Exploring Friedman's Boosting Algorithm for Regularized Linear Regression

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Documentation for package ‘l2boost’ version 1.0.3

• DESCRIPTION file.
• Package NEWS.

Help Pages

l2boost-package	Efficient implementation of Friedman's boosting algorithm for linear regression using an l2-loss function and coordinate direction (design matrix columns) basis functions.
coef.l2boost	Extract model coefficients from an l2boost model object at any point along the solution path indexed by step m. 'coef' is a generic function which extracts model coefficients from objects returned by modeling functions.
cv.l2boost	K-fold cross-validation using 'l2boost'.
diabetes	Blood and other measurements in diabetics [Hastie and Efron (2012)]
elasticNetSim	A blocked correlated data simulation.
error.bars	nice standard errors for plots
fitted.l2boost	Extract the fitted model estimates along the solution path for an l2boost model.
l2boost	Generic gradient descent boosting method for linear regression.
l2boost.default	Generic gradient descent boosting method for linear regression.
l2boost.formula	Generic gradient descent boosting method for linear regression.
mvnorm.l2boost	multivariate normal data simulations.
plot.l2boost	Plotting for 'l2boost' objects.
plot.lines	plots.lines is used by 'plot.l2boost' to the path lines (each j, against each r-step)
predict.l2boost	predict method for l2boost models.
print.l2boost	print method for 'l2boost' and 'cv.l2boost' objects.
print.summary.l2boost	Unimplemented generic function These are placeholders right now.
residuals.l2boost	Model residuals for the training set of an l2boost model object
summary.l2boost	Unimplemented generic function These are placeholders right now.
VAR	This is a hidden function of the l2boost package. VAR is a helper function that specifically returns NA if all values of the argument x are NA, otherwise, it returns a var object.