MortalityLaws - Parametric Mortality Models, Life Tables and HMD

Fit the most popular human mortality 'laws', and construct full and abridge life tables given various input indices. A mortality law is a parametric function that describes the dying-out process of individuals in a population during a significant portion of their life spans. For a comprehensive review of the most important mortality laws see Tabeau (2001) <doi:10.1007/0-306-47562-6_1>. Practical functions for downloading data from various human mortality databases are provided as well.

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actuarial-sciencedemographydownload-hmdhuman-mortality-lawslife-tablemortality

7.70 score 36 stars 2 dependents 117 scripts 750 downloads

ungroup - Penalized Composite Link Model for Efficient Estimation of Smooth Distributions from Coarsely Binned Data

Versatile method for ungrouping histograms (binned count data) assuming that counts are Poisson distributed and that the underlying sequence on a fine grid to be estimated is smooth. The method is based on the composite link model and estimation is achieved by maximizing a penalized likelihood. Smooth detailed sequences of counts and rates are so estimated from the binned counts. Ungrouping binned data can be desirable for many reasons: Bins can be too coarse to allow for accurate analysis; comparisons can be hindered when different grouping approaches are used in different histograms; and the last interval is often wide and open-ended and, thus, covers a lot of information in the tail area. Age-at-death distributions grouped in age classes and abridged life tables are examples of binned data. Because of modest assumptions, the approach is suitable for many demographic and epidemiological applications. For a detailed description of the method and applications see Rizzi et al. (2015) <doi:10.1093/aje/kwv020>.

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distributionsglmsmoothingungroupingcpp

6.12 score 16 stars 83 scripts 385 downloads

MortalityGaps - The Double-Gap Life Expectancy Forecasting Model

Life expectancy is highly correlated over time among countries and between males and females. These associations can be used to improve forecasts. Here we have implemented a method for forecasting female life expectancy based on analysis of the gap between female life expectancy in a country compared with the record level of female life expectancy in the world. Second, to forecast male life expectancy, the gap between male life expectancy and female life expectancy in a country is analysed. We named this method the Double-Gap model. For a detailed description of the method see Pascariu et al. (2018). <doi:10.1016/j.insmatheco.2017.09.011>.

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demographyforecasting-life-expectancylongevitymortality-forecasting

4.32 score 3 stars 14 scripts 229 downloads