R PackagesThe R packages here perform a variety of analyses. I plan
to upload them
to CRAN
once the relevant papers are published.
R package for fitting a corrected
PCA to the (possibly transformed) latent Poisson means of a distribution.
Given a data matrix where are conditionally independent given , this package estimates the covariance matrix of a transformation , and from this estimates the principal components.
The link below provides brief documentation of the functions provided by the package.
Given a predictor matrix and a response variable , 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.
The link below provides brief documentation of the functions provided by the package.
R package for performing the
adequate bootstrap method, which reduces the bootstrap size based
on model adequacy. Full details are
in this paper.
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.
The link below provides brief documentation of the functions provided by the package.
A c++ version of the program is
available here.