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Anthropometry (version 1.1)

Statistical Methods for Anthropometric Data

Description

Anthropometry brings together some 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

567

Version

1.1

License

GPL (>= 2)

Maintainer

Guillermo Vinue

Last Published

October 13th, 2014

Functions in Anthropometry (1.1)

Anthropometry-package

Statistical Methods for Anthropometric Data
WeightsMixtureUB

Calculation of the weights for the OWA operators
figures8landm

Figures with labelled landmarks
plotTreeHipam

HIPAM dendogram
cdfDiss

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

Run the archetypoid algorithm several times
accommodation

Data preprocessing before computing archetypes
qtranProcrustes

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

Demo database of the Spanish anthropometric survey
dataUSAF

USAF 1967 database
trimmedoid

Trimmed k-medoids algorithm
Anthropometry-internalPlotTree

Several internal functions used to build the HIPAM plot tree
landmarks

Landmarks representing the woman's body
Anthropometry-internalHIPAM

Several internal functions used by both $HIPAM_{MO}$ and $HIPAM_{IMO}$ algorithms
trimowa

Trimmed PAM with OWA operators
GetDistMatrix

Dissimilarity matrix between individuals and prototypes
LloydShapes

Lloyd k-means for 3D shapes
TDDclust

Trimmed clustering based on L1 data depth
shapes3dMod

3D shapes plot
hipamBigGroups

Hipam medoids of the clusters with more than 2 elements
outlierHipam

Individuals of the hipam clusters with 1 or 2 elements
HartiganShapes

Hartigan-Wong k-means for 3D shapes
stepArchetypesMod

Archetype algorithm to raw data
cube34

Cube of 34 landmarks
CCbiclustAnthropo

Cheng and Church biclustering algorithm applied to anthropometric data
plotMedoids

Medoids representation
screeArchetyp

Screeplot of archetypes and archetypoids
getBestPamsamMO

Generation of the candidate clustering partition in $HIPAM_{MO}$
Anthropometry-internalDepth

Several internal functions to clustering based on the L1 data depth
trimmedLloydShapes

Trimmed Lloyd k-means for 3D shapes
indivNearest

Nearest individuals to archetypes
hipamAnthropom

HIPAM algorithm for anthropometric data
cMDSwomen

Description of the dissimilarities between women's trunks
compPerc

Computing percentiles of a certain archetypoid
xyplotPCA

PC scores for archetypes
skeletonsArchet

Skeleton plots of archetypal individuals
archetypesBoundary

Archetypal analysis in multivariate accommodation problem
getBestPamsamIMO

Generation of the candidate clustering partition in $HIPAM_{IMO}$
checkBranchLocalMO

Evaluation of the candidate clustering partition in $HIPAM_{MO}$
checkBranchLocalIMO

Evaluation of the candidate clustering partition in $HIPAM_{IMO}$
Anthropometry-internalArchetypoids

Several internal functions to compute and represent archetypes and archetypoids
optraProcrustes

Auxiliary optra subroutine of the Hartigan-Wong k-means for 3D shapes
cube8

Cube of 8 landmarks
plotTrimmOutl

Trimmed or outlier observations representation
parallelepiped8

Parallelepiped of 8 landmarks
overlappingRows

Overlapped biclusters by rows
archetypoids

Finding archetypoids
parallelepiped34

Parallelepiped of 34 landmarks