# classifly v0.4

0

0th

Percentile

## Explore classification models in high dimensions

Given $p$-dimensional training data containing $d$ groups (the design space), a classification algorithm (classifier) predicts which group new data belongs to. Generally the input to these algorithms is high dimensional, and the boundaries between groups will be high dimensional and perhaps curvilinear or multi-faceted. This package implements methods for understanding the division of space between the groups.

## Functions in classifly

 Name Description classify Extract classifications from a variety of methods. classifly Classifly provides a convenient method to fit a classification function and then explore the results in the original high dimensional space. generate_classification_data Generate classification data. simvar Simulate observations from a vector variables Extract predictor and response variables for a model object. knnf A wrapper function for knn to allow use with classifly. advantage Calculate the advantage the most likely class has over the next most likely. olives Olives posterior Extract posterior group probabilities generate_data Generate new data from a data frame. explore Default method for exploring objects No Results!