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Overview

This package provides efficient algorithms for fitting linear / logistic regression model with Independently Interpretable Lasso.

Installation

To install: install.packages("iilasso")

References

Takada, M., Suzuki, T., & Fujisawa, H. (2018). Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables. AISTATS. http://proceedings.mlr.press/v84/takada18a/takada18a.pdf

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Version

Install

install.packages('iilasso')

Monthly Downloads

16

Version

0.0.1

License

MIT + file LICENSE

Maintainer

Masaaki TAKADA

Last Published

April 16th, 2018

Functions in iilasso (0.0.1)

plot_lasso

Plot a solution path
predict_lasso

Predict responses
softThresholdC

soft thresholding function
covC

calculate covariance matrix
cv_lasso

Fit a model using a design matrix with cross validation
covCdaC

Optimize a linear regression model by coordinate descent algorithm using a covariance matrix
soft_threshold

soft thresholding function
logitCdaC

Optimize a logistic regression model by coordinate descent algorithm using a design matrix
cov_cda_r2

(Experimental) Optimize a ULasso linear regression model by coordinate descent algorithm using a covariance matrix with R
logit_lasso

Fit a logistic regression model using a design matrix
cov_cda_r

Optimize a linear regression model by coordinate descent algorithm using a covariance matrix with R
updateLassoC

update rule function
setup_lambda

Set up a lambda sequence
covCdaC2

(Experimental) Optimize an ULasso linear regression problem by coordinate descent algorithm using a covariance matrix
logitCdaC2

(Experimental) Optimize an ULasso logistic regression problem by coordinate descent algorithm using a design matrix
lasso

Fit a model using a design matrix
plot_cv_lasso

Plot a cross validation error path
logit_cda_r

Optimize a logistic regression model by coordinate descent algorithm using a design matrix with R
update_lasso

update rule function
cov_lasso

Fit a linear regression model using a covariance matrix
logit_cda_r2

(Experimental) Optimize a ULasso logistic regression model by coordinate descent algorithm using a design matrix with R