Functions, methods, and datasets for fitting dimension
 reduction regression, using slicing (methods SAVE and SIR), Principal
 Hessian Directions (phd, using residuals and the response), and an
 iterative IRE.  Partial methods, that condition on categorical
 predictors are also available.  A variety of tests, and stepwise
 deletion of predictors, is also included.  Also included is
 code for computing permutation tests of dimension.  Adding additional
 methods of estimating dimension is straightforward.
 For documentation, see the vignette in the package.   With version 3.0.4,
 the arguments for dr.step have been modified.

Models the relationship between dose levels and responses in a pharmacological experiment using the 4 Parameter Logistic model. Traditional packages on dose-response modelling such as 'drc' and 'nplr' often draw errors due to convergence failure especially when data have outliers or non-logistic shapes. This package provides robust estimation methods that are less affected by outliers and other initialization methods that work well for data lacking logistic shapes. We provide the bounds on the parameters of the 4PL model that prevent parameter estimates from diverging or converging to zero and base their justification in a statistical principle. These methods are used as remedies to convergence failure problems.  Gadagkar, S. R. and Call, G. B. (2015) <doi:10.1016/j.vascn.2014.08.006> Ritz, C. and Baty, F. and Streibig, J. C. and Gerhard, D. (2015) <doi:10.1371/journal.pone.0146021>.

Decodes meshes and point cloud data encoded by the Draco mesh
    compression library from Google. Note that this is only designed for basic
    decoding and not intended as a full scale wrapping of the Draco library.

Statistical tests and test statistics to identify events in a
    dataset that are dragon kings (DKs). The statistical methods in this
    package were reviewed in Wheatley & Sornette (2015) <doi:10.2139/ssrn.2645709>.

These are data sets for the hit TV show, RuPaul's Drag Race. 
    Data right now include episode-level data, contestant-level data, and 
    episode-contestant-level data. This is a work in progress, and a love letter
    of a kind to RuPaul's Drag Race and the performers that have appeared on the show.
    This may not be the most productive use of my time, but I have tenure and what 
    are you going to do about it? I think there is at least some value in this package
    if it allows the show's fandom to learn more about the R programming language 
    around its contents.