Learn R Programming

Overview

Provides a tool for non linear mapping (non linear regression) using a mixture of regression model and an inverse regression strategy. The methods include the GLLiM model (see Deleforge et al (2015) \doi{10.1007/s11222-014-9461-5}) based on Gaussian mixtures and a robust version of GLLiM, named SLLiM (see Perthame et al (2016) \doi{10.1016/j.jmva.2017.09.009}) based on a mixture of Generalized Student distributions. The methods also include BLLiM (see Devijver et al (2017) arXiv:1701.07899) which is an extension of GLLiM with a sparse block diagonal structure for large covariance matrices (particularly interesting for transcriptomic data).

Installation

# To get xLLiM from CRAN
install.packages("xLLiM")
library(xLLiM)

Or the development version from GitHub

# install.packages("devtools")
devtools::install_github("epertham/xLLiM", ref = "master")
library(xLLiM)

Copy Link

Version

Install

install.packages('xLLiM')

Monthly Downloads

1,805

Version

2.3

License

GPL (>= 2)

Maintainer

Emeline Perthame

Last Published

October 27th, 2023

Functions in xLLiM (2.3)

xLLiM-package

High Dimensional Locally-Linear Mapping
bllim

EM Algorithm for Block diagonal Gaussian Locally Linear Mapping
gllim_inverse_map

Inverse Mapping from gllim or bllim parameters
gllim

EM Algorithm for Gaussian Locally Linear Mapping
data.xllim.test

Testing data to run examples of usage of gllim_inverse_map and sllim_inverse_map functions
emgm

Perform EM algorithm for fitting a Gaussian mixture model (GMM)
sllim

EM Algorithm for Student Locally Linear Mapping
data.xllim.trueparameters

True parameters used to simulate the datasets data.xllim and data.xllim.test
preprocess_data

A proposition of function to process high dimensional data before running gllim, sllim or bllim
data.xllim

Simulated data to run examples of usage of gllim and sllim functions
sllim_inverse_map

Inverse Mapping from sllim parameters