LinearRegression: Linear Regression
In dfalbel/rsk:

Ordinary least squares Linear Regression.

rsk_LinearRegression

LinearRegression(x, y, fit_intercept = TRUE, normalize = FALSE,
  copy_X = TRUE, n_jobs = 1)

`x`	matrix. Training Data
`y`	matrix. Target Values
`fit_intercept`	boolean, optional whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).
`normalize`	boolean, optional, default False. If True, the regressors X will be normalized before regression. This parameter is ignored when fit_intercept is set to False. When the regressors are normalized, note that this makes the hyperparameters learnt more robust and almost independent of the number of samples. The same property is not valid for standardized data. However, if you wish to standardize, please use preprocessing.StandardScaler before calling fit on an estimator with normalize=False.
`copy_X`	boolean, optional, default True. If True, X will be copied; else, it may be overwritten.
`n_jobs`	int, optional, default 1. The number of jobs to use for the computation. If -1 all CPUs are used. This will only provide speedup for n_targets > 1 and sufficient large problems.