Estimating Marginal Effects with Prognostic Covariate Adjustment


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Documentation for package ‘postcard’ version 1.0.0

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coef.rctglm Methods for objects of class 'rctglm'
default_learners Creates a list of learners
est Methods for objects of class 'rctglm'
estimand Methods for objects of class 'rctglm'
estimand.rctglm Methods for objects of class 'rctglm'
fit_best_learner Find the best learner in terms of RMSE among specified learners using cross validation
glm_data Generate data simulated from a GLM
options postcard Options
power_gs Power and sample size estimation for linear models
power_linear Power and sample size estimation for linear models
power_marginaleffect Power approximation for estimating marginal effects in GLMs
power_nc Power and sample size estimation for linear models
print.rctglm Methods for objects of class 'rctglm'
prog Extract information about the fitted prognostic model
prog.rctglm_prog Extract information about the fitted prognostic model
rctglm Fit GLM and find any estimand (marginal effect) using plug-in estimation with variance estimation using influence functions
rctglm_methods Methods for objects of class 'rctglm'
rctglm_with_prognosticscore Use prognostic covariate adjustment when fitting an rctglm
samplesize_gs Power and sample size estimation for linear models
variance_ancova Power and sample size estimation for linear models