cFMDbench v1.0.1

Summary

This patch release improves the reproducibility of the cFMDbench Conda/R setup and fixes issues encountered when rendering the Quarto workflow with current conda-forge and mlr3 packages.

The default installation path now focuses on the standard ranger and xgboost workflow, while optional backends such as R torch/mlr3torch, TabPFN/mlr3extralearners remain explicit opt-ins.

Changes

• Reworked the R package installer to use a Conda-first strategy for compiled dependencies.
• Removed broad dependencies = TRUE installation behavior.
• Kept optional learner ecosystems out of the default install path.
• Added explicit install flags for optional backends:
  • INSTALL_R_TORCH=1
  • INSTALL_TABPFN_R=1
• Hardened the Quarto notebook for server rendering.
• Improved project-root detection during Quarto runs.
• Made package loading deterministic with explicit conflict preferences.
• Disabled global progressr handlers during Quarto rendering.
• Set xgboost explicitly to booster = "gbtree" for compatibility with current mlr3learners/paradox validation.
• Updated README and Conda setup documentation.

Recommended Default Workflow

For the default ranger and xgboost analysis:

conda env create -f conda/cfmdbench-r.yml
conda activate cfmdbench-r
Rscript conda/install_r_packages.R
quarto render analysis/cFMDbench.qmd