.pmm.match              Finds an imputed value from matches in the
                        predictive metric (deprecated)
D1                      Compare two nested models using D1-statistic
D2                      Compare two nested models using D2-statistic
D3                      Compare two nested models using D3-statistic
ampute                  Generate missing data for simulation purposes
anova.mira              Compare several nested models
appendbreak             Appends specified break to the data
as.mids                 Converts an imputed dataset (long format) into
                        a 'mids' object
as.mira                 Create a 'mira' object from repeated analyses
as.mitml.result         Converts into a 'mitml.result' object
boys                    Growth of Dutch boys
brandsma                Brandsma school data used Snijders and Bosker
                        (2012)
bwplot.mads             Box-and-whisker plot of amputed and non-amputed
                        data
bwplot.mids             Box-and-whisker plot of observed and imputed
                        data
cbind                   Combine R objects by rows and columns
cc                      Select complete cases
cci                     Complete case indicator
complete.mids           Extracts the completed data from a 'mids'
                        object
construct.blocks        Construct blocks from 'formulas' and
                        'predictorMatrix'
convergence             Computes convergence diagnostics for a 'mids'
                        object
densityplot.mids        Density plot of observed and imputed data
employee                Employee selection data
estimice                Computes least squares parameters
extractBS               Extract broken stick estimates from a 'lmer'
                        object
fdd                     SE Fireworks disaster data
fdgs                    Fifth Dutch growth study 2009
fico                    Fraction of incomplete cases among cases with
                        observed
filter.mids             Subset rows of a 'mids' object
fix.coef                Fix coefficients and update model
flux                    Influx and outflux of multivariate missing data
                        patterns
fluxplot                Fluxplot of the missing data pattern
futuremice              Wrapper function that runs MICE in parallel
getfit                  Extract list of fitted models
getqbar                 Extract estimate from 'mipo' object
glm.mids                Generalized linear model for 'mids' object
ibind                   Enlarge number of imputations by combining
                        'mids' objects
ic                      Select incomplete cases
ici                     Incomplete case indicator
is.mads                 Check for 'mads' object
is.mids                 Check for 'mids' object
is.mipo                 Check for 'mipo' object
is.mira                 Check for 'mira' object
is.mitml.result         Check for 'mitml.result' object
leiden85                Leiden 85+ study
lm.mids                 Linear regression for 'mids' object
mads                    Multivariate amputed data set ('mads')
make.blocks             Creates a 'blocks' argument
make.blots              Creates a 'blots' argument
make.calltype           Create calltype of the imputation model
make.formulas           Creates a 'formulas' argument
make.method             Creates a 'method' argument
make.post               Creates a 'post' argument
make.predictorMatrix    Creates a 'predictorMatrix' argument
make.visitSequence      Creates a 'visitSequence' argument
make.where              Creates a 'where' argument
mammalsleep             Mammal sleep data
matchindex              Find index of matched donor units
md.pairs                Missing data pattern by variable pairs
md.pattern              Missing data pattern
mdc                     Graphical parameter for missing data plots
mice.impute.2l.bin      Imputation by a two-level logistic model using
                        'glmer'
mice.impute.2l.lmer     Imputation by a two-level normal model using
                        'lmer'
mice.impute.2l.norm     Imputation by a two-level normal model
mice.impute.2l.pan      Imputation by a two-level normal model using
                        'pan'
mice.impute.2lonly.mean
                        Imputation of most likely value within the
                        class
mice.impute.2lonly.norm
                        Imputation at level 2 by Bayesian linear
                        regression
mice.impute.2lonly.pmm
                        Imputation at level 2 by predictive mean
                        matching
mice.impute.cart        Imputation by classification and regression
                        trees
mice.impute.jomoImpute
                        Multivariate multilevel imputation using 'jomo'
mice.impute.lasso.logreg
                        Imputation by direct use of lasso logistic
                        regression
mice.impute.lasso.norm
                        Imputation by direct use of lasso linear
                        regression
mice.impute.lasso.select.logreg
                        Imputation by indirect use of lasso logistic
                        regression
mice.impute.lasso.select.norm
                        Imputation by indirect use of lasso linear
                        regression
mice.impute.lda         Imputation by linear discriminant analysis
mice.impute.logreg      Imputation by logistic regression
mice.impute.logreg.boot
                        Imputation by logistic regression using the
                        bootstrap
mice.impute.mean        Imputation by the mean
mice.impute.midastouch
                        Imputation by predictive mean matching with
                        distance aided donor selection
mice.impute.mnar.logreg
                        Imputation under MNAR mechanism by NARFCS
mice.impute.mpmm        Imputation by multivariate predictive mean
                        matching
mice.impute.norm        Imputation by Bayesian linear regression
mice.impute.norm.boot   Imputation by linear regression, bootstrap
                        method
mice.impute.norm.nob    Imputation by linear regression without
                        parameter uncertainty
mice.impute.norm.predict
                        Imputation by linear regression through
                        prediction
mice.impute.panImpute   Impute multilevel missing data using 'pan'
mice.impute.passive     Passive imputation
mice.impute.pmm         Imputation by predictive mean matching
mice.impute.polr        Imputation of ordered data by polytomous
                        regression
mice.impute.polyreg     Imputation of unordered data by polytomous
                        regression
mice.impute.quadratic   Imputation of quadratic terms
mice.impute.rf          Imputation by random forests
mice.impute.ri          Imputation by the random indicator method for
                        nonignorable data
mice.impute.sample      Imputation by simple random sampling
mice.mids               Multivariate Imputation by Chained Equations
                        (Iteration Step)
mice.theme              Set the theme for the plotting Trellis
                        functions
mids                    Multiply imputed data set ('mids')
mids2mplus              Export 'mids' object to Mplus
mids2spss               Export 'mids' object to SPSS
mira                    Create an object of class "mira"
mnar_demo_data          MNAR demo data
name.blocks             Name imputation blocks
name.formulas           Name formula list elements
ncc                     Number of complete cases
nelsonaalen             Cumulative hazard rate or Nelson-Aalen
                        estimator
nhanes                  NHANES example - all variables numerical
nhanes2                 NHANES example - mixed numerical and discrete
                        variables
nic                     Number of incomplete cases
nimp                    Number of imputations per block
norm.draw               Draws values of beta and sigma by Bayesian
                        linear regression
parlmice                Wrapper function that runs MICE in parallel
pattern                 Datasets with various missing data patterns
pool                    Combine estimates by pooling rules
pool.compare            Compare two nested models fitted to imputed
                        data
pool.r.squared          Pools R^2 of m models fitted to
                        multiply-imputed data
pool.scalar             Multiple imputation pooling: univariate version
pool.table              Combines estimates from a tidy table
popmis                  Hox pupil popularity data with missing
                        popularity scores
pops                    Project on preterm and small for gestational
                        age infants (POPS)
potthoffroy             Potthoff-Roy data
predict_mi              Predict method for linear models with multiply
                        imputed data
print.mira              Print a 'mira' object
quickpred               Quick selection of predictors from the data
selfreport              Self-reported and measured BMI
squeeze                 Squeeze the imputed values to be within
                        specified boundaries.
stripplot.mids          Stripplot of observed and imputed data
summary.mira            Summary of a 'mira' object
supports.transparent    Supports semi-transparent foreground colors?
tbc                     Terneuzen birth cohort
toenail                 Toenail data
toenail2                Toenail data
version                 Echoes the package version number
walking                 Walking disability data
windspeed               Subset of Irish wind speed data
with.mids               Evaluate an expression in multiple imputed
                        datasets
xyplot.mads             Scatterplot of amputed and non-amputed data
                        against weighted sum scores
xyplot.mids             Scatterplot of observed and imputed data
