Provides tools for fitting parametric mortality curves. Implements multiple optimisation strategies to enhance robustness and stability of parameter estimation. Offers tools for forecasting mortality rates guided by mortality curves. 
 For modelling details see: 
 Tabeau (2001) <doi:10.1007/0-306-47562-6_1>, 
 Renshaw and Haberman (2006) <doi:10.1016/j.insmatheco.2005.12.001>, 
 Cairns et al. (2009) <doi:10.1080/10920277.2009.10597538>,
 Li and Lee (2005) <doi: 10.1353/dem.2005.0021>.

Tool for demographic modeling and analysis, combining stochastic
    simulation-based projections with classic demographic methods. Provides
    utilities for disaggregating population data using Karup-King interpolation,
    fitting Brass relational logit models, calculating fertility and mortality
    metrics, building complete and abridged life tables with model-based
    separation factors, estimating child mortality indirectly from children
    ever born and surviving (Brass-Trussell method), decomposing and
    standardizing rates, and performing hierarchical subnational population
    projections. Relational life table estimation and interpolation
    methods are described in Brass (1975) "Methods for Estimating Fertility and
    Mortality from Limited and Defective Data", Preston et al. (2001,
    ISBN:978-0631226161) "Demography: Measuring and Modeling Population
    Processes", Siegel and Swanson (2004, ISBN:978-0126419559) "The Methods and
    Materials of Demography", and Raftery et al. (2012) "Bayesian probabilistic
    population projections for all countries" <doi:10.1073/pnas.1211452109>.

Construction and analysis of matrix population models in R.

To create demographic table with simple summary statistics,
        with optional comparison(s) over one or more groups.

Functions for demographic analysis including lifetable
        calculations; Lee-Carter modelling; functional data analysis of
        mortality rates, fertility rates, net migration numbers; and
        stochastic population forecasting.