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>.

Readme

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
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Vignettes of iilasso

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introduction.Rmd
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Details

Type Package
Date 2018-06-21
License MIT + file LICENSE
LinkingTo Rcpp, BH
RoxygenNote 6.0.1
VignetteBuilder knitr
URL http://proceedings.mlr.press/v84/takada18a/takada18a.pdf
NeedsCompilation yes
Packaged 2018-06-21 14:13:32 UTC; takada
Repository CRAN
Date/Publication 2018-06-21 16:52:35 UTC

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