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nproc  

Neyman-Pearson (NP) Classification Algorithms and NP Receiver Operating Characteristic (NP-ROC) Curves
View on CRAN: Click here


Download and install nproc package within the R console
Install from CRAN:
install.packages("nproc")

Install from Github:
library("remotes")
install_github("cran/nproc")

Install by package version:
library("remotes")
install_version("nproc", "2.1.5")



Attach the package and use:
library("nproc")
Maintained by
Yang Feng
[Scholar Profile | Author Map]
First Published: 2016-02-13
Latest Update: 2020-01-13
Description:
In many binary classification applications, such as disease diagnosis and spam detection, practitioners commonly face the need to limit type I error (i.e., the conditional probability of misclassifying a class 0 observation as class 1) so that it remains below a desired threshold. To address this need, the Neyman-Pearson (NP) classification paradigm is a natural choice; it minimizes type II error (i.e., the conditional probability of misclassifying a class 1 observation as class 0) while enforcing an upper bound, alpha, on the type I error. Although the NP paradigm has a century-long history in hypothesis testing, it has not been well recognized and implemented in classification schemes. Common practices that directly limit the empirical type I error to no more than alpha do not satisfy the type I error control objective because the resulting classifiers are still likely to have type I errors much larger than alpha. As a result, the NP paradigm has not been properly implemented for many classification scenarios in practice. In this work, we develop the first umbrella algorithm that implements the NP paradigm for all scoring-type classification methods, including popular methods such as logistic regression, support vector machines and random forests. Powered by this umbrella algorithm, we propose a novel graphical tool for NP classification methods: NP receiver operating characteristic (NP-ROC) bands, motivated by the popular receiver operating characteristic (ROC) curves. NP-ROC bands will help choose in a data adaptive way and compare different NP classifiers.
How to cite:
Yang Feng (2016). nproc: Neyman-Pearson (NP) Classification Algorithms and NP Receiver Operating Characteristic (NP-ROC) Curves. R package version 2.1.5, https://cran.r-project.org/web/packages/nproc. Accessed 04 Apr. 2025.
Previous versions and publish date:
0.1 (2016-02-13 19:01), 0.4 (2016-05-03 08:20), 0.5 (2016-05-08 18:19), 1.1 (2016-06-19 16:44), 1.2 (2016-08-11 13:02), 2.0.1 (2016-09-27 08:41), 2.0.4 (2017-01-13 15:37), 2.0.6 (2017-03-04 17:12), 2.0.8 (2017-09-01 15:04), 2.0.9 (2017-09-18 05:29), 2.1.1 (2018-02-13 16:42), 2.1.4 (2018-11-16 19:20)
Other packages that cited nproc R package
View nproc citation profile
Other R packages that nproc depends, imports, suggests or enhances
Complete documentation for nproc
Functions, R codes and Examples using the nproc R package
Some associated functions: compare . lines.nproc . npc . nproc . plot.nproc . predict.npc . print.npc . print.nproc . rocCV . 
Some associated R codes: compare.R . lines.nproc.R . npc.R . npfuns.R . nproc.R . plot.nproc.R . predict.npc.R . print.npc.R . print.nproc.R . rocCV.R .  Full nproc package functions and examples
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