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.

Joint dimension reduction and spatial clustering is conducted for
    Single-cell RNA sequencing and spatial transcriptomics data, and more details can be referred to 
    Wei Liu, Xu Liao, Yi Yang, Huazhen Lin, Joe Yeong, Xiang Zhou, Xingjie Shi and Jin Liu. (2022) <doi:10.1093/nar/gkac219>. It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the smoothness parameter and the number of clusters as well.

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.