Contains a suite of functions for health economic evaluations with missing outcome data. 
  The package can fit different types of statistical models under a fully Bayesian approach using the software 'JAGS' (which should be installed locally and which is loaded in 'missingHE' via the 'R' package 'R2jags'). 
  Three classes of models can be fitted under a variety of missing data assumptions: selection models, pattern mixture models and hurdle models.
  In addition to model fitting, 'missingHE' provides a set of specialised functions to assess model convergence and fit, and to summarise the statistical and economic results using different types of measures and graphs. 
  The methods implemented are described in Mason (2018) <doi:10.1002/hec.3793>, Molenberghs (2000) <doi:10.1007/978-1-4419-0300-6_18> and Gabrio (2019) <doi:10.1002/sim.8045>.

A system for Analysis of LSD when there is one missing observation. Methods for this process is described in A.M.Gun,M.K.Gupta,B.Dasgupta(2019,ISBN:81-87567-81-3).

A system for Analysis of RBD when there is one missing observation. Methods for this process is described in A.M.Gun,M.K.Gupta,B.Dasgupta(2019,ISBN:81-87567-81-3).

Imputation of incomplete continuous or categorical datasets; Missing values are imputed with a principal component analysis (PCA), a multiple correspondence analysis (MCA) model or a multiple factor analysis (MFA) model; Perform multiple imputation with and in PCA or MCA.

To test whether the missing data mechanism, in a set of incompletely observed data, is one of missing completely at random (MCAR). 
    For detailed description see Jamshidian, M. Jalal, S., and Jansen, C. (2014). "MissMech: An R Package for Testing Homoscedasticity, Multivariate Normality, and Missing Completely at Random (MCAR)", Journal of Statistical Software, 56(6), 1-31. <https://www.jstatsoft.org/v56/i06/> <doi:10.18637/jss.v056.i06>.

Supply functions for the creation and handling of missing
    data as well as tools to evaluate missing data methods. Nearly all
    possibilities of generating missing data discussed by Santos et al.
    (2019) <doi:10.1109/ACCESS.2019.2891360> and some additional are
    implemented.  Functions are supplied to compare parameter estimates
    and imputed values to true values to evaluate missing data methods.
    Evaluations of these types are done, for example, by Cetin-Berber et
    al. (2019) <doi:10.1177/0013164418805532> and Kim et al. (2005)
    <doi:10.1093/bioinformatics/bth499>.