# `R`

Packages

The `R`

packages here perform a variety of analyses. I plan
to upload them
to `CRAN`

once the relevant papers are published.

Unless otherwise
mentioned, I am the maintainer for these
packages. Email me if you
identify any bugs or want to suggest features for future versions. The
packages are all distributed under a GPL 3.0 version. This means you
are free to distribute copies or modify the software. However any
copies you distribute (modified or otherwise) must also be distributed
under a GPL 3.0 or later.

### PoissonPCA

##### Tianshu Huang & Toby Kenney

Dowload Version 1.0.1
PoissonPCA is an `R`

package for fitting a corrected
PCA to the (possibly transformed) latent Poisson means of a distribution.

#### Summary

Given a data matrix $X$ where $X$_{ij}∼
Po(Λ_{ij}) are conditionally independent
given $\Lambda $, this package estimates the covariance
matrix of a transformation $f(\Lambda )$, and from this
estimates the principal components.

#### Documentation

The link below provides brief documentation of the functions
provided by the package.

Documentation

### SuRF

##### Lihui Liu

Dowload Version 1.0.1
SuRF is a variable selection method based on Forward Selection
with Ranking by Subsampling.

#### Summary

Given a predictor matrix $X$ and a response
variable $Y$, this package aims to perform variable
selection for a predictive generalised linear model. It does this in
two stages: first a subsampling method with LASSO for ranking the
variables, then a forward selection algorithm.

#### Documentation

The link below provides brief documentation of the functions
provided by the package.

Documentation
#### Old Versions

Version 1.0.0

### AdequateBootstrap

##### Toby Kenney

Dowload Version 1.0.0
AdequateBootstrap is an `R`

package for performing the
adequate bootstrap method, which reduces the bootstrap size based
on model adequacy. Full details are
in this paper.

#### Summary

Given a parametric model and some data, the adequate boostrap
first calculates the bootstrap size at which a bootstrap sample has
a 50% chance of rejecting the model adequacy test. It then uses
bootstraps of this size to obtain confidence intervals for the
parameters. The idea is that the confidence intervals should
include the issue of model uncertainty.

#### Documentation

The link below provides brief documentation of the functions
provided by the package.

Documentation
#### Note

A `c++`

version of the program is
available here.