Title: Design and analysis of mixture experiments
Description: This package contains functions for creating designs for
mixture experiments, making ternary contour plots, and making

Author: John Lawson <lawson@byu.edu> and Cameron Willden <ccwillden@gmail.com>, with contributions from Greg Piepel
Description: This package contains functions for creating designs for mixture experiments, making ternary contour plots, and making mixture effect plots.

Description: Functions for creating designs for mixture experiments, making ternary contour plots, and making mixture effect plots.

Title: Design and Analysis of Mixture Experiments

Title: Computes false discovery rates and effect sizes using normal
Author: Omkar Muralidharan, with many suggestions from Bradley Efron
Description: This package fits normal mixture models to data and uses
them to compute effect size estimates and local and tail area
false discovery rates. To make this precise, suppose you have
many normally distributed z's, and each z[i] has mean delta[i].
This package will estimate delta[i] based on the z's (effect
sizes), P(delta[i]=0|z[i]) (local false discovery rates) and
P(delta[i]=0||z[i]|>z) (tail area false discovery rates).

Title: Multiple Imputation Through 'XGBoost'
person(given = "Yongshi", family ="Deng", email = "yongshi.deng@auckland.ac.nz",
person(given = "Thomas", family = "Lumley", email = "t.lumley@auckland.ac.nz",
Description: Multiple imputation using 'XGBoost', bootstrapping and predictive mean
matching as described in Deng and Lumley (2021) <arXiv:2106.01574>. It is built
under Fully Conditional Specification, where 'XGBoost' imputation models are
built for each incomplete variable. It supports various types of variables and
offers different settings regarding bootstrapping and predictive mean matching.
Visual diagnostic functions are also provided for inspecting multiply imputed
Imports: data.table, ggplot2, Matrix, mice, Rfast, rlang, scales,
Author: Yongshi Deng [aut, cre] (<https://orcid.org/0000-0001-5845-859X>),