Provide methods to perform customized inference at individual level by taking
contextual covariates into account. Three main functions are provided
in this package: (i) LASER(): it generates specially-designed artificial relevant
samples for a given case; (ii) g2l.proc(): computes customized fdr(z|x); and (iii)
rEB.proc(): performs empirical Bayes inference based on LASERs. The details can be
found in Mukhopadhyay, S., and Wang, K (2021, <arXiv:2004.09588>).

Linear Predictor Score, for Binary Inference from Multiple
Continuous Variables
An implementation of the Linear Predictor Score approach, as initiated by Radmacher et al. (J Comput Biol 2001) and enhanced by Wright et al. (PNAS 2003) for gene expression signatures. Several tools for unsupervised clustering of gene expression data are also provided.

Linear Predictor Score, for binary inference from multiple
continuous variables.
This package provides functions implementing the Linear Predictor Score approach, as initiated by Radmacher et al. (J Comput Biol 2001) and enhanced by Wright et al. (PNAS 2003). Given a set of continuous variables (typically gene expression values), it builds a simple score able to retrieve previously defined classes.

Learned Pattern Similarity and Representation for Time Series
Learned Pattern Similarity (LPS) for time series by Mustafa Gokce Baydogan,
Ensemble of regression trees by Andy Liaw and Matthew Wiener.
Learned Pattern Similarity (LPS) for time series. This package is based on the
'randomForest' package by Andy Liaw. LPS aims at finding time series patterns to
compute the similarity.

Learned Pattern Similarity (LPS) for time series by Mustafa Gokce Baydogan
Learned Pattern Similarity (LPS) for time series.
Implements a novel approach to model the dependency structure
in time series that generalizes the concept of autoregression to local
auto-patterns. Generates a pattern-based representation of time series
along with a similarity measure called Learned Pattern Similarity (LPS).
Introduces a generalized autoregressive kernel.This package is based on the
'randomForest' package by Andy Liaw.