Sample Size Calculation for High Dimensional Classification
Study
Meihua Wu <meihuawu@umich.edu>,
Brisa N. Sanchez <brisa@umich.edu>,
Peter X.K. Song <pxsong@umich.edu>,
Raymond Luu <raluu@umich.edu>,
Wen Wang <wangwen@umich.edu>
Determine the sample size requirement to achieve the target probability of correct classification (PCC) for studies employing high-dimensional features.  The package implements functions to 1) determine the asymptotic feasibility of the classification problem; 2) compute the upper bounds of the PCC for any linear classifier; 3) estimate the PCC of three design methods given design assumptions; 4) determine the sample size requirement to achieve the target PCC for three design methods.

Tests for High Dimensional Generalized Linear Models
Test the significance of coefficients in high dimensional generalized linear models.

A generic function and a set of methods to calculate highest density intervals for a variety of classes of objects which can specify a probability density distribution, including MCMC output, fitted density objects, and functions.

c(person("Ngumbang", "Juat", role="cre", email="ngumbangjuat@hotmail.com"),
person("Mike", "Meredith", role="aut"),
person("John", "Kruschke", role="aut"))
Ngumbang Juat [cre],
Mike Meredith [aut],
John Kruschke [aut]

Distribution-Free Exact High Dimensional Low Sample Size
k-Sample Tests
c(person("Biplab", "Paul", role = c("aut", "cre"), email = "biplab.paul@niser.ac.in"),
person("Shyamal K.", "De", role = c("aut")),
person("Anil K.", "Ghosh", role = c("aut")))
We construct four new exact level (size) alpha tests for testing the equality
of k distributions, which can be conveniently used in high dimensional low sample
size setup based on clustering. These tests are easy to implement and distribution-free.
Under mild conditions, we have proved the consistency of these tests as the dimension
d of each observation grows to infinity, whereas the sample size remains fixed.
We also apply step-down-procedure (1979) for multiple testing. Details are in
Biplab Paul, Shyamal K De and Anil K Ghosh (2020); Soham Sarkar and Anil K Ghosh (2019)
<doi:10.1109/TPAMI.2019.2912599>; William M Rand (1971) <doi:10.1080/01621459.1971.10482356>;
Cyrus R Mehta and Nitin R Patel (1983) <doi:10.2307/2288652>; Joseph C Dunn (1973)
<doi:10.1080/01969727308546046>; Sture Holm (1979) <doi:10.2307/4615733>.
Biplab Paul [aut, cre],
Shyamal K. De [aut],
Anil K. Ghosh [aut]