Package: DepthProc 2.1.5
Zygmunt Zawadzki
DepthProc: Statistical Depth Functions for Multivariate Analysis
Data depth concept offers a variety of powerful and user friendly tools for robust exploration and inference for multivariate data. The offered techniques may be successfully used in cases of lack of our knowledge on parametric models generating data due to their nature. The package consist of among others implementations of several data depth techniques involving multivariate quantile-quantile plots, multivariate scatter estimators, multivariate Wilcoxon tests and robust regressions.
Authors:
DepthProc_2.1.5.tar.gz
DepthProc_2.1.5.tar.gz(r-4.5-noble)DepthProc_2.1.5.tar.gz(r-4.4-noble)
DepthProc_2.1.5.tgz(r-4.4-emscripten)DepthProc_2.1.5.tgz(r-4.3-emscripten)
DepthProc.pdf |DepthProc.html✨
DepthProc/json (API)
NEWS
| # Install 'DepthProc' in R: |
| install.packages('DepthProc', repos = 'https://cloud.r-project.org') |
Bug tracker:https://github.com/zzawadz/depthproc/issues5 issues
Pkgdown site:https://www.depthproc.zstat.pl
- USLABOUR - US Labour dataset
- cracow.airpollution - Air pollution with PM10 in Cracow within day and night in December 2016
- france - Relation between minimum wage (MW) and unemployment rate (UR) in France.
- inf.mort - Infant mortality rate (0-1 year) per 1,000 live births
- internet.users - Internet view data
- katowice.airpollution - Air pollution in Katowice city by hour.
- maesles.imm - Children 1 year old immunized against measles, percentage
- under5.mort - Children under 5 months mortality rate per 1,000 live births
Last updated 3 years agofrom:06e170c5c2. Checks:1 OK, 2 NOTE. Indexed: no.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 11 2025 |
| R-4.5-linux-x86_64 | NOTE | Mar 11 2025 |
| R-4.4-linux-x86_64 | NOTE | Mar 11 2025 |
Exports:ablineas.matrixasymmetryCurvebinningDepth2DcombineDepthCurvesCovLPddMvnormddPlotdeepReg2ddepthdepthContourdepthDensitydepthEucliddepthLocaldepthLPdepthMahdepthMediandepthPerspdepthProjectiondepthTukeyfncBoxPlotfncDepthfncDepthFMfncDepthMBDfncDepthMedianfncGetBandgetPlotlsdAddContourlsdGetContourlsdSampleDepthContourslsdSampleMaxDepthmWilcoxonTestplotrunifspherescaleCurvetrimProjReg2d
Dependencies:abindbootclicolorspacecubatureDEoptimRfansifarvergeometryggplot2gluegtableisobandlabelinglatticelifecyclelinproglpSolvemagicmagrittrMASSMatrixMatrixModelsmgcvmunsellmvtnormnlmenppcaPPpillarpkgconfigquadprogquantregR6RColorBrewerRcppRcppArmadilloRcppProgressrlangrobustbaserrcovscalessmSparseMsurvivaltibbleutf8vctrsviridisLitewithrzoo
Citation
To cite package 'DepthProc' in publications use:
Daniel Kosiorowski, Zygmunt Zawadzki (2022). DepthProc An R Package for Robust Exploration of Multidimensional Economic Phenomena.
Corresponding BibTeX entry:
@Manual{,
title = {DepthProc An R Package for Robust Exploration of
Multidimensional Economic Phenomena},
author = {Daniel Kosiorowski and Zygmunt Zawadzki},
year = {2022},
}
Readme and manuals
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")
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:
library(DepthProc)
set.seed(123)
d <- 10
x <- mvrnorm(1000, rep(0, d), diag(d))
# Default - utilize as many threads as possible
system.time(depth(x, x, method = "LP"))
#> user system elapsed
#> 0.351 0.000 0.090
# Only single thread - 4 times slower:
system.time(depth(x, x, method = "LP", threads = 1))
#> user system elapsed
#> 0.208 0.000 0.208
# Two threads - 2 times slower:
system.time(depth(x, x, method = "LP", threads = 2))
#> user system elapsed
#> 0.201 0.000 0.103
Available depth functions
x <- 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 = "Mahalanobis")
#> Warning in dep1d_params$u <- u[, i]: Coercing LHS to a list
Functional BoxPlot
x <- matrix(rnorm(2000), ncol = 100)
fncBoxPlot(x, bands = c(0, 0.5, 1), method = "FM")
