gim - Generalized Integration Model
Implements the generalized integration model, which integrates individual-level data and summary statistics under a generalized linear model framework. It supports continuous and binary outcomes to be modeled by the linear and logistic regression models. For binary outcome, data can be sampled in prospective cohort studies or case-control studies. Described in Zhang et al. (2020)<doi:10.1093/biomet/asaa014>.
Last updated 3 years ago
3.00 score 5 scripts 162 downloadsmultipleOutcomes - Asymptotic Covariance Matrix of Regression Models for Multiple Outcomes
Regression models can be fitted for multiple outcomes simultaneously. This package computes estimates of parameters across fitted models and returns the matrix of asymptotic covariance. Various applications of this package, including CUPED (Controlled Experiments Utilizing Pre-Experiment Data), multiple comparison adjustment, are illustrated.
Last updated 9 months ago
2.30 score 1 scripts 262 downloadseasySimData - A Wrapper of 'simdata' Package
Simulating data according to marginal distributions and pairwise correlation. This is a wrapper for the 'simdata' package to make it easier to use.
Last updated 4 months ago
1.00 score 181 downloads