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