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fdaMocca

Model-Based Clustering for Functional Data with Covariates

Installation

About

Routines for model-based functional cluster analysis for functional data with optional covariates. The idea is to cluster functional subjects (often called functional objects) into homogenous groups by using spline smoothers (for functional data) together with scalar covariates. The spline coefficients and the covariates are modelled as a multivariate Gaussian mixture model, where the number of mixtures corresponds to the number of clusters. The parameters of the model are estimated by maximizing the observed mixture likelihood via an EM algorithm (Arnqvist and Sjöstedt de Luna, 2019) . The clustering method is used to analyze annual lake sediment from lake Kassjön (Northern Sweden) which cover more than 6400 years and can be seen as historical records of weather and climate.

Key Metrics

Version 0.1-1
R ≥ 3.6.0
Published 2022-07-21 617 days ago
Needs compilation? no
License GPL-2
License GPL-3
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Maintainer

Maintainer

Natalya Pya Arnqvist

nat.pya@gmail.com

Authors

Natalya Pya
Arnqvist[aut

cre

Per Arnqvist

aut / cre

Sara Sjöstedt de Luna

aut

Material

ChangeLog
Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

fdaMocca archive

Depends

R ≥ 3.6.0

Imports

stats
graphics
Matrix
parallel
foreach
doParallel
mvtnorm
fda
grDevices