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.

Set of functions to impute missing rare earth data, calculate La 
    and Pr concentrations and Ce anomalies in zircons based on the
    Chondrite-Onuma and Chondrite-Lattice of Carrasco-Godoy and 
    Campbell (2023) <doi:10.1007/s00410-023-02025-9> and the Logarithmic regression from  
    Zhong et al. (2019) <doi:10.1007/s00710-019-00682-y>.

Provides new imputation methods for the 'mice' package based on generalized additive models for location, scale, and shape (GAMLSS) as described in de Jong, van Buuren and Spiess <doi:10.1080/03610918.2014.911894>.

Provides a test bench for the comparison of missing data imputation 
    methods in uni-variate time series. Imputation methods are compared using 
    different error metrics. Proposed imputation methods and alternative error 
    metrics can be used.

Imputation (replacement) of missing values 
             in univariate time series. 
             Offers several imputation functions
             and missing data plots. 
             Available imputation algorithms include: 
            'Mean', 'LOCF', 'Interpolation', 
            'Moving Average', 'Seasonal Decomposition', 
            'Kalman Smoothing on Structural Time Series models',
            'Kalman Smoothing on ARIMA models'. Published in Moritz and Bartz-Beielstein (2017) 
            <doi:10.32614/RJ-2017-009>.