This function represents the scatter plots of bust circumference against other selected variable (chest, hip, neck to ground or waist) jointly with the prototypes obtained for each bust class provided by either trimowa or hipamAnthropom. In addition, the prototypes defined by the European standard on sizing systems. Size designation of clothes. Part 3: Measurements and intervals can be also displayed.
● Data Source:
CranContrib
● Keywords: dplot
● Alias: plotPrototypes
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2 images
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This function allows us to represent in the same plot the screeplot of the archetypes and the both cand_ns, cand_alpha and cand_beta archetypoids.
● Data Source:
CranContrib
● Keywords: math
● Alias: screeArchetypal
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1 images
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This is a helper function for computing the hipamAnthropom elements provided by the hipamAnthropom algorithm for a number of bust sizes defined by the European Normative (EN). Therefore, the hipamAnthropom is used inside this function.
● Data Source:
CranContrib
● Keywords: math
● Alias: computSizesHipamAnthropom
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0 images
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The HIerarchical Partitioning Around Medoids clustering method (HIPAM) was originally created to gene clustering (Wit et al. (2004)). The HIPAM algorithm is a divisive hierarchical clustering method based on the PAM algorithm.
● Data Source:
CranContrib
● Keywords: array
● Alias: hipamAnthropom
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0 images
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In the HIPAM algorithm, each (parent) cluster P is investigated to see if it can be divided further into new (child) clusters, or stop (in this case, P would be a terminal node).
● Data Source:
CranContrib
● Keywords: array
● Alias: checkBranchLocalMO
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0 images
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The basic foundation of k-means is that the sample mean is the value that minimizes the Euclidean distance from each point, to the centroid of the cluster to which it belongs. Two fundamental concepts of the statistical shape analysis are the Procrustes mean and the Procrustes distance. Therefore, by integrating the Procrustes mean and the Procrustes distance we can use k-means in the shape analysis context.
● Data Source:
CranContrib
● Keywords: array
● Alias: trimmedLloydShapes
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The Hartigan-Wong version of the k-means algorithm uses two auxiliary algorithms: the optimal transfer stage (optra) and the quick transfer stage (qtran).
● Data Source:
CranContrib
● Keywords: array
● Alias: qtranShapes
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0 images
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This function is a slight modification of the original shapes3d function of the shapes R package so that the resulting plot has customized title and axes. Specifically, the changing lines regarding the original function are those related to its argument axes3 when it is fixed to TRUE.
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: shapes3dShapes
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0 images
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This function allows us to reproduce the results shown in section 2.2.2 and section 3.1 of Epifanio et al. (2013). In addition, from the results provided by this function, the other results shown in section 3.2 and section 3.3 of the same paper can be also reproduced (see section examples below).
● Data Source:
CranContrib
● Keywords: array
● Alias: archetypesBoundary
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Execute the archetypoid algorithm repeatedly. It is inspired by the stepArchetypes function of the archetypes R package.
● Data Source:
CranContrib
● Keywords: array
● Alias: stepArchetypoids
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providing 
.