iilasso v0.0.1
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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 | |
| 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 | |
| No Results! | ||
Vignettes of iilasso
| Name | ||
| introduction.Rmd | ||
| No Results! | ||
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Details
| Type | Package |
| Date | 2018-04-16 |
| 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 | 2018-04-15 15:13:14 UTC; takada |
| Repository | CRAN |
| Date/Publication | 2018-04-16 11:31:06 UTC |
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