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ReMFPCA

Installation

You can install ReMFPCA from github with:

# install.packages("remotes")
remotes::install_github("haghbinh/ReMFPCA")

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Install

install.packages('ReMFPCA')

Monthly Downloads

174

Version

2.0.0

License

GPL (>= 2)

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Repository

https://github.com/haghbinh/ReMFPCA

Maintainer

Hossein Haghbin

Last Published

April 12th, 2025

Functions in ReMFPCA (2.0.0)

Multiplication of an `mvmfd` object with a scalar

Addition of two `mvmfd` objects

Penalty Function

Standard deviation of an object of class `mfd`.

Extract subsets of an `mfd` object

Scalar multiplication of an `mfd` object

Extract subsets of an `mvmfd` object

Add two `mfd` objects

plots an object of classes `mfd`, `mvmfd` or `remfpca`

A Class for `ReMFPCA` objects

Subtract two `mfd` objects

electrical_power_data

Electrical Power Dataset

Check if an object is of class 'basismfd'

Subtraction of two `mvmfd` objects

Compute the inner product between two objects of class `mvmfd`

Check if an object is of class 'mvbasismfd'

Define a Set of Multidimensional Functional Basis

Compute the inner product between two objects of class `mfd`

Bivariate plot for `mvmfd` objects

Check if an object is of class 'mfd'

motion_sense_data

Motion Sense Dataset: Measurements of user acceleration and pitch attitude collected by smartphones from 24 individuals performing four distinct activities: jogging, walking, sitting, and standing.

Compute the norm of an object of class `mvmfd`

Length of an object of classes `mfd`or `mvmfd`.

Check if an object is of class 'mvmfd'

Define a Set of Multivariate Multidimensional Functional Data objects

mean of an object of classes `mfd`or `mvmfd`.

Compute the norm of an object of class `mfd`

Define a Set of Multidimensional Functional Data objects

Define a Set of Multivariate Multidimensional Functional Basis