Computing package for Multidimensional Poverty Index (MPI) using Alkire-Foster method. Given N individuals, each person has D indicators of deprivation, the package compute MPI value to represent the degree of poverty in a population. The inputs are 1) an N by D matrix, which has the element (i,j) represents whether an individual i is deprived in an indicator j (1 is deprived and 0 is not deprived), and 2) the deprivation threshold.  The main output is the MPI value, which has the range between zero and one. MPI value is approaching one if almost all people are deprived in all indicators, and it is approaching zero if almost no people are deprived in any indicator.  Please see Alkire S., Chatterjee, M., Conconi, A., Seth, S. and Ana Vaz (2014) <doi:10.35648/20.500.12413/11781/ii039> for The Alkire-Foster methodology.

A set of easy-to-use functions for computing the Multidimensional Poverty Index (MPI).

Estimate Multidimensional Poverty Indices disaggregated by population subgroups based on the Alkire and Foster method (2011) <doi:10.1016/j.jpubeco.2010.11.006>. This includes the calculation of standard errors and confidence intervals. Other partial indices such as incidence, intensity and indicator-specific measures as well as intertemporal changes analysis can also be estimated. The standard errors and confidence intervals are calculated considering the complex survey design.

Model stability and variable inclusion plots [Mueller and Welsh
    (2010, <doi:10.1111/j.1751-5823.2010.00108.x>); Murray, Heritier and Mueller
    (2013, <doi:10.1002/sim.5855>)] as well as the adaptive fence [Jiang et al.
    (2008, <doi:10.1214/07-AOS517>); Jiang et al. 
    (2009, <doi:10.1016/j.spl.2008.10.014>)] for linear and generalised linear models.

Leverages the R language to automate latent variable model estimation
	and interpretation using 'Mplus', a powerful latent variable modeling program
	developed by Muthen and Muthen (<https://www.statmodel.com>). Specifically, this package
    provides routines for creating related groups of models, running batches of
    models, and extracting and tabulating model parameters and fit statistics.