Title: Automated Moderated Nonlinear Factor Analysis Using 'M-plus'
person("Isabella", "Stallworthy", email = "stall142@umn.edu", role = c("aut")),
person("Meriah", "DeJoseph", email = "dejos002@umn.edu", role = c("aut")),
person("Robin", "Sifre", email = "robinsifre@gmail.com", role = c("aut")),
Description: Automated generation, running, and interpretation of moderated nonlinear factor analysis models for obtaining scores from observed variables, using the method described by Gottfredson and colleagues (2019) <doi:10.1016/j.addbeh.2018.10.031>. This package creates M-plus input files which may be run iteratively to test two different types of covariate effects on items: (1) latent variable impact (both mean and variance); and (2) differential  item functioning. After sequentially testing for all effects, it also creates a final model by including all significant effects after adjusting for multiple comparisons. Finally, the package creates a scoring model which uses the final values of parameter estimates to generate latent variable scores. \n\n This package generates TEMPLATES for M-plus inputs, which can and should be inspected, altered, and run by the user. In addition to being presented without warranty of any kind, the package is provided under the assumption that everyone who uses it is reading, interpreting, understanding, and altering every M-plus input and output file. There is no one right way to implement moderated nonlinear factor analysis, and this package exists solely to save users time as they generate M-plus syntax according to their own judgment.
Imports: grDevices, graphics, stats, ggplot2, MplusAutomation,
reshape2, gridExtra, stringr, plyr, devtools, dplyr, stringi

Description: In fields such as ecology, microbiology, and genomics, non-Euclidean distances are widely applied to describe pairwise dissimilarity between samples. Given these pairwise distances, principal coordinates analysis (PCoA) is commonly used to construct a visualization of the data. However, confounding covariates can make patterns related to the scientific question of interest difficult to observe. We provide 'aPCoA' as an easy-to-use tool to improve data visualization in this context, enabling enhanced presentation of the effects of interest. Details are described in Yushu Shi, Liangliang Zhang, Kim-Anh Do, Christine Peterson and Robert Jenq (2020) <arXiv:2003.09544>.
Imports: vegan, randomcoloR, mvabund, ape, car, cluster