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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

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, in case of issue when upgrading to this version, please consult the help of the relevant function for more information about its interface.

For the core miic() function, the main breaking changes in the interface (when upgrading from the 1.5.3 release on CRAN) are:

in the parameters:

  • cplx: renaming of the complexity term "mdl" → "bic"

  • ori_proba_ratio → ort_proba_ratio

in the miic object returned:

  • all.edges.summary → summary

    • Nxy_ai → n_xy_ai
    • log_confidence → info_shifted
    • infOrt → ort_inferred
    • trueOrt → ort_ground_truth
    • isOrtOk → is_inference_correct
    • isCausal → is_causal
    • proba → p_y2x, p_x2y
    • consensus → ort_consensus

  • orientations.prob → triples

    • NI3 → ni3
    • Error → conflict

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.

v2.0.0

Features

  • tMIIC version for temporal causal discovery on stationary time series: new mode of miic() to reconstruct networks from temporal stationary datasets (Simon et al., eLife 2024).
    The temporal mode of miic() is not activated by default and can be enabled by setting the newly added parameter mode to "TS"(Temporal Stationary). A tuning of the temporal mode is possible through a set of new parameters: max_nodes, n_layers, delta_t, mov_avg and keep_max_data.

v1.8.1

Fixes and improvements

  • The discretization of continuous variables has been improved when dealing with variables having a large number of identical values.

  • Fix for memory overflow on shared memory space.

v1.8.0

Features

  • Addition of the 'is consequence' prior knowledge. Consequence variables are excluded from the possible contributors, edges between consequences are ignored and edges between a non consequence and a consequence are pre-oriented toward the consequence.
    Information about consequence variables can be provided to miic() in the state_order, by supplying an is_consequence column.

v1.7.0

Features

  • iMIIC version introducing contextual variables, genuine vs putative causes and multiple enhancements to deal with very large datasets (Ribeiro-Dantas et al., iScience 2024).
    Information on contextual variables can be provided to miic() in the state_order, by supplying an is_contextualcolumn and genuine vs putative causes can be tuned by the newly added parameter ort_consensus_ratio.

v1.6.0

Features

  • Enhancement of orientations using mutual information supremum principle for finite datasets (Cabeli et al., Why21 at NeurIPS 2021).
    The use of enhanced orientations is controlled by the newly added parameter negative_info of miic() and is activated by default.

  • By default, miic() does not propagate orientations anymore and allows latent variables discovery during orientation step.

v1.5.3

Features

  • Release to CRAN

v1.5.2

Fixes and improvements

  • Further refactoring of the C++ code for the computation of information.

  • Fix minor bugs in the continuous computation.

  • Fix incompatibility with older versions of GCC (std::align).

v1.5.1

Fixes and improvements

  • Fix various bugs in the computation of information in the presence of NA values in the dataset.

  • An overhaul of the C++ code base, better memory management, computation time and code readability.

v1.5.0

Features

  • Add a column consensus to the reconstructed graph's edges summary associated with the option consistent, and a new parameter consensus_threshold accordingly.

  • Add a parameter ori_proba_ratio to have more control on the orientation of edges.

Fixes and improvements

  • Faster post processing in R.

  • Rework plot functionality.

  • Fix a bug in the orientation part about the log score.

  • Refactor of the C++ code base (orientation).

v1.4.2

Fixes and improvements

  • Various fixes of memory leaks and ambiguous function calls (at least for all that appear in CRAN check).

  • Refactor of the C++ code base (confidence cut).

Known issues

  • Error when running the cosmicCancer example on CRAN's Solaris system.

Miscellaneous

  • Move from BitBucket to GitHub, the repo is now public.

v1.4.1

Not released. A failed attempt to fix the check errors (though submitted to CRAN).

v1.4.0

Incompatible changes

  • Standardize the API naming convention: snake_case for parameters and camelCase for functions. This should have led to a major version increment to v2.0.0 given the previous version on CRAN is v1.0.3. But v1.0.3 and earlier versions were not properly maintained and versioned under a version control system (so we actually forgot to take them into consideration when releasing this version).

Features

  • The method now works with continuous variables (solely or mixed with discrete variables), thanks to the discretization method as described in Cabeli et al., PLoS Comput. Biol. 2020.

  • Add an option consistent to improve the reconstructed graph's interpretability based on schemes as described in Li et al., NeurIPS 2019.

Fixes and improvements

  • Various fixes of memory leaks and typos.

  • Major refactoring of the old C++ code base (still WIP) to improve readability and flexibility, and to enforce proper coding style and documentation.

  • Enforce proper coding style for the R code base.

Known issues

  • Still have some memory leaks and CRAN check errors and notes on certain platforms.