory 
+  fault.
+
+# v2.0.2
+
+## Fixes and improvements
+
+* Minor changes to fulfill CRAN requirements.
+
+# v2.0.1
+
 ## Fixes and improvements
 
 * Faster post-processing in R for datasets with large number of variables.
 
 ## Breaking changes
 
-Consolidating long-pending breaking changes:
+Preparation of new release on CRAN, consolidating long-pending breaking changes:
 
 * Harmonization of exported function names using `camel case`.
 
 * Harmonization of parameters and return values using `snake case`.
 
 * Harmonization of abbreviations.
 
-All the documentation has been updated accordingly, if you encounter any issue
+All the documentation has been updated accordingly, in case of issue when
 upgrading to this version, please consult the help of the relevant function
 for more information about its interface.
 
@@ -51,11 +64,6 @@ Still compared to 1.5.3, another important change in the behavior of `miic()`
 is that, by default, `miic()` no longer propagates orientations 
 and allows latent variables discovery during orientation step.
 
-## Known issues
-
-* Conditioning on a (very) large number of contributors can lead to a memory 
-  fault.
-
 # v2.0.0
 
 ## Features

R/computeInformation.R

@@ -38,7 +38,7 @@
 #'
 #' @references
 #' \itemize{
-#' \item Cabeli \emph{et al.}, PLoS Comput. Biol. 2020, \href{https://doi.org/10.1371/journal.pcbi.1007866}{Learning clinical networks from medical records based on information estimates in mixed-type data}
+#' \item Cabeli \emph{et al.}, PLoS Comput. Biol. 2020, \href{https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007866}{Learning clinical networks from medical records based on information estimates in mixed-type data}
 #' \item Affeldt \emph{et al.}, UAI 2015, \href{https://auai.org/uai2015/proceedings/papers/293.pdf}{Robust Reconstruction of Causal Graphical Models based on Conditional 2-point and 3-point Information}
 #' }
 #'
@@ -339,7 +339,7 @@ computeMutualInfo <- function(x, y,
 #'
 #' @references
 #' \itemize{
-#' \item Cabeli \emph{et al.}, PLoS Comput. Biol. 2020, \href{https://doi.org/10.1371/journal.pcbi.1007866}{Learning clinical networks from medical records based on information estimates in mixed-type data}
+#' \item Cabeli \emph{et al.}, PLoS Comput. Biol. 2020, \href{https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007866}{Learning clinical networks from medical records based on information estimates in mixed-type data}
 #' \item Affeldt \emph{et al.}, UAI 2015, \href{https://auai.org/uai2015/proceedings/papers/293.pdf}{Robust Reconstruction of Causal Graphical Models based on Conditional 2-point and 3-point Information}
 #' }
 #'

R/discretizeMutual.R

@@ -37,7 +37,7 @@
 #'
 #' @references
 #' \itemize{
-#' \item Cabeli \emph{et al.}, PLoS Comput. Biol. 2020, \href{https://doi.org/10.1371/journal.pcbi.1007866}{Learning clinical networks from medical records based on information estimates in mixed-type data}
+#' \item Cabeli \emph{et al.}, PLoS Comput. Biol. 2020, \href{https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007866}{Learning clinical networks from medical records based on information estimates in mixed-type data}
 #' }
 #'
 #' @param x [a vector]

R/miic.R

@@ -53,11 +53,11 @@
 #' @references
 #' \itemize{
 #' \item Simon \emph{et al.}, eLife 2024, \href{https://www.biorxiv.org/content/10.1101/2024.02.06.579177v1.abstract}{CausalXtract: a flexible pipeline to extract causal effects from live-cell time-lapse imaging data}
-#' \item Ribeiro-Dantas \emph{et al.}, iScience 2024, \href{https://doi.org/10.1016/j.isci.2024.109736}{Learning interpretable causal networks from very large datasets, application to 400,000 medical records of breast cancer patients}
+#' \item Ribeiro-Dantas \emph{et al.}, iScience 2024, \href{https://arxiv.org/pdf/2303.06423}{Learning interpretable causal networks from very large datasets, application to 400,000 medical records of breast cancer patients}
 #' \item Cabeli \emph{et al.}, NeurIPS 2021, \href{https://why21.causalai.net/papers/WHY21_24.pdf}{Reliable causal discovery based on mutual information supremum principle for finite dataset}
-#' \item Cabeli \emph{et al.}, PLoS Comput. Biol. 2020, \href{https://doi.org/10.1371/journal.pcbi.1007866}{Learning clinical networks from medical records based on information estimates in mixed-type data}
+#' \item Cabeli \emph{et al.}, PLoS Comput. Biol. 2020, \href{https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007866}{Learning clinical networks from medical records based on information estimates in mixed-type data}
 #' \item Li \emph{et al.}, NeurIPS 2019, \href{http://papers.nips.cc/paper/9573-constraint-based-causal-structure-learning-with-consistent-separating-sets.pdf}{Constraint-based causal structure learning with consistent separating sets}
-#' \item Verny \emph{et al.}, PLoS Comput. Biol. 2017, \href{https://doi.org/10.1371/journal.pcbi.1005662}{Learning causal networks with latent variables from multivariate information in genomic data}
+#' \item Verny \emph{et al.}, PLoS Comput. Biol. 2017, \href{https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005662}{Learning causal networks with latent variables from multivariate information in genomic data}
 #' \item Affeldt \emph{et al.}, UAI 2015, \href{https://auai.org/uai2015/proceedings/papers/293.pdf}{Robust Reconstruction of Causal Graphical Models based on Conditional 2-point and 3-point Information}
 #' }
 #'

src/Makevars

@@ -1,5 +1,3 @@
 # openmp support
 PKG_CXXFLAGS = $(SHLIB_OPENMP_CXXFLAGS)
 PKG_LIBS     = $(SHLIB_OPENMP_CXXFLAGS)
-# Require C++14
-CXX_STD = CXX14

src/Makevars.win

@@ -1,5 +1,3 @@
 # openmp support
 PKG_CXXFLAGS = $(SHLIB_OPENMP_CXXFLAGS)
 PKG_LIBS     = $(SHLIB_OPENMP_CXXFLAGS) $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS)
-# Require C++14
-CXX_STD = CXX14