This package uses the marginal relationship between predictor variables and a response to identify a small subset of measurements which appear relevant for prediction, produce a low-dimensional linear embedding based on this small subset, and amplify this embedding with information from the remaining measurements. The goal is to perform principle components regression (PCR), but in high dimensional settings where the number of measurements exceeds the number of observations. The technique employed uses some approximation methods to increase statistical and computational efficiency.
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