Functions to justify alpha levels for statistical hypothesis tests by avoiding Lindley's paradox, or by minimizing or balancing error rates. For more information about the package please read the following: Maier & Lakens (2021) <doi:10.31234/osf.io/ts4r6>).

Install packages without attaching them. If a package it is already installed, it will be skipped.

Provides functions to standardize and whiten data, and to perform
  Principal Component Analysis (PCA).  The main advantage of this
  package over alternatives like prcomp() is, that jvcoords makes it
  easy to convert (additional) data between the original and the
  transformed coordinates.  The package also provides a class coords,
  which can represent affine coordinate transformations.  This class
  forms the basis of the transformations provided by the package, but
  can also be used independently.  The implementation has been
  optimized to be of comparable speed (and sometimes even faster) than
  existing alternatives.

Miscellaneous tools and functions,
    including: generate descriptive statistics tables,
    format output, visualize relations among variables or check
    distributions, and generic functions for residual and
    model diagnostics.

Quality of life functions for interactive programming.
    Shortcuts for common combinations of functions or different default
    arguments. Not to be used in production level scripts, but useful for
    exploring and quickly manipulating data for easy analysis. Also
    imports a variety of packages to facilitate the installation of those
    imported packages on the host machine.

Build complex HTML or 'LaTeX' tables using 'kable()' from 'knitr' 
    and the piping syntax from 'magrittr'. Function 'kable()' is a light weight 
    table generator coming from 'knitr'. This package simplifies the way to 
    manipulate the HTML or 'LaTeX' codes generated by 'kable()' and allows 
    users to construct complex tables and customize styles using a readable 
    syntax.

Implementation of various kernel adaptive methods in nonparametric curve
    estimation like density estimation as introduced in Stute and Srihera (2011)
    <doi:10.1016/j.spl.2011.01.013> and Eichner and Stute (2013)
    <doi:10.1016/j.jspi.2012.03.011> for pointwise estimation, and like regression
    as described in Eichner and Stute (2012) <doi:10.1080/10485252.2012.760737>.