R: Empirical Sample Complexity Bounds

# Empirical Sample Complexity Bounds

## Documentation for package ‘scR’ version 0.7.0

- DESCRIPTION file
- Package NEWS
- Package README

## Help Pages

acc_sim | Utility function to generate accuracy metrics, for use with 'estimate_accuracy()'
conduct_interpolation | Conduct interpolation on a single simulation
create_scb_model | Create custom model fitting function
create_scb_prediction | Create custom prediction function
estimate_accuracy | Estimate sample complexity bounds for a binary classification algorithm using either simulated or user-supplied data.
fit_and_predict | Fit an extrapolation model using nonlinear least squares
fit_gp_scb_curve | Fit a monotone Gaussian process sample-complexity curve
gendata | Simulate data with appropriate structure to be used in estimating sample complexity bounds
getpac | Recalculate achieved sample complexity bounds given different parameter inputs
interpolate_scb | Conduct interpolation on a list of data
interpolate_scb_gp | Interpolate sample-complexity curves using monotone Gaussian processes
loss | Utility function to define the least-squares loss function to be optimized for 'simvcd()'
plot.empirical_scb_gp | Plot a monotone Gaussian process sample-complexity fit
plot.empirical_scb_list | Plot method for an 'empirical_scb_list' object
plot.scb_data | Plot method for simulated sample complexity bounds ('scb_data' object)
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.
summary.empirical_scb_gp | Summarize a monotone Gaussian process sample-complexity fit
summary.empirical_scb_list | Summary of empirical sample complexity bound results