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