Package index

All functions

assessBalance() print(<devMSM_bal_stats>) summary(<devMSM_bal_stats>) plot(<devMSM_bal_stats>)

Assesses confounder balancing

compareHistories() print(<devMSM_comparisons>) plot(<devMSM_comparisons>) summary(<devMSM_comparisons>)

Estimate, compare, and visualize exposure histories

createFormulas() print(<devMSM_formulas>)

Create balancing formulas

createWeights() print(<devMSM_weights>) plot(<devMSM_weights>) summary(<devMSM_weights>)

Creates IPTW balancing weights

fitModel() print(<devMSM_models>)

Fit outcome model

initMSM()

Initial step in devMSMs workflow

sim_data_imp_list

Wide data imputed and read in (continuous exposure)

sim_data_long_miss

Long data with missingness (continuous exposure)

sim_data_long_miss_bin

Long data with missingness (binary exposure)

sim_data_mice

Wide data imputed with mice (continuous exposure)

sim_data_wide

Wide complete data (continuous exposure) These data are simulated based on data from the Family Life Project (FLP), a longitudinal study following 1,292 families representative of two geographic areas (three counties in North Carolina and three counties in Pennsylvania) with high rural child poverty (Vernon-Feagans et al., 2013; Burchinal et al., 2008). These data contain economic strain (ESEATA1) as a continuously distributed variable and have no missing data.

sim_data_wide_bin

Wide complete data (binary exposure) These data are simulated based on data from the Family Life Project (FLP), a longitudinal study following 1,292 families representative of two geographic areas (three counties in North Carolina and three counties in Pennsylvania) with high rural child poverty (Vernon-Feagans et al., 2013; Burchinal et al., 2008). These data contain economic strain (ESEATA1) as a binary variable, and have no missing data.

sim_data_wide_miss

Wide data with missingness (continuous exposure)

sim_data_wide_miss_bin

Wide data with missingness (binary exposure)

trimWeights()

Trim IPTW balancing weights, if needed