This site contains code for several purposes and its intended to work within R (http://www.r-project.org/)
- Manuel Febrero Bande y Manuel Oviedo de la Fuente
fda.usc: Functional Data Analysis and Utilities for Statistical Computing (fda.usc).
The new R package fda.usc includes methods for:
1. Functional Data Representation
2. Exploratory Functional Data Analysis
3. Functional Outlier Detection
4. Functional Regression with Scalar Response
5. Functional Supervised and Non-Supervised Classification
6. Functional ANOVA
The purpose of this package is to integrate own developments in Functional Data Analysis with those from other authors: fda package and/or group STAPH.
The package fda.usc is avalaible through CRAN and further documentation are available:
|Example R scripts:||script.zip|
- Manuel Febrero Bande
• PMICALC - Post Mortem Interval Calculator (Update 19/07/2007)
This computes Post Mortem Interval using Additive Models (AM) and Support Vector Machines (SVM) from the data obtained by José Ignacio Múñoz Barús and colleagues. The concentration of [K+], [Hx] and [Urea] in the vitreous humour is used to produce the estimation of the Post Mortem Interval.
• Depth for Functional Data - (Preliminary Version 18/07/2007)
In the following files in R (Depth-5.R, Outlier.func.R ) you will find some routines for functional data depth, outliers and bootstrap for functional data following the papers:
Cuevas, A.; Febrero-Bande, M. and Fraiman, R. (2006). "On the use of the bootstrap for estimating functions with functional data". Computational Statistics & Data Analysis 51, nº 2, 1063-1074.
Febrero-Bande, M.; Galeano, P. and González-Manteiga, W. (To appear). "A Functional Analysis of NOx levels: location and scale estimation and outlier detection". Computational Statistics. In press.
Febrero-Bande, M.; Galeano, P. and González-Manteiga, W. (To appear). "Outlier detection in functional data by depth measures with application to identify abnormal NOx levels". Environmetrics.
• geoR_NP (Update 18/07/2007)
geoR_NP.R contains some routines to estimate the variogram & ordinary kriging in a non parametric way. These routines follow the style of library(geoR). This work is based on the following the papers:
GARCÍA-SOIDÁN, P.H.; FEBRERO-BANDE, M. and GONZÁLEZ-MANTEIGA, W. (2003). "Local linear regression estimation of the variogram". Statistics & Probability Letters Vol. 64, 169-179.
GARCÍA-SOIDÁN, P.H.; FEBRERO-BANDE, M. and GONZÁLEZ-MANTEIGA, W. (2004). "Nonparametric kernel estimation of an isotropic variogram.". Journal of Stat. Planning Inference. 121, 65-92.
- Beatriz Pateiro López
• R package alphahull
alphahull: Generalization of the convex hull of a sample of points in the plane. This package computes the alpha-shape and alpha-convex hull of a given sample of points in the plane. The concepts of alpha-shape and alpha-convex hull generalize the definition of the convex hull of a finite set of points. The programming is based on the duality between the Voronoi diagram and Delaunay triangulation. The package also includes a function that returns the Delaunay mesh of a given sample of points and its dual Voronoi diagram in one single object.
|MacOS X binary:||alphahull_0.2-1.tgz|
|Vignettes:||Generalizing the Convex Hull of a Sample: The R Package alphahull|
alphashape3d: Implementation of the 3D alpha-shape for the reconstruction of 3D sets from a point cloud The package alphashape3d presents the implementation in R of the alpha-shape of a finite set of points in the three-dimensional space. This geometric structure generalizes the convex hull and allows to recover the shape of non-convex and even non-connected sets in 3D, given a random sample of points taken into it. Besides the computation of the alpha-shape, the package alphashape3d provides users with functions to compute the volume of the alpha-shape, identify the connected components and facilitate the three-dimensional graphical visualization of the estimated set.
|MacOS X binary:||alphashape3d_0.2.tgz|
• R package DTDA.
DTDA: Doubly truncated data analysis
This package implements different algorithms for analyzing randomly truncated data, one-sided and two-sided (i.e. doubly) truncated data. Two real data sets are included. It incorporates the iterative methods introduced by Efron and Petrosian (1999) and Shen (2008). Estimation of the lifetime distribution function and truncation times distributions is possible, together with the corresponding pointwise confidence limits based on bootstrap methods. Plots of cumulative distributions and survival functions are provided. Two real data sets are included: right-truncated AIDS data and doubly truncated data on quasar luminosities.
|MacOS X binary:||DTDA_2.1-1.tgz|