lagshift

lagMax

Generates feature matrix outputs from R object inputs
using a variety of expansion functions.  The generated
feature matrices have applications as inputs
for a variety of machine learning algorithms.
The expansion functions are based on coercing the input
to a matrix, treating the columns as features and
converting individual columns or combinations into blocks of
columns.
Currently these include expansion of columns by
efficient sparse embedding by vectors of lags,
quadratic expansion into squares and unique products,
powers by vectors of degree,
vectors of orthogonal polynomials functions,
and block random affine projection transformations (RAPTs).
The transformations are
magrittr- and cbind-friendly, and can be used in a
building block fashion.  For instance, taking the cos() of
the output of the RAPT transformation generates a
stationary kernel expansion via Bochner's theorem, and this
expansion can then be cbind-ed with other features.
Additionally, there are utilities for replacing features,
removing rows with NAs,
creating matrix samples of a given distribution,
a simple wrapper for LASSO with CV,
a Freeman-Tukey transform,
generalizations of the outer function,
matrix size-preserving discrete difference by row,
plotting, etc.

lagshift: Helper function for eLag.

Description

Usage

Arguments

Value

Examples