Simulate Models Based on the Generalized Linear Model

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Documentation for package ‘simglm’ version 1.0.0

• DESCRIPTION file
• User guides, package vignettes and other documentation
• Package NEWS
• Package README

Help Pages

aggregate_outcome_by_level	Aggregate outcome to specified cluster level
compute_density_values	Convenience function for computing density values for plotting.
compute_statistics	Compute Power, Type I Error, or Precision Statistics
correlate_variables	Correlate elements
desireVar	Computes mixture normal variance
dropout_missing	Missing Data Functions
extract_coefficients	Extract Coefficients
fit_propensity	Primary propensity model fitting
generate_missing	Tidy Missing Data Function
generate_response	Simulate response variable
mar_missing	Missing Data Functions
missing_data	Missing Data Functions
model_fit	Tidy Model Fitting Function
parse_correlation	Parse correlation arguments
parse_formula	Parses tidy formula simulation syntax
parse_multiplemember	Parse Multiple Membership Random Effects
parse_power	Parse power specifications
parse_randomeffect	Parses random effect specification
parse_varyarguments	Parse between varying arguments
parse_varyarguments_w	Parse within varying arguments
random_missing	Missing Data Functions
rbimod	Simulating mixture normal distributions
replicate_simulation	Replicate Simulation
robust_model	Robust Model Standard Errors
run_shiny	Run Shiny Application Demo
simglm	Single wrapper function
simulate_error	Tidy error simulation
simulate_fixed	Tidy fixed effect formula simulation
simulate_heterogeneity	Tidy heterogeneity of variance simulation
simulate_knot	Simulate knot locations
simulate_propensity	Simulate Propensity Scores
simulate_randomeffect	Tidy random effect formula simulation
sim_continuous2	Simulate continuous variables
sim_factor2	Simulate categorical or factor variables
sim_ordinal2	Simulate discrete variables
sim_time	Simulate Time
transform_outcome	Transform response variable