camel: Calibrated Machine Learning

The package "camel" provides the implementation of a family of high-dimensional calibrated machine learning tools, including (1) LAD, SQRT Lasso and Calibrated Dantzig Selector for estimating sparse linear models; (2) Calibrated Multivariate Regression for estimating sparse multivariate linear models; (3) Tiger, Calibrated Clime for estimating sparse Gaussian graphical models. We adopt the combination of the dual smoothing and monotone fast iterative soft-thresholding algorithm (MFISTA). The computation is memory-optimized using the sparse matrix output, and accelerated by the path following and active set tricks.

Install the latest version of this package by entering the following in R:
AuthorXingguo Li, Tuo Zhao, and Han Liu
Date of publication2013-09-09 15:49:04
MaintainerXingguo Li <>

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camel Man page
camel.cmr Man page
camel.cmr.mfista Man page
camel-package Man page
camel.plot Man page
camel.slim Man page
camel.slim.dantzig.mfista Man page
camel.slim.lad.mfista Man page
camel.slim.sqrt.mfista Man page
camel.tiger Man page
camel.tiger.clime.mfista Man page Man page
camel.tiger.generator Man page
camel.tiger.roc Man page Man page
camel.tiger.slasso.mfista Man page
eyedata Man page Man page
plot.roc Man page Man page
plot.sim Man page
plot.slim Man page
plot.tiger Man page
print.cmr Man page
print.roc Man page Man page
print.sim Man page
print.slim Man page
print.tiger Man page
tiger.likelihood Man page
tiger.tracel2 Man page

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