# iilasso v0.0.2

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## Independently Interpretable Lasso

Efficient algorithms for fitting linear / logistic regression model with Independently Interpretable Lasso. 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>.

## 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

## Functions in iilasso

 Name Description logitCdaC Optimize a logistic regression model by coordinate descent algorithm using a design matrix softThresholdC soft thresholding function update_lasso 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 covC calculate covariance matrix cv_lasso Fit a model using a design matrix with cross validation cov_cda_r Optimize a linear regression model by coordinate descent algorithm using a covariance matrix with R lasso Fit a model using a design matrix covCdaC Optimize a linear regression model by coordinate descent algorithm using a covariance matrix logit_lasso Fit a logistic regression model using a design matrix plot_cv_lasso Plot a cross validation error path plot_lasso Plot a solution path predict_lasso Predict responses logitCdaC2 (Experimental) Optimize an ULasso logistic regression problem by coordinate descent algorithm using a design matrix soft_threshold soft thresholding function cov_cda_r2 (Experimental) Optimize a ULasso linear regression model by coordinate descent algorithm using a covariance matrix with R logit_cda_r2 (Experimental) Optimize a ULasso logistic regression model by coordinate descent algorithm using a design matrix with R cov_lasso Fit a linear regression model using a covariance matrix updateLassoC update rule function logit_cda_r Optimize a logistic regression model by coordinate descent algorithm using a design matrix with R No Results!