This bug-fix updates the package documentation in response to a new roxygen2 bug.
This version includes minor changes implemented for CRAN approval. Namely, return values were added to several functions which did not have a documented value in the initial submission.
This is a new package that defines functions to describe regression models using only pre-computed summary statistics (PCSS) (i.e. means, variances, and covariances) in place of individual participant data. Possible models include linear models for linear combinations, products, and logical combinations of phenotypes. These methods were originally presented in
Wolf, J.M., Westra, J., and Tintle, N. (2021). Using summary statistics to evaluate the genetic architecture of multiplicative combinations of initially analyzed phenotypes with a flexible choice of covariates. bioRxiv. https://doi.org/10.1101/2021.03.08.433979.
Wolf, J.M., Barnard, M., Xueting, X., Ryder, N., Westra, J., and Tintle, N. (2020). Computationally efficient, exact, covariate-adjusted genetic principal component analysis by leveraging individual marker summary statistics from large biobanks. Pacific Symposium on Biocomputing, 25, 719-730. https://doi.org/10.1142/9789811215636_0063.
Gasdaska A., Friend D., Chen R., Westra J., Zawistowski M., Lindsey W. and Tintle N. (2019) Leveraging summary statistics to make inferences about complex phenotypes in large biobanks. Pacific Symposium on Biocomputing, 24, 391-402. https://doi.org/10.1142/9789813279827_0036.
pcsslm() approximates a linear model of a combination of variables using PCSS
anova.pcsslm() approximates an analysis of variance table for one or more linear models fit using PCSS