Design of Rotatable Central Composite Experiments and Response Surface Analysis (rsurface)
Produces tables with the level of replication (number of replicates) and the experimental uncoded values of the quantitative factors to be used for rotatable Central Composite Design (CCD) experimentation and a 2-D contour plot of the corresponding variance of the predicted response according to Mead et al. (2012) <doi:10.1017/CBO9781139020879> design_ccd(), and analyzes CCD data with response surface methodology ccd_analysis(). A rotatable CCD provides values of the variance of the predicted response that are concentrically distributed around the average treatment combination used in the experimentation, which with uniform precision (implied by the use of several replicates at the average treatment combination) improves greatly the search and finding of an optimum response. These properties of a rotatable CCD represent undeniable advantages over the classical factorial design, as discussed by Panneton et al. (1999) <doi:10.13031/2013.13267> and Mead et al. (2012) <doi:10.1017/CBO9781139020879.018> among others.

Extrema-Weighted Feature Extraction (xwf)
Extrema-weighted feature extraction for varying length functional data. Functional data analysis method that performs dimensionality reduction based on predefined features and allows for quantile weighting. Method implemented as presented in Van den Boom et al. (2017) <arXiv:1709.10467>.

Check if an ‘externalptr’ is a Null Pointer (isnullptr)
Check if an ‘externalptr’ is a null pointer. R does currently not have a native function for that purpose. This package contains a C function that returns TRUE in case of a null pointer.

Grab Bag of ‘ggplot2’ Functions (ggallin)
Extra geoms and scales for ‘ggplot2’, including geom_cloud(), a Normal density cloud replacement for errorbars; transforms ssqrt_trans and pseudolog10_trans, which are loglike but appropriate for negative data; interp_trans() and warp_trans() which provide scale transforms based on interpolation; and an infix compose operator for scale transforms.

Kernel Fisher Discriminant Analysis (kfda)
Kernel Fisher Discriminant Analysis (KFDA) is performed using Kernel Principal Component Analysis (KPCA) and Fisher Discriminant Analysis (FDA). There are some similar packages. First, ‘lfda’ is a package that performs Local Fisher Discriminant Analysis (LFDA) and performs other functions. In particular, ‘lfda’ seems to be impossible to test because it needs the label information of the data in the function argument. Also, the ‘ks’ package has a limited dimension, which makes it difficult to analyze properly. This package is a simple and practical package for KFDA based on the paper of Yang, J., Jin, Z., Yang, J. Y., Zhang, D., and Frangi, A. F. (2004) <DOI:10.1016/j.patcog.2003.10.015>.

Create FDA-Style Data and Program Definitions (define)
Creates a directory of archived files with a descriptive ‘PDF’ document at the root level (i.e. ‘define.pdf’) containing tables of definitions of data items and relative-path hyperlinks to the documented files. Converts file extensions to ‘txt’ per FDA expectations and converts ‘CSV’ files to ‘SAS’ Transport format. Relies on data item descriptors stored as per R package ‘spec’. See ‘package?define’. See also ‘?define’. Requires a compatible installation of ‘pdflatex’, e.g. <https://…/>.

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