v4.99.0

MetaProViz: METabolomics pre-PRocessing, functiOnal analysis and VIZualisation
This is a package release v4.99.0 where we:

• Extended our vignette collection with new tutorials on metabolite ID processing, MetSigDB and prior knowledge networks, and updated all vignettes to the current API, including a new tutorials overview on the website
• Made the individual processing() steps available as standalone functions (feature_filtering(), mvi_imputation(), tic_norm(), core_norm(), outlier_detection()), all supporting SummarizedExperiment objects
• Added a "Manual" feature filtering mode to processing() and feature_filtering()
• Implemented a novel id_processing() function for automated and customizable processing of metabolite IDs in the feature metadata
• Updated and improved the example datasets, e.g. SummarizedExperiment versions with complete feature metadata
• Offer new network visualizations, viz_pk_network() and viz_shared_pk_network(), to visualize connections of metabolites to prior knowledge such as MetSigDB
• Removed the qcc dependency and fixed a range of bugs in the package, its build and its dependencies, reaching higher stability

MetaProViz can analyse standard metabolomics and exometabolomics data (CoRe). It performs pre-processing including feature filtering, missing value imputation, normalisation and outlier detection. It perform functional analysis including differential metabolite analysis (DMA), clustering based on regulatory rules (MCA) and contains different visualisation methods to extract biological interpretable graphs and saves them in a publication ready format.