# Estimate Vapnik-Chervonenkis Dimension and Sample Complexity

## Documentation for package ‘scR’ version 0.4.0

- DESCRIPTION file
- Package README

## Help Pages

acc_sim | Utility function to generate accuracy metrics, for use with 'estimate_accuracy()'
br | Replication data for 'Predicting Recidivism'
estimate_accuracy | Estimate sample complexity bounds for a binary classification algorithm using either simulated or user-supplied data.
gendata | Simulate data with appropriate structure to be used in estimating sample complexity bounds
getpac | Recalculate achieved sample complexity bounds given different parameter inputs
loss | Utility function to define the least-squares loss function to be optimized for 'simvcd()'
plot_accuracy | Represent simulated sample complexity bounds graphically
risk_bounds | Utility function to generate data points for estimation of the VC Dimension of a user-specified binary classification algorithm given a specified sample size.
scb | Calculate sample complexity bounds for a classifier given target accuracy
simvcd | Estimate the Vapnik-Chervonenkis (VC) dimension of an arbitrary binary classification algorithm.