Compare two nested models using D1-statistic
Compare two nested models using D2-statistic
Compare two nested models using D3-statistic
Generate missing data for simulation purposes
Multivariate amputation based on continuous probability functions
Multivariate amputation based on discrete probability functions
Multivariate amputation under a MCAR mechanism
Converts an imputed dataset (long format) into a 'mids' object
Create a 'mira' object from repeated analyses
Brandsma school data used Snijders and Bosker (2012)
Box-and-whisker plot of amputed and non-amputed data
Box-and-whisker plot of observed and imputed data
Extracts the completed data from a 'mids' object
Construct blocks from 'formulas' and 'predictorMatrix'
Density plot of observed and imputed data
Extends formula's with predictor matrix settings
Extract broken stick estimates from a 'lmer' object
Fraction of incomplete cases among cases with observed
Influx and outflux of multivariate missing data patterns
Glance method to extract information from a 'mipo' object
Generalized linear model for 'mids' object
Enlarge number of imputations by combining 'mids' objects
Jamshidian and Jalal's Non-Parametric MCAR Test
Graphical parameter for missing data plots
'mice': Multivariate Imputation by Chained Equations
Imputation by a two-level logistic model using 'glmer'
Imputation by a two-level normal model using 'lmer'
Imputation by a two-level normal model using 'pan'
Imputation of most likely value within the class
Imputation at level 2 by Bayesian linear regression
Imputation at level 2 by predictive mean matching
Imputation by classification and regression trees
Multivariate multilevel imputation using 'jomo'
Imputation by direct use of lasso logistic regression
Imputation by direct use of lasso linear regression
Imputation by indirect use of lasso logistic regression
Imputation by indirect use of lasso linear regression
Imputation by linear discriminant analysis
Imputation by logistic regression using the bootstrap
Imputation by predictive mean matching with distance aided donor selection
Imputation under MNAR mechanism by NARFCS
Imputation by Bayesian linear regression
Imputation by linear regression, bootstrap method
Imputation by linear regression without parameter uncertainty
Imputation by linear regression through prediction
Impute multilevel missing data using 'pan'
Imputation of ordered data by polytomous regression
Imputation of unordered data by polytomous regression
Imputation by the random indicator method for nonignorable data
Multivariate Imputation by Chained Equations (Iteration Step)
Set the theme for the plotting Trellis functions
'mipo': Multiple imputation pooled object
Multiply imputed repeated analyses ('mira')
Cumulative hazard rate or Nelson-Aalen estimator
NHANES example - all variables numerical
NHANES example - mixed numerical and discrete variables
Draws values of beta and sigma by Bayesian linear regression
Wrapper function that runs MICE in parallel
Datasets with various missing data patterns
Plot the trace lines of the MICE algorithm
Finds an imputed value from matches in the predictive metric (deprecated)
Compare two nested models fitted to imputed data
Pools R^2 of m models fitted to multiply-imputed data
Multiple imputation pooling: univariate version
Hox pupil popularity data with missing popularity scores
Project on preterm and small for gestational age infants (POPS)
Quick selection of predictors from the data
Squeeze the imputed values to be within specified boundaries.
Tidy method to extract results from a 'mipo' object
Evaluate an expression in multiple imputed datasets
Scatterplot of amputed and non-amputed data against weighted sum scores
Scatterplot of observed and imputed data