Package HMDA updated to version 0.3.0 with previous version 0.2.0 dated 2026-02-16

Wed, 04 Mar 2026 07:02:00 CDT

Title: Holistic Multimodel Domain Analysis for Exploratory Machine Learning
Description: Holistic Multimodel Domain Analysis (HMDA) is a robust and transparent framework designed for exploratory machine learning research, aiming to enhance the process of feature assessment and selection. HMDA addresses key limitations of traditional machine learning methods by evaluating the consistency across multiple high-performing models within a fine-tuned modeling grid, thereby improving the interpretability and reliability of feature importance assessments. Specifically, it computes Weighted Mean SHapley Additive exPlanations (WMSHAP), which aggregate feature contributions from multiple models based on weighted performance metrics. HMDA also provides confidence intervals to demonstrate the stability of these feature importance estimates. This framework is particularly beneficial for analyzing complex, multidimensional datasets common in health research, supporting reliable exploration of mental health outcomes such as suicidal ideation, suicide attempts, and other psychological cond [...truncated...]
Author: E. F. Haghish [aut, cre, cph]
Maintainer: E. F. Haghish <haghish@hotmail.com>

Diff between HMDA versions 0.2.0 dated 2026-02-16 and 0.3.0 dated 2026-03-04

 DESCRIPTION              |    8 ++++----
 MD5                      |   22 ++++++++++++----------
 NAMESPACE                |    2 ++
 R/hmda.best.models.R     |   10 +++++-----
 R/hmda.domain.R          |    7 +++----
 R/hmda.grid.R            |    2 ++
 R/hmda.plot.R            |   16 ++++++++--------
 R/hmda.plot.metrics.R    |   44 +++++++++++++++++++++++---------------------
 R/hmda.row.plot.R        |only
 man/hmda.best.models.Rd  |   10 +++++-----
 man/hmda.domain.Rd       |    7 +++----
 man/hmda.plot.metrics.Rd |   31 +++++++++++++++++--------------
 man/hmda.row.plot.Rd     |only
 13 files changed, 84 insertions(+), 75 deletions(-)

More information about HMDA at CRAN