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CorReg (version 1.2.17)

Linear Regression Based on Linear Structure Between Variables

Description

Linear regression based on a recursive structural equation model (explicit multiples correlations) found by a M.C.M.C.(Markov Chain Monte Carlo) algorithm. It permits to face highly correlated variables. Variable selection is included (by lasso, elastic net, etc.). It also provides some graphical tools for basic statistics. For more information about the method, read the PhD thesis of Clement Thery (2015) in the link below.

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Version

Install

install.packages('CorReg')

Monthly Downloads

199

Version

1.2.17

License

CeCILL

Maintainer

Last Published

February 20th, 2020

Functions in CorReg (1.2.17)

Terminator

Destructing values to have missing ones
readZ

Read the structure and explain it
correg

Linear regression using CorReg's method, with variable selection.
cleanZtest

Clean Z's columns based on p-values (coefficients or global)
cleanZ

clean the structure of correlations Z (if BIC improved)
compare_struct

To compare sub-regression structures
purge_values

Replaces unwanted values by NAs
density_estimation

BIC of estimated marginal gaussian mixture densities
matplot_zone

Matplot with curves comparison by background colors.
mixture_generator

Gaussian mixtures dataset generator with regression between the covariates
naive_model

How would it be if we were naive ?
recursive_tree

Decision tree in a recursive way
showdata

To show the missing values of a dataset
report_MSE

Quickly reports some MSE
structureFinder

MCMC algorithm to find a structure between the covariates
Conan

Removes missing values (rows and column to obtain a large full matrix)
Numeric_Only

To clean non numeric values in a vector
BicZ

Compute the BIC of a given structure
ProbaZ

Probability of Z without knowing the dataset. It also gives the exact number of binary nilpotent matrices of size p.
CorReg-package

Quick tutorial for CorReg package
BoxPlot

Boxplot with confidence interval and ANOVA on the plot.
Hist

Histograms with clusters
MSE_loc

Simple MSE function
CVMSE

Cross validation