Provides functions for making contour plots.
  The contour plot can be created from grid data, a function,
  or a data set. If non-grid data is given, then a Gaussian
  process is fit to the data and used to create the contour plot.

Plots a set of x,y,z co-ordinates in a contour map. Designed to be similar to plots in base R so additional elements can be added using lines(), points() etc. This package is intended to be better suited, than existing packages, to displaying circular shaped plots such as those often seen in the semi-conductor industry.

One degree of freedom contrasts for 'lm', 'glm', 'gls', and 'geese' objects.

Quickly set and summarize contrasts for factors prior to regression  analyses. Intended comparisons, baseline conditions, and intercepts can be explicitly set and documented without the user needing to directly manipulate matrices. Reviews and introductions for contrast coding are available in Brehm and Alday (2022)<doi:10.1016/j.jml.2022.104334> and Schad et al. (2020)<doi:10.1016/j.jml.2019.104038>.

Contrast trees represent a new approach for assessing the
    accuracy of many types of machine learning estimates that are not
    amenable to standard (cross) validation methods; see "Contrast
    trees and distribution boosting", Jerome H. Friedman (2020)
    <doi:10.1073/pnas.1921562117>. In situations where inaccuracies
    are detected, boosted contrast trees can often improve
    performance. Functions are provided to to build such trees in
    addition to a special case, distribution boosting, an assumption
    free method for estimating the full probability distribution of an
    outcome variable given any set of joint input predictor variable
    values.

A Bayesian approach to using 
    predictive probability in an ANOVA construct with a continuous normal response, 
    when threshold values must be obtained for the question of interest to be 
    evaluated as successful (Sieck and Christensen (2021) <doi:10.1002/qre.2802>). 
    The Bayesian Mission Mean (BMM) is used to evaluate a question 
    of interest (that is, a mean that randomly selects combination of factor levels 
    based on their probability of occurring instead of averaging over the factor 
    levels, as in the grand mean). Under this construct, in contrast to a Gibbs 
    sampler (or Metropolis-within-Gibbs sampler), a two-stage sampling method is 
    required. The nested sampler determines the conditional posterior distribution 
    of the model parameters, given Y, and the outside sampler determines the marginal 
    posterior distribution of Y (also commonly called the predictive distribution for Y). 
    This approach provides a sample from the joint posterior distribution of Y and 
    the model parameters, while also accounting for the threshold value that must be 
    obtained in order for the question of interest to be evaluated as successful.