promor 0.2.0

promor 0.2.0

New data types allowed

• A new argument (data_type) added to the create_df function to
  accommodate other types of LFQ data (raw intensity, iBAQ).

A new input type added

• A new argument input_type added to the create_df function to
  allow users to input data from a standard quantitative matrix.

Workflow changes

• To allow for missing data imputation prior to or after data normalization
  step (depending on the imputation method used), the following changes were made:
  • norm_df and imp_df arguments replaced with a generic df argument in
    the functions, find_dep, impute_na, normalize_data, and heatmap_de
  • A note was added to the tutorials to clarify that for some imputation
    methods, such as the kNN method, data normalization should be performed prior
    to imputation.

Default machine learning algorithms

• naive_bayes added to the default algorithm_list argument in the
  train_models function.