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docopulae

A direct approach to optimal designs for copula models based on the Fisher information. Provides flexible functions for building joint PDFs, evaluating the Fisher information and finding optimal designs. It includes an extensible solution to summation and integration called nint, functions for transforming, plotting and comparing designs, as well as a set of tools for common low-level tasks.

Goals

docopulae strives to provide functions which allow the user to

  • define a wide variety of models by the joint probability density function (PDF)
  • evaluate the Fisher information by providing a convenient interface
  • find optimal designs using some sensitivity function
  • visualize designs
  • compare Ds-optimal designs

Workflow

See ./misc/workflow.dot.pdf and ./misc/nint.dot.pdf. Vignettes explaining these steps in detail are planned.

Quick Start

First of all, if you are completely unfamiliar with R then I strongly recommend you to read "A (very) short introduction to R" first (just google it). Basic knowledge is necessary and assumed almost everywhere.

TODO. For the moment see and follow the example for the function param on the corresponding help page. It requires at least the packages copula, SparseGrid and numDeriv to be installed. Run devtools::update_packages(c('copula', 'SparseGrid', 'numDeriv')) (or instead with install.packages) to install/update them. To install/update all suggested packages run devtools::update_packages(c('copula', 'numDeriv', 'Deriv', 'cubature', 'SparseGrid', 'mvtnorm', 'testthat')).

If R's help won't work after installing the packages, restart R to resolve.

Have fun :)

Install

  • from CRAN
    • install.packages('docopulae')
  • from GitHub with devtools
    • devtools::install_github('arappold/docopulae')
  • from GitHub without devtools

Bugs

If you are absolutely certain that you found a bug, please let me know by creating an issue at https://github.com/arappold/docopulae/issues. Explain how to reproduce the bug, best by attaching a small script, and I will investigate as soon as I got time to.

(just a) Warning: docopulae allows complex/complicated scripts. And even though we might think we know what it tells R what to do, we most often don't.

Feature Requests

If you feel unhappy about certain aspects of docopulae and perhaps have an adequate solution, please create an issue and lets discuss about it.

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Version

Install

install.packages('docopulae')

Monthly Downloads

231

Version

0.4.0

License

MIT + file LICENSE

Issues

Pull Requests

Stars

Forks

Maintainer

Andreas Rappold

Last Published

October 26th, 2018

Functions in docopulae (0.4.0)

nint_tanTransform

Tangent Transform
Wynn

Wynn
nint_space

Space
nint_expandSpace

Expand Space
wDsensitivity

Weighted D Sensitivity
nint_scatDim

Scatter Dimension
reduce

Reduce Design
nint_integrateNCube

Integrate Hypercube
print.nint_space

Print Space
nint_integrate

Integrate
nint_integrateNFunc

Integrate N Function
integrateA

Integrate Alternative
nint_intvDim

Interval Dimension
wDefficiency

Weighted D Efficiency
nint_funcDim

Function Dimension
update.param

Update Parametric Model
DerivLogf

Build Derivative Function for Log f
nint_gridDim

Grid Dimension
nint_ERROR

Space Validation Errors
nint_validateSpace

Validate Space
roworder

Matrix Ordering Permutation
rowmatch

Row Matching
param

Parametric Model
plot.desigh

Plot Design
rowsduplicated

Determine Duplicate Rows
nint_transform

Transform Integral
seq1

Sequence Generation
numDerivLogf

Build Derivative Function for Log f
Dsensitivity

D Sensitivity
docopulae

Design of Experiments with Copulas
fisherI

Fisher Information
getM

Get Fisher Information
buildf

Build probability density or mass Function
Defficiency

D Efficiency
grow.grid

Grow Grid
design

Design
nint_TYPE

Dimension Type Attribute Values