Implement the Best Subset Selection algorithm in R.

Algorithm:

1. Let M₀ denote the null model, which contains no predictors. This model simply predicts the sample mean for each observation.

2. For k = 1,2, . . . p:

a. Fit all models that contain exactly k predictors.

b. Pick the best among these models, and call it M_k. Here best is defined as having the smallest RSS, or equivalently largest R².

3. Select a single best model from among M₀, . . . ,M_p using cross-validated prediction error, C_p(AIC), BIC, or adjusted R²

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