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.

Provide a suite of functions for conducting and automating Latent Growth Modeling (LGM) in 'Mplus', including Growth Curve Model (GCM), Growth-Based Trajectory Model (GBTM) and Latent Class Growth Analysis (LCGA). 
  The package builds upon the capabilities of the 'MplusAutomation' package (Hallquist & Wiley, 2018) to streamline large-scale latent variable analyses. 
  “MplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus.” Structural Equation Modeling, 25(4), 621–638. <doi:10.1080/10705511.2017.1402334>
  The workflow implemented in this package follows the recommendations outlined in Van Der Nest et al. (2020). 
  “An Overview of Mixture Modeling for Latent Evolutions in Longitudinal Data: Modeling Approaches, Fit Statistics, and Software.” Advances in Life Course Research, 43, Article 100323. <doi:10.1016/j.alcr.2019.100323>.