Contains functions for multiple imputation which
    complements existing functionality in R.
    In particular, several imputation methods for the
    mice package (van Buuren & Groothuis-Oudshoorn, 2011,
    <doi:10.18637/jss.v045.i03>) are implemented.
    Main features of the miceadds package include
    plausible value imputation (Mislevy, 1991,
    <doi:10.1007/BF02294457>), multilevel imputation for
    variables at any level or with any number of hierarchical
    and non-hierarchical levels (Grund, Luedtke & Robitzsch,
    2018, <doi:10.1177/1094428117703686>; van Buuren, 2018, 
    Ch.7, <doi:10.1201/9780429492259>), imputation using 
    partial least squares (PLS) for high dimensional 
    predictors (Robitzsch, Pham & Yanagida, 2016), 
    nested multiple imputation (Rubin, 2003, 
    <doi:10.1111/1467-9574.00217>), substantive model
    compatible imputation (Bartlett et al., 2015,
    <doi:10.1177/0962280214521348>), and features
    for the generation of synthetic datasets
    (Reiter, 2005, <doi:10.1111/j.1467-985X.2004.00343.x>;
    Nowok, Raab, & Dibben, 2016, <doi:10.18637/jss.v074.i11>).

Statistical Analyses and Pooling after Multiple Imputation. A large variety 
   of repeated statistical analysis can be performed and finally pooled. Statistical analysis 
   that are available are, among others, Levene's test, Odds and Risk Ratios, One sample 
   proportions, difference between proportions and linear and logistic regression models. 
   Functions can also be used in combination with the Pipe operator. 
   More and more statistical analyses and pooling functions will be added over time.
   Heymans (2007) <doi:10.1186/1471-2288-7-33>.
   Eekhout (2017) <doi:10.1186/s12874-017-0404-7>.
	 Wiel (2009) <doi:10.1093/biostatistics/kxp011>.
	 Marshall (2009) <doi:10.1186/1471-2288-9-57>.
	 Sidi (2021) <doi:10.1080/00031305.2021.1898468>.
	 Lott (2018) <doi:10.1080/00031305.2018.1473796>.
	 Grund (2021) <doi:10.31234/osf.io/d459g>.

Various tools for microeconomic analysis and microeconomic modelling,
   e.g. estimating quadratic, Cobb-Douglas and Translog functions,
   calculating partial derivatives and elasticities of these functions,
   and calculating Hessian matrices, checking curvature
   and preparing restrictions for imposing monotonicity of Translog functions.