Simple implementation of ordinary least squares that computes with sparse feature matrices.

## Arguments

- y
The outcome variable.

- X
The feature matrix.

- const
Boolean equal to

`TRUE`

if a constant should be included. The default is`FALSE`

- w
A vector of weights for weighted least squares.

## Value

`ols`

returns an object of S3 class
`ols`

. An object of class `ols`

is a list containing
the following components:

`coef`

A vector with the regression coefficents.

`y`

,`X`

,`const`

,`w`

Pass-through of the user-provided arguments. See above.

## See also

Other ml_wrapper:
`mdl_glmnet()`

,
`mdl_glm()`

,
`mdl_ranger()`

,
`mdl_xgboost()`