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Anthropometry (version 1.10)

Statistical Methods for Anthropometric Data

Description

Statistical methodologies especially developed to analyze anthropometric data. These methods are aimed at providing effective solutions to some commons problems related to Ergonomics and Anthropometry. They are based on clustering, the statistical concept of data depth, statistical shape analysis and archetypal analysis.

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Version

Install

install.packages('Anthropometry')

Monthly Downloads

487

Version

1.10

License

GPL (>= 2)

Homepage

https://www.R-project.org

Maintainer

Guillermo Vinue

Last Published

April 6th, 2018

Functions in Anthropometry (1.10)

archetypesBoundary

Archetypal analysis in multivariate accommodation problem

Cube of 34 landmarks

descrDissTrunks

Description of the dissimilarities between women's trunks

Figures of 8 landmarks with labelled landmarks

percentilsArchetypoid

Helper function for computing percentiles of a certain archetypoid

Dissimilarity matrix between individuals and prototypes

Prototypes representation

screeArchetypal

Screeplot of archetypal individuals

Trimmed or outlier observations representation

plotTreeHipamAnthropom

HIPAM dendogram

HIPAM algorithm for anthropometric data

skeletonsArchetypal

Skeleton plot of archetypal individuals

Auxiliary optra subroutine of the Hartigan-Wong k-means for 3D shapes

overlapBiclustersByRows

Overlapped biclusters by rows

stepArchetypesRawData

Archetype algorithm to raw data

Cube of 8 landmarks

parallelep34landm

Parallelepiped of 34 landmarks

landmarksSampleSpaSurv

Landmarks of the sampled women of the Spanish Survey

nearestToArchetypes

Nearest individuals to archetypes

cdfDissWomenPrototypes

CDF for the dissimilarities between women and computed medoids and standard prototypes

getBestPamsamIMO

Generation of the candidate clustering partition in $HIPAM_IMO$

Helper generic function for obtaining the trimmed and outlier observations

stepArchetypoids

Run the archetypoid algorithm several times

Auxiliary qtran subroutine of the Hartigan-Wong k-means for 3D shapes

getBestPamsamMO

Generation of the candidate clustering partition in $HIPAM_MO$

parallelep8landm

Parallelepiped of 8 landmarks

trimmedLloydShapes

Trimmed Lloyd k-means for 3D shapes

weightsMixtureUB

Calculation of the weights for the OWA operators

Data preprocessing before computing archetypal observations

Trimmed k-medoids algorithm

Helper function for plotting the shapes

Trimmed PAM with OWA operators

sampleSpanishSurvey

Sample database of the Spanish anthropometric survey

xyplotPCArchetypes

PC scores for archetypes

Anthropometry-package

Statistical Methods for Anthropometric Data

Trimmed clustering based on L1 data depth

Anthropometry-internalHipamAnthropom

Several internal functions used by both $HIPAM_MO$ and $HIPAM_IMO$ algorithms

Lloyd k-means for 3D shapes

Anthropometry-internalPlotTree

Several internal functions used to build the HIPAM plot tree

Anthropometry-internalArchetypoids

Several internal functions to compute and represent archetypes and archetypoids

USAF 1967 survey

Hartigan-Wong k-means for 3D shapes

Anthropometry-internalTDDclust

Several internal functions to clustering based on the L1 data depth

CCbiclustAnthropo

Cheng and Church biclustering algorithm applied to anthropometric data

checkBranchLocalMO

Evaluation of the candidate clustering partition in $HIPAM_MO$

Helper function for the 3D landmarks

bustSizesStandard

Helper function for defining the bust sizes

Helper generic function for obtaining the anthropometric cases

checkBranchLocalIMO

Evaluation of the candidate clustering partition in $HIPAM_IMO$

computSizesTrimowa

Computation of the trimowa elements for a given number of sizes defined by the EN

computSizesHipamAnthropom

Computation of the hipamAnthropom elements for a given number of sizes defined by the EN

Finding archetypoids