The general workflow of most imputation methods is quite
    similar. The aim of this package is to provide parts of this general
    workflow to make the implementation of imputation methods easier. The
    heart of an imputation method is normally the used model. These models
    can be defined using the 'parsnip' package or customized
    specifications. The rest of an imputation method are more technical
    specification e.g. which columns and rows should be used for
    imputation and in which order. These technical specifications can be
    set inside the imputation functions.

A collection of functions for left-censored missing data imputation. Left-censoring is a special case of missing not at random (MNAR)  mechanism that generates non-responses in proteomics experiments. The package also contains functions to artificially generate peptide/protein expression data (log-transformed) as random draws from a multivariate Gaussian distribution as well as a function to generate missing data (both randomly and non-randomly). For comparison reasons, the package also contains several wrapper functions for the imputation of non-responses that are missing at random. * New functionality has been added: a hybrid method that allows the imputation of missing values in a more complex scenario where the missing data are both MAR and MNAR.

Imputation of longitudinal categorical covariates. We use a methodological framework which ensures that the plausibility of transitions is preserved, overfitting and colinearity issues are resolved, and confounders can be utilized. See Mamouris (2023) <doi:10.1002/sim.9919> for an overview.

Compute missing values on a training data set and impute them on a new data set. Current available options are median/mode and random forest.

Implements imputation methods using EM and Data Augmentation for
    multinomial data following the work of Schafer 1997 <ISBN: 978-0-412-04061-0>.

Multivariate Expectation-Maximization (EM) based imputation framework that offers several different algorithms. These include regularisation methods like Lasso and Ridge regression, tree-based models and dimensionality reduction methods like PCA and PLS.