dbnR 0.7.5

Deprecated the ‘size’ argument in ‘forecast_ts’ and ‘smooth_ts’.
A named vector is no longer the only option required for the ‘mvn_inference’ function. It now also takes a single row data.table and makes the conversion to named vector inside. This avoids the user needing to have an external auxiliary function.
New security check to confirm that a data.table has only one row. This is also needed in the inference, because we can only provide one row of evidence. It gave a non-descriptive error before, now it warns about using more than one row in the evidence data.table.

dbnR 0.7.4

Added the possibility of plotting only a subset of the nodes with the visualization tool.
Unified the ‘plot_network’ function for visualizing BNs and the ‘plot_dynamic_network’ function for visualizing DBNs into a single one.
More informative error messages in security_checks.R when dealing with ellipsis.

Added the possibility of passing a blacklist_tr parameter to the dmmhc algorithm in order to avoid certain inter-slice arcs.

Added authors and doi of the structure learning algorithms to the description field as per request of CRAN maintainers.

Fixed the create_blacklist method for sizes greater than 10. A regex “$” was missing.

Added variable inertia, global best and local best parameters over time to the PSOHO algorithm
Now filtered_fold_dt() allows a boolean argument to avoid deleting the id_var column before returning the folded dataset

Added the possibility of doing smoothing over a time series
Fixed a bug that returned NaNs when predicting 1 variable in t_0 and providing all other variables as evidence

Parameter for different bnlearn scores in the PSOHO algorithm
New Jupyter notebook that shows how to integrate dbnR with Python
Fixed the ‘intra’ argument in the mmhc algorithm. The ‘if’ statements regarding it were wrong.
Several fixes in compatibility with ‘as.data.table’ calls

Hotfix for the ‘f_dt’ parameter in the ‘learn_dbn_struc’ function. It was not being passed down to each algorithm.
New auxiliary function ‘filtered_fold_dt’
Added the possibility of not learning intra-slice arcs in the dmmhc algorithm

Removed obsolete and time consuming security checks. The psoho initial checks were supposed to secure the separate usage of the ‘Position’ and ‘Velocity’ classes, but they are not exported, and consequently they cannot be misused in the sense that the checks prevented. They also were O(n) on the size of the ordering, and were run each time some operation was performed over positions or velocities.

Additional security checks for the PSOHO algorithm
Added an auxiliary function to reduce the frequency in a data.table by performing the mean of consecutive rows
Added an auxiliary function to fold a dataset and not allow instances from different labeled series to be in the same row
Added the possibility to introduce a previously folded dataset to the structure learning. This helps when the dataset has several iterations of the same process separated by id. If you fold the dataset directly, data from 2 or more different processes will appear in some rows, giving the impression to the model that the ending and the beginning of the processes merge into each other, which most likely is not true

Fixed the lack of imports from the ‘R6’ package with @importFrom, NOTE in CRAN checks results

Fixed the visualization of dynamic networks with no intra-slice arcs. The nodes were plotted in a horizontal line, now they are shown in vertical lines in each time slice

Dropped std:vector in favor of Rcpp::Numeric/StringVector in gauss_transform.cpp
Fixed a security check typo

Fixed the dependency on R (>= 3.5.0) rather than R (>= 3.6.0). CRAN checks for old released R versions failed because of this. The only difference is that running the package with R (< 3.6.0) displays the plot_dynamic_network() with a different color palette.