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missMethods 0.4.0

count_NA() counts the number of missing values in a vector, data frame or matrix.

apply_imputation() and friends now have an option to convert tibbles instead of throwing an error.

delete_MAR_1_to_x() and delete_MNAR_1_to_x() now handle unordered factors as documented (thanks to Steve Roehrig for reporting).
delete_MAR_1_to_x() and delete_MNAR_1_to_x() now display the correct adjusted x value, if it is too high or too low (thanks to Steve Roehrig for reporting).
apply_imputation() type rowwise now works for data frames (thanks to @khughitt for fixing).

All delete_ functions have the argument n_mis_stochastic now. For some functions, this is only a renaming of the old stochastic argument (e.g. delete_MCAR()), for others this is completely new. The new name emphasis that this argument controls if the number of missing values is stochastic or deterministic.
delete_MAR_1_to_x() and delete_MNAR_1_to_x() get a new argument x_stochastic along the line of n_mis_stochastic.
Add the (package-wide) option missMethods.warn.too.high.p to control the displaying of warnings for too high values of p (the probability for a value to be missing).

delete_values() and get_NA_indices() centralize many steps of the old (not exported) delete_ functions.
All delete_MAR_ and delete_MNAR_ functions and delete_MCAR() call the new delete_values() function now.
Most of the delete_ functions use the new get_NA_indices() to determine the missing values.

Evaluation functions can now compare a data frame with a matrix (thanks to Marie Feldhoff for the suggestion).
impute_EM() now returns the number of performed EM iterations as attribute.
delete_rank() now hands the argument ties.method over to rank().
Fix two tests for delete_one_group() (wrong argument FUN instead of cutoff_fun).
Correct documentation of median.factor() (thanks to @labachevskij).
impute_LS_adaptive() has now the default setting warn_r_max = FALSE.
Remove LazyData from DESCRIPTION (fix for a CRAN NOTE).

impute_in_classes() allows to apply any imputation function inside imputation classes
impute_hot_deck_in_classes() hot deck imputation inside of imputation classes (adjustment cells)
impute_EM() imputes values using EM parameter estimates
imputed_expected_values() imputes expected values from a multivariate normal distribution
impute_LS_adaptive() performs LSimpute_adaptive as described by Bo et al. (2004)
impute_LS_array() performs LSimpute_array as described by Bo et al. (2004)
impute_LS_combined() performs LSimpute_combined as described by Bo et al. (2004)
impute_LS_gene() performs LSimpute_gene as described by Bo et al. (2004)

add cov_only and cor_only as parameter in evaluate_imputation_parameters()
improve vignette “Generating missing values” (add connections to Santos et al. (2019))
rename of cols variables: now all should be named cols_mis, cols_ctrl etc.
rename of ds variables: now all should be named ds_imp, ds_orig etc.
rename of pars variables: now all should be named pars_est or pars_true
sampling of sRHD type cols_seq is now correct, if the donor is only one numeric value
use markdown for documentation of new functions

Functions for the creation of missing values:

delete_MAR_censoring() and delete_MNAR_censoring() create missing (not) at random values using a censoring mechanism
delete_MAR_one_group() and delete_MNAR_one_group() create missing (not) at random values by deleting values in one of two groups
delete_MAR_rank() and delete_MNAR_rank() create missing (not) at random values using a ranking mechanism

Functions for evaluation:

evaluate_imputation_parameters() compares estimated parameters after imputation to true parameters

delete_MAR_1_to_x() and delete_MNAR_1_to_x() can now handle (unordered) factors
new criteria for evaluate_imputed_values() and evaluate_parameters(): six forms of NRMSE, nr_equal, nr_NA and precision
evaluate_imputed_values(): add argument cols_which to select columns for evaluation.

all delete_ functions now take the same first three arguments: ds, p, cols_mis
package now on GitHub and CRAN

Functions for the creation of missing values:

delete_MCAR() creates missing completely at random values in different ways
delete_MAR_1_to_x() and delete_MNAR_1_to_x() create missing (not) at random values using a 1:x mechanism

Functions for imputation:

impute_mean(), impute_median(), impute_mode() different forms of mean, median and mode imputation
impute_sRHD() simple Random Hot-Deck imputation with the possibility to specify a donor limit
apply_imputation() a function to apply aggregating functions for imputation

Functions for evaluation:

Miscellaneous:

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