Changes in version 0.4.0 (2022-09-16)                  

New functions

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

Improvements

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

Fixes

  - 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).

Internal

  - delete_values() now only takes mech_type and derives mechanism.

                 Changes in version 0.3.0 (2022-02-10)                  

Update of delete_ functions

User-visible changes

  - 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).

Internal

  - 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.

Miscellaneous

  - 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).

                 Changes in version 0.2.0 (2020-07-30)                  

New functions

  - 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)

Miscellaneous

  - 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

                 Changes in version 0.1.0 (2020-04-01)                  

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

New features

  - 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.

Miscellaneous

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

                        Changes in version 0.0.1                        

Implemented functions

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:

  - evaluate_imputed_values() compares imputed to true values
  - evaluate_parameters() compares estimated to true parameters

Miscellaneous:

  - median.factor() computes medians for ordered factors