  The goal of the linear law-based feature space transformation (LLT) algorithm
is to assist with the classification of univariate and multivariate time
series. The presented R package, called LLT, implements this algorithm in a
flexible yet user-friendly way. This package first splits the instances into
training and test sets. It then utilizes time-delay embedding and spectral
decomposition techniques to identify the governing patterns (called linear
laws) of each input sequence (initial feature) within the training set.
Finally, it applies the linear laws of the training set to transform the
initial features of the test set. These steps are performed by three separate
functions called trainTest, trainLaw, and testTrans. Their application requires
a predefined data structure; however, for fast calculation, they use only
built-in functions. The LLT R package and a sample dataset with the appropriate
data structure are publicly available on GitHub.
