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scR: Learning from Noise: Applying Sample Complexity Bounds for Political Science Research

This R package provides a computationally efficient way of calculating Sample Complexity Bounds (scb), suggested by Carter and Choi (2023).

Authors

Perry Carter and Dahyun Choi

Paper

Learning from Noise: Applying Sample Complexity Bounds for Political Science Research

Installation

Install the package from CRAN by running the R commands:

install.packages("scR")
library("scR")

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Version

Install

install.packages('scR')

Monthly Downloads

181

Version

0.4.0

License

MIT + file LICENSE

Issues

0

Pull Requests

0

Stars

0

Forks

0

Maintainer

Perry Carter

Last Published

December 23rd, 2024

Functions in scR (0.4.0)

plot_accuracy

Represent simulated sample complexity bounds graphically
acc_sim

Utility function to generate accuracy metrics, for use with estimate_accuracy()
br

Replication data for 'Predicting Recidivism'
loss

Utility function to define the least-squares loss function to be optimized for simvcd()
estimate_accuracy

Estimate sample complexity bounds for a binary classification algorithm using either simulated or user-supplied data.
getpac

Recalculate achieved sample complexity bounds given different parameter inputs
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.
simvcd

Estimate the Vapnik-Chervonenkis (VC) dimension of an arbitrary binary classification algorithm.
scb

Calculate sample complexity bounds for a classifier given target accuracy
gendata

Simulate data with appropriate structure to be used in estimating sample complexity bounds