rMIDAS 1.0.1

• Marked the package as deprecated and directed users to rMIDAS2
• Added a migration vignette and startup/package documentation notices to support existing users
• Limited test-time thread usage to avoid CRAN check CPU-time inflation on Debian

rMIDAS 1.0.0

• To mark the publication of our article in the Journal of Statistical Software (see citation("rMIDAS")), we are releasing our first stable release!
• Minor documentation changes to reflect this publication

v0.5.0

• rMIDAS now includes an automatic setup that prompts the user on whether to automatically set up a Python environment and its dependencies
• Addressed dependency issues and deprecation warnings (rather a Python update than R)
• An additional .Rmd example that showcases rMIDAS core functions
• Added a new vignette for running rMIDAS in headless mode, along with updates to the existing vignettes
• Updated the accompanying YAML environment file that works on all major operating systems (including macOS running Apple silicon hardware)
• Expanded our GitHub Actions workflow to also perform R-CMD-checks on macOS and Windows systems
• Updated README file

v0.4.2

• Added headless functionality to matplotlib calls in Python
• Updated conda setup file
• Minor updates to underlying Python code to address deprecation issues

v0.4.1

• Disabled Tensorflow deprecation warnings as default (as Python rather than R warning)
• Updated accompanying YAML for easier Conda setup
• Added no-binary pip install to YAML to resolve BLAS issues on Macs

v0.4

• python argument in set_python_env renamed to x for clarity
• Minor fixes including remedying bug in complete() function
• Improved documentation

rMIDAS 0.3

• Minor updates to underlying Python code to mirror MIDASpy v1.2.1
• Added NULL defaults to cat_cols and bin_cols parameters within rMIDAS::convert()
• Overimputation legend now plotted in bottom-right corner of figure
• Minor changes to README

rMIDAS 0.2

• rMIDAS now fully supports both Tensorflow 1.X and 2.X
• Added two vignettes for demonstrating imputation workflow and configuring Python installs/environments
• Streamlined handling of Python configuration and interface with reticulate
• Added a fast parameter to the complete() function, giving users more flexibility on how to handle predicted probabilities for categorical and binary variables.
• Added function add_missingness() to spike-in missingness for examples
• Minor changes to README
• Minor changes to DESCRIPTION including title and description fields
• Replaced all instances of cat() with message() for better logging
• Bug fixes related to GitHub issues

rMIDAS 0.1

• First release including all core functionality
• VAE and overimputation diagnostic tests included
• Easy to use pre/post-processing of data
• Multiple imputation wrapper of `glm()’ for in-built analysis of completed data