Provides a collection of features, decomposition methods, 
    statistical summaries and graphics functions for the analysing tidy time
    series data. The package name 'feasts' is an acronym comprising of its key
    features: Feature Extraction And Statistics for Time Series.

Define feature logic, compute only new or unprocessed
    rows, and persist the resulting flat feature table in a database.
    The package provides an explicit incremental pipeline for fetching
    source rows, computing feature definitions, and writing computed
    features to a database table.

Automated feature engineering functions tailored for credit scoring. It includes utilities for extracting structured features from timestamps, IP addresses, and email addresses, enabling enhanced predictive modeling for financial risk assessment.

Read and write feather files, a lightweight binary columnar
    data store designed for maximum speed.

Local inferential feature significance for multivariate kernel density estimation.

Uses three different correlation coefficients to
    calculate measurement-level adequate correlations in a feature matrix: 
    Pearson product-moment correlation coefficient,
    Intraclass correlation and Cramer's V.

An R interface for generating features for a cohort using data in the Common Data Model. Features can be constructed using default or custom made feature definitions. Furthermore it's possible to aggregate features and get the summary statistics.

Finds features through a detailed analysis of model residuals using rpart classification and regression trees. Scans the residuals of a model across subsets of the data to identify areas where the model differs from the actual data.

Feature flags allow developers to turn features of their
    software on and off in form of configuration. This package provides
    functions for creating feature flags in code. It exposes an interface
    for defining own feature flags which are enabled based on custom criteria.

Implements a novel approach for measuring feature importance in k-means clustering. Importance of a feature is measured by the misclassification rate relative to the baseline cluster assignment due to a random permutation of feature values. An explanation of permutation feature importance in general can be found here: <https://christophm.github.io/interpretable-ml-book/feature-importance.html>.