DepthProc

DepthProc project consist of a set of statistical procedures based on so called statistical depth functions. The project involves free available R package and its description.

Versions

CRAN release version

Installation

DepthProc is avaiable on CRAN:

install.packages("DepthProc")

You can also install it from GitHub with devtools package:

library(devtools)
# install_github("zzawadz/DepthProc")
install.packages("DepthProc", repos = c('https://zzawadz.r-universe.dev', 'https://cloud.r-project.org'))

Main features:

Speed and multithreading

Most of the code is written in C++ for additional efficiency. We also use OpenMP to speedup computations with multithreading. The speedup is close to linear in the number of cores — the timings below were knitted on a 4-core machine, so restricting depth() to one thread costs roughly 4x the elapsed time, and to two threads roughly 2x:

library(DepthProc)
set.seed(123)

d <- 10
x <- MASS::mvrnorm(2000, rep(0, d), diag(d))
# Default - utilize as many threads as possible
system.time(depth(x, x, method = "LP"))
#>    user  system elapsed 
#>   1.864   0.001   0.473

# Only single thread - compare the elapsed column:
system.time(depth(x, x, method = "LP", threads = 1))
#>    user  system elapsed 
#>   1.769   0.000   1.772

# Two threads:
system.time(depth(x, x, method = "LP", threads = 2))
#>    user  system elapsed 
#>   1.803   0.000   0.904

Available depth functions

x <- MASS::mvrnorm(100, c(0, 0), diag(2))

depthEuclid(x, x)
depthMah(x, x)
depthLP(x, x)
depthProjection(x, x)
depthLocal(x, x)
depthTukey(x, x)

## Base function to call others:
depth(x, x, method = "Projection")
depth(x, x, method = "Local", depth_params1 = list(method = "LP"))

## Get median
depthMedian(x, 
  depth_params = list(
    method = "Local",
    depth_params1 = list(method = "LP")))

Basic plots

Contour plot

library(mvtnorm)
y <- rmvt(n = 200, sigma = diag(2), df = 4, delta = c(3, 5))
depthContour(y, points = TRUE, graph_params = list(lwd = 2))

Perspective plot

depthPersp(y, depth_params = list(method = "Mahalanobis"))

Functional depths:

There are two functional depths implemented - modified band depth (MBD), and Frainman-Muniz depth (FM):

x <- matrix(rnorm(60), nc = 20)
fncDepth(x, method = "MBD")
fncDepth(x, method = "FM", dep1d_params = list(method = "Mahalanobis"))

Functional BoxPlot

x <- matrix(rnorm(2000), ncol = 100)
fncBoxPlot(x, bands = c(0, 0.5, 1), method = "FM")