Imports: utils, stats, methods, stringr, Rcpp

Title: The hidden F-test for nonadditivity
Author: Jason A. Osborne and Christopher T. Franck
Maintainer: Jason A. Osborne <jaosborn@ncsu.edu>
Description: Conducts the hidden F-test for nonadditivity

Title: Conduct the F test for hidden additivity in two-factor
Description: Compute the maximal F-ratio and Bonferroni-adjusted pvalue for a test of no interaction between group and Factor 2 among all possible assignments of levels of Factor 1 into two groups.  Generate ANOVA table and interaction plot corresponding to this maximal configuration.

Title: The all-Configurations, Maximum-Interaction F-Test for Hidden
Author: Jason A. Osborne, Christopher T. Franck and Bongseog Choi
Description: Computes the F-test and Bonferroni-adjusted p-value for a test of interaction in two-factor studies.  Reports corresponding interaction plot and analysis of variance table and p-values from several other tests of non-additivity.

Title: The All-Configurations, Maximum-Interaction F-Test for Hidden

Description: Computes the ACMIF test and Bonferroni-adjusted p-value of interaction in two-factor studies.  Produces corresponding interaction plot and analysis of variance tables and p-values from several other tests of non-additivity.

Title: Identify Influential Observations in High Dimension
Description: Efficient tool for identifying influential observations in
high dimensional linear regression. The tool implements two detection
techniques single detection (Barry et al. (2020)
al. (2021) <arXiv:2105.12286>).  The single detection is an adaptation
of Cook's measure for high dimensional data. The method relies on the
concept of expectile to construct an influence measure based on
asymmetric correlations. The multiple detection technique applies a
group deletion procedure to build the algorithm on three main steps.
The first stage applies an ultra conservative score to mitigate the
swamping effect, the second stage uses the clean sample generated in
the previous stage and applies an aggressive score to attenuate the
masking phenomenon. Finally, the last step is concerned with the
validation of the influential set generated by the two previous steps.
The main functions take a response variable and a design matrix as
input and output a set of potential influential observations.
Packaged: 2021-08-18 23:50:56 UTC; Amadou.Barry