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glmaag (version 0.0.6)

Adaptive LASSO and Network Regularized Generalized Linear Models

Description

Efficient procedures for adaptive LASSO and network regularized for Gaussian, logistic, and Cox model. Provides network estimation procedure (combination of methods proposed by Ucar, et. al (2007) and Meinshausen and Buhlmann (2006) ), cross validation and stability selection proposed by Meinshausen and Buhlmann (2010) and Liu, Roeder and Wasserman (2010) methods. Interactive R app is available.

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Version

Install

install.packages('glmaag')

Monthly Downloads

24

Version

0.0.6

License

MIT + file LICENSE

Maintainer

Kaiqiao Li

Last Published

May 10th, 2019

Functions in glmaag (0.0.6)

coef.cv_glmaag

Coefficients
tune_network

tune two network
coef.ss_glmaag

Coefficients for ss_glmaag
plot.ss_glmaag

Instability plot
predict.cv_glmaag

Predict
cv_glmaag

Cross validation for glmaag
evaluate

Evaluate prediction
evaluate_plot

Prediction visualization
glmaag

Fit glmaag model
L0

sample network 0
laps

Standardized Laplacian matrix
runtheExample

Shiny app
plot.cv_glmaag

Cross validation plot
L1

sample network 1
sampledata

Simulated data
plot.glmaag

Paths for glmaag object
getS

Estimate standardized Laplacian matrix
getcut

Get optimal cut points for binary or right censored phenotype
print.cv_glmaag

the results of the cross validation model
print.ss_glmaag

the results of the stability selection model
coef.glmaag

Coefficients for glmaag
ss_glmaag

Stability selection for glmaag
predict.glmaag

Prediction for glmaag
predict.ss_glmaag

Prediction via stability selection