# multiDEGGs 1.2.0
### Updated reference network
- The reference network internally used by multiDEGGs is now updated with 28,352
  additional links obtained from Omnipath. The internal `omic_network` object contains
  now a total of 53,035 literature-reported biological links.
  Running `get_diffNetworks()` on the same data might now result in differential
  networks containing new additional links.
- New citation added.

# multiDEGGs 1.1.3
Vignettes improvements.

# multiDEGGs 1.1.2
Patch: `get_diffNetworks_singleOmic()` now double checks that `assayData` and 
`metadata` are aligned for the layer 

# multiDEGGs 1.1.1
Minor fixes in documentation

# multiDEGGs 1.1.0
### New features for feature augmentation in ML
Two new functions are provided for nested feature engineering. To use them in 
combination with the `nestedcv` package their name must be passed to the 
`modifyX` parameter of `nestcv.glmnet()` or `nestcv.train()`. 
  
- The `multiDEGGs_filter()` function performs feature selection based entirely 
on differential network analysis. 
- The `multiDEGGs_combined_filter()` function combines traditional statistical
feature selection (5 options) with differential network analysis. 
- Internally the two `predict.multiDEGGs_filter()` and 
`predict.multiDEGGs_combined_filter()` S3 methods generate predictions by 
creating a dataset with single and combined predictors based on the filtering 
results of a `multiDEGGs_filter` model.
- The vignette has been updated to showcase the new feature

# multiDEGGs 1.0.0
### Initial Release
- First public version of `multiDEGGs`
- Provides tools for differential network analysis.
- Can be easily integrated in machine learning pipelines as feature selection method.
- Supports both single and multi omic analyses.
- Compatible with R >= 4.4.
