In dbolotov/amelie: Anomaly Detection with Normal Probability Functions

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
  # fig.path = "README-"
)
options(tibble.print_min = 5, tibble.print_max = 5)

About

amelie implements anomaly detection with maximum likelihood estimates and normal probability density functions. The package follows and builds on the approach described in Andrew Ng's course on machine learning.

Current CRAN version: 0.2.0

Development version (this repository): 0.3.0

Installation

Release from CRAN

install.packages("amelie")

Latest changes from GitHub

# install.packages("devtools")
devtools::install_github("dbolotov/amelie")

Example

library(amelie)

x1 <- c(1,.2,3,1,1,.7,-2,-1)
x2 <- c(0,.5,0,.4,0,1,-.3,-.1)
x <- do.call(cbind,list(x1,x2))
y <- c(0,0,0,0,0,0,1,1)
dframe <- data.frame(x,y)
df_fit <- ad(y ~ x1 + x2, dframe)

Documentation

• Introduction vignette for a description of the algorithm.
• Package site for package documentation (built with pkgdown).

dbolotov/amelie documentation built on May 25, 2019, 4:23 p.m.