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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# roahd <a href='https://astamm.github.io/roahd/'><img src='man/figures/logo.png' align="right" height="139" /></a>
<!-- badges: start -->
[](https://github.com/astamm/roahd/actions)
[](https://github.com/astamm/roahd/actions)
[](https://app.codecov.io/gh/astamm/roahd)
[](https://github.com/astamm/roahd/actions)
[](https://CRAN.R-project.org/package=roahd)
[](https://cran.r-project.org/package=roahd)
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The __roahd__ (_Robust Analysis of High-dimensional Data_) package allows to use
a set of statistical tools for the _exploration_ and _robustification_ of
univariate and multivariate __functional datasets__ through the use of
depth-based statistical methods.
In the implementation of functions, special attention was put to their
efficiency, so that they can be profitably used also for the analysis of
high-dimensional datasets.
For a full-featured description of the package, please take a look at the
[roahd](https://astamm.github.io/roahd/articles/roahd.html) vignette.
## Installation
Install the released version of **roahd** from CRAN:
```{r cran-install, eval=FALSE}
install.packages("roahd")
```
Or install the development version from GitHub with:
```{r github-install, eval=FALSE}
# install.packages("remotes")
remotes::install_github("astamm/roahd")
```
## [`fData`](https://astamm.github.io/roahd/reference/fData.html) and [`mfData`](https://astamm.github.io/roahd/reference/mfData.html) objects
A simple `S3` representation of functional data object, [`fData`](https://astamm.github.io/roahd/reference/fData.html), allows to
encapsulate the important features of univariate functional datasets (like the
grid of the dependent variable, the pointwise observations, etc.):
```{r fData-constructor}
library(roahd)
# Grid representing the dependent variable
grid = seq( 0, 1, length.out = 100 )
# Pointwise measurements of the functional dataset
Data = matrix( c( sin( 2 * pi * grid ),
cos ( 2 * pi * grid ),
sin( 2 * pi * grid + pi / 4 ) ), ncol = 100, byrow = TRUE )
# S3 object encapsulating the univariate functional dataset
fD = fData( grid, Data )
# S3 representation of a multivariate functional dataset
mfD = mfData( grid, list( 'comp1' = Data, 'comp2' = Data ) )
```
Also, this allows to exploit simple calls to customized functions which
simplifies the exploratory analysis:
```{r fData-operations, eval=FALSE}
# Algebra of fData objects
fD + 1 : 100
fD * 4
fD + fD
# Subsetting fData objects (providing other fData objects)
fD[ 1, ]
fD[ 1, 2 : 4]
# Sample mean and (depth-based) median(s)
mean( fD )
mean( fD[ 1, 10 : 20 ] )
median_fData( fD, type = 'MBD' )
```
```{r fData-plot}
# Plotting functions
plot( fD )
plot( mean( fD ), lwd = 4, add = TRUE )
plot( fD[ 2:3, ] )
```
## Robust methods for functional data analysis
A part of the package is specifically devoted to the computation of depths and
other statistical indices for functional data:
- Band depths and modified band depths,
- Modified band depths for multivariate functional data,
- Epigraph and hypograph indexes,
- Spearman and Kendall's correlation indexes for functional data,
- Confidence intervals and tests on Spearman's correlation coefficients for
univariate and multivariate functional data.
These also are the core of the visualization / robustification tools like
functional boxplot
([`fbplot`](https://astamm.github.io/roahd/reference/fbplot.html)) and
outliergram
([`outliergram`](https://astamm.github.io/roahd/reference/outliergram.html)),
allowing the visualization and identification of amplitude and shape outliers.
Thanks to the functions for the simulation of synthetic functional datasets,
both [`fbplot`](https://astamm.github.io/roahd/reference/fbplot.html) and
[`outliergram`](https://astamm.github.io/roahd/reference/outliergram.html)
procedures can be auto-tuned to the dataset at hand, in order to control the
true positive outliers rate.
## Citation
If you use this package for your own research, please cite the corresponding R
Journal article:
```{r citation, echo=FALSE}
citation("roahd")
```