Substantive model compatible fully conditional specification multiple imputation can be useful for imputing missing values in covariates in a way which accommodates the form of the substantive/outcome model. One of its drawbacks compared to standard FCS imputation, as implemented in the mice package in R, is its higher computational burden. This is due to the use of rejection sampling when imputing missing values in continuous covariates.

I am happy to announce that thanks to the efforts of Edouard Bonneville, the smcfcs package in R now supports the use of multiple cores by parallel processing. The package now has a function smcfcs.parallel. This can be used to call the other smcfcs functions in parallel. Having specified the number of imputations desired, smcfcs.parallel splits these across the number of cores/processors specified by the user in the n_core argument. Since multiple imputation is ’embarrassingly parallel’, substantial speed improvements can be achieved. Many thanks to Ed for his continuing contributions to smcfcs in R.