# multivarious

This package is intended to provide some basic abstractions and default implementations of basic computational infrastructure for multivariate component-based modeling such as principal components analysis.

The main idea is to model multivariate decompositions as involving projections from an input data space to a lower dimensional component space. This idea is encapsulated by the `projector`

class and the `project`

function. Support for two-way mapping (row projection and column projection) is provided by the derived class `bi-projector`

. Generic functions for common operations are included:

`project`

for mapping from input space into (usually) reduced-dimensional output space
`partial_project`

for mapping a subset of input space into output space
`project_vars`

for mapping new variables (“supplementary variables”) to output space
`reconstruct`

for reconstructing input data from its low-dimensional representation
`residuals`

for extracting residuals of a fit with `n`

components.

## Installation

You can install the development version from GitHub with:

## Example

This is a basic example which shows you how to solve a common problem: