Package index • describe

All functions

check.mar(): MAR check using logistic regression

check.mcar(): Little's test for missing completely at random (MCAR)

check.missing(): Check percent of missing values in data frame or matrix

cmean.impute(): Conditional Mean Imputation (CMI)

coldeck.impute(): Cold deck imputation

gen.mar(): Generate a data frame with values missing at random

gen.mcar(): Generate a data frame with values missing completely at random

gen.mnar(): Generate a data frame with values missing not at random

geom.mad(): Calculate the median of distances to the geometric mean

geom.mean(): Calculate the geometric mean

geom.median(): Calculate the geometric median

geom.sd(): Calculate the geometric standard deviation (GSD)

geom.var(): Calculate the geometric variance

geom.zscore(): Calculate the geometric standardized score

grouped.mean(): @title Calculate the grouped mean

grouped.median(): Calculate the grouped median

harm.mean(): Calculate the harmonic mean

hotdeck.impute(): Hot deck imputation

kurtosis(): Calculate kurtosis

mar.transform(): Transform data to missing values at random.

mcar.transform(): Transform data to missing values completely at random

mnar.transform(): Transform data to missing values not at random

pmean.match(): Predictive mean matching imputation (PMI)

quad.mean(): Calculate the quadratic mean

skewness(): Calculate skewness

stoc.impute(): Stochastic regression imputation (SRI)

variance(): Calculate variance

weight.harm.mean(): Calculate the weighted harmonic mean

z.scores(): Calculate z-scores