Package index • nuggets

Data Preparation

Functions for transformation of input data into the format suitable for pattern extraction.

is_almost_constant(): Test whether a vector is almost constant

partition(): Convert columns of a data frame to Boolean or fuzzy sets (triangular, trapezoidal, or raised-cosine)

remove_almost_constant(): Remove almost constant columns from a data frame

dig_tautologies(): Find tautologies or "almost tautologies" in a dataset

Pre-defined Pattern Extraction

Functions for extraction of pre-defined patterns from input data.

dig_associations() experimental: Search for association rules

dig_correlations() experimental: Search for conditional correlations

dig_baseline_contrasts() experimental: Search for conditions that yield in statistically significant one-sample test in selected variables.

dig_complement_contrasts() experimental: Search for conditions that provide significant differences in selected variables to the rest of the data table

dig_paired_baseline_contrasts() experimental: Search for conditions that provide significant differences between paired variables

Custom Pattern Extraction

Functions for extraction of custom patterns from input data.

dig(): Search for patterns of a custom type

dig_grid() experimental: Search for grid-based rules

Pattern Pre- and Post-processing

Functions for pre- and post-processing of extracted patterns.

explore(<associations>) experimental: Show interactive application to explore association rules

geom_diamond(): Geom for drawing diamond plots of lattice structures

remove_ill_conditions(): Remove invalid conditions from a list

which_antichain(): Return indices of first elements of the list, which are incomparable with preceding elements.

Tools and Helper Functions

Other functions that can be useful in the pattern extraction process.

bound_range(): Bound a range of numeric values

fire(): Obtain truth-degrees of conditions

format_condition(): Format a vector of predicates into a condition string

is_condition(): Check whether a list of character vectors contains valid conditions

is_degree(): Test whether an object contains numeric values from the interval \([0,1]\)

is_nugget(): Test whether an object is a nugget

is_subset(): Determine whether one vector is a subset of another

nugget(): Create a nugget object of a given flavour

parse_condition(): Convert condition strings into lists of predicate vectors

shorten_condition(): Shorten predicates within conditions

values(): Extract values from predicate names

var_grid(): Create a tibble of combinations of selected column names

var_names(): Extract variable names from predicate names