Function reference • miceafter

Functions to prepare Multiply Imputed datasets
`df2milist()`	Turns a data frame with multiply imputed data into an object of class 'milist'
`list2milist()`	Turns a list object with multiply imputed datasets into an object of class 'milist'.
`mids2milist()`	Turns a 'mice::mids' object into an object of class 'milist' to be further used by 'miceafter::with'
Function for Repeated Analysis
`with(<milist>)`	Evaluate an Expression across a list of multiply imputed datasets
Functions for Repeated Statistical Analysis
`bf_test()`	Calculates the Brown-Forsythe test.
`cindex()`	Calculates the c-index and standard error
`cor_est()`	Calculates the correlation coefficient
`levene_test()`	Calculates the Levene's test
`odds_ratio()`	Calculates the odds ratio (OR) and standard error.
`prop_nna()`	Calculates the posterior beta components for a single proportion
`prop_wald()`	Calculates a single proportion and related standard error according to Wald
`propdiff_ac()`	Calculates the difference between proportions and standard error according to method Agresti-Caffo
`propdiff_wald()`	Calculates the difference between proportions and standard error according to Wald
`risk_ratio()`	Calculates the risk ratio (RR) and standard error.
`t_test()`	Calculates the one, two and paired sample t-test
Functions to Pool Statistical Analyses
`pool_bftest()`	Calculates the pooled Brown-Forsythe test.
`pool_cindex()`	Calculates the pooled C-index and Confidence intervals
`pool_cor()`	Calculates the pooled correlation coefficient and Confidence intervals
`pool_D2()`	Combines the Chi Square statistics across Multiply Imputed datasets
`pool_glm()`	Pools and selects Linear and Logistic regression models across multiply imputed data.
`pool_levenetest()`	Calculates the pooled Levene test.
`pool_odds_ratio()`	Calculates the pooled odds ratio (OR) and related confidence interval.
`pool_prop_nna()`	Calculates the pooled proportion and confidence intervals using an approximate Beta distribution.
`pool_prop_wald()`	Calculates the pooled proportion and standard error according to Wald across multiply imputed datasets.
`pool_prop_wilson()`	Calculates the pooled single proportion confidence intervals according to Wilson across multiply imputed datasets.
`pool_propdiff_ac()`	Calculates the pooled difference between proportions and standard error according to Agresti-Caffo across multiply imputed datasets.
`pool_propdiff_nw()`	Calculates the pooled difference between proportions and confidence intervals according to Newcombe-Wilson (NW) across multiply imputed datasets.
`pool_propdiff_wald()`	Calculates the pooled difference between proportions and standard error according to Wald across multiply imputed datasets.
`pool_risk_ratio()`	Calculates the pooled risk ratio (RR) and related confidence interval.
`pool_scalar_RR()`	Rubin's Rules for scalar estimates
`pool_t_test()`	Calculates the pooled t-test and Confidence intervals
`glm_mi()`	Direct Pooling and model selection of Linear and Logistic regression models across multiply imputed data.
Extra Functions
`check_model()`	Function to check input data for function `glm_mi`
`clean_P()`	Function to clean variables
`cor2fz()`	Fisher z transformation of correlation coefficient
`f2chi()`	Converts F-values into Chi Square values
`fz2cor()`	Fisher z back transformation of correlation coefficient
`invlogit()`	Takes the inverse of a logit transformed value
`invlogit_ci()`	Takes the inverse of logit transformed parameters and calculates the confidence intervals
`logit_trans()`	Logit transformation of parameter estimates
`pool_D4()`	Pools the Likelihood Ratio tests across Multiply Imputed datasets ( method D4)
Datasets used in examples and tutorials
`lbp_orig`	Dataset of 159 Low Back Pain Patients with missing values
`lbpmicox`	Survival data of 265 Low Back Pain Patients
`lbpmilr`	Data of 159 Low Back Pain Patients

Reference

Functions to prepare Multiply Imputed datasets

Function for Repeated Analysis

Functions for Repeated Statistical Analysis

Functions to Pool Statistical Analyses

Extra Functions

Datasets used in examples and tutorials