Changes in version 1.1.1 (2025-12-11)                  

  - Bugs in the SEMml() function were fixed to address the update of
    xboost v3.1.2.1.

  - Added new function trainingReport(). Display a (r,c) panel history
    plot from SEMdnn() output, for each MLP model (if algo="nodewise or
    "layerwise") and bootstrap sample (if nboot > 0), in order to check
    the model's learning process.

                 Changes in version 1.1.0 (2025-11-10)                  

  - All new and revised torch functions no longer require the "cito"
    package.

  - Added new argument algo =
    c("nodewise","layerwise","structured","neuralgraph") to the SEMdnn()
    function. Four algorithms are now implemented using R MLPs (number
    of nodes with non-zero incoming connectivity) for "nodewise", L<R
    MLPs (number of layers in the input graph) for "layerwise", and 1
    MLP for "structured" or "neuralgraph".

  - Delete algo="nn" and algo="dnn" in the SEMml() function. These NNs
    for a small neural network model (1 hidden layer and 10 nodes) and
    large neural network model (1 hidden layers and 1000 nodes) run with
    algo="nodewise" of the SEMdnn() function.

  - Added new function getLOCO(). For SEMml()
    algo=c("sem","tree","rf","xgb"), computes the contributions of each
    variable to individual predictions using LOCO (Leave Out COvariates)
    values based on ghost variables. A CPU-efficient procedure.

  - Various fixed bugs discovered after the release 1.0.0.

                 Changes in version 1.0.0 (2025-01-23)                  

  - Version 1.0.0 is a major release with several new features,
    including:

  - Added new argument outcome = NULL (defult). This parameter is used
    in SEMdnn() and SEMml() functions to process a sink categorical node
    (as a factor) for classification purposes using all graph nodes as
    covariates.

  - Added new argument newoutcome = NULL (defult). This parameter is
    used in predict (.SEM, .DNN, .ML) functions to predict a sink
    categorical node (as a factor) for classification purposes using all
    graph nodes as covariates.

  - classificationReport() function. A report showing the main
    classification metrics, like precision, recall, F1-score, accuracy,
    Matthew's correlation coefficient (mcc) for all classes of the node
    = as.factor(outcome).

  - crossValidation() function. A R-repeated K-fold cross-validation
    with a list of M models from SEMrun(), SEMml() and SEMdnn(). The
    winning model is selected by reporting the mean predicted
    performances across all RxKxM runs.

  - getVariableImportance() function. Extraction of common Machine
    Learning (ML) variable (predictor) importance measures after fitting
    SEMrun(), SEMml() or SEMdnn() models.

  - Added new argument nboot = 0 (default). This parameter implements
    cheap bootstrapping in SEMdnn() and SEMml() functions to generate
    uncertainties, i.e. CIs, for DNN/ML parameters. Bootstrapping can be
    enabled by setting a small number (from 1 to 10) of bootstrap
    samples.

  - Change argument thr = 0.5 * max(abs(parameters)) (default). Now the
    DAG can be colored using a numeric [0-1] threshold. For
    example, 1/0.5 = 2, can be interpreted as the number of times a
    node/edge parameter is less than the maximum parameter value.

  - Various fixed bugs discovered after the release 0.1.0.

                 Changes in version 0.1.0 (2024-09-16)                  

  - First stable version on CRAN