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StMoMo

StMoMo (Stochastic Mortality Modelling) is an R package providing functions to specify and fit stochastic mortality models including the Lee-Carter models, the CBD model, the APC model and many other (possibly new) models. The package also includes tools for analysing the goodness of fit of the models and performing mortality projections and simulations.

An overview of the usage and facilities of StMoMo is described in the working paper Andrés M. Villegas, Pietro Millossovich, Vladimir K. Kaishev. “StMoMo: An R Package for Stochastic Mortality Modelling”

Installation:

To install the stable version on R CRAN:

    install.packages("StMoMo")

To install the latest development version:

    install.packages("devtools")
    devtools::install_github("amvillegas/StMoMo")

License:

This package is free and open source software, licensed under GPL (>= 2)

If you are interested in the package feel free to email andresmauriciovillegas@gmail.com or track development at http://github.com/amvillegas/StMoMo

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Version

Install

install.packages('StMoMo')

Monthly Downloads

786

Version

0.4.1

License

GPL (>= 2)

Issues

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Repository

http://github.com/amvillegas/StMoMo

Maintainer

Andres Villegas

Last Published

April 13th, 2018

Functions in StMoMo (0.4.1)

extractCoefficientsFromGnm

Extract the model coefficient

fitted.fitStMoMo

Compute fitted values for a Stochastic Mortality Model

computeDevianceBinomial

Binomial deviance

Create an M8 type extension of the Cairns-Blake-Dowd mortality model

Fit a Multivariate Random Walk with Drift

Generate weight matrix

logLik.fitStMoMo

Log-Likelihood of a fitStMoMo object

getMinimalFitStMoMo

Extract a lighter version of a fitted Stochastic Mortality Model

forecast.fitStMoMo

Forecast mortality rates using a Stochastic Mortality Model

Generic for fitting a Stochastic Mortality Model

genPoissonResBootSamples

Generate Poisson residual bootstrap sample

forecast.iarima

Forecast independent arima series

genPoissonSemiparametricBootSamples

Generate Poisson semiparametric bootstrap samples

Logit function logit computes the logit function

Forecast a Multivariate Random Walk with Drift

Create an M6 type extension of the Cairns-Blake-Dowd mortality model

genBinomialResBootSamples

Generate Binomial residual bootstrap sample

Fit independent arima series to a multivariate time series

predict.fitStMoMo

Predict method for Stochastic Mortality Models fits

Create a Lee-Carter model

Inverse Logit function invlogit computes the inverse logit function

genBinomialSemiparametricBootSamples

Generate Binomial semiparametric bootstrap samples

Compute the link for a given mortality models

plot.bootStMoMo

Plot bootstrapped parameters of a Stochastic Mortality Model

Plot fitted parameters from a stochastic mortality model

simulate.bootStMoMo

Simulate future sample paths from a Bootstrapped Stochastic Mortality Model

initial2central

Transform StMoMoData from initial to central exposures

Create an M7 type extension of the Cairns-Blake-Dowd mortality model

simulate.fitStMoMo

Simulate future sample paths from a Stochastic Mortality Model

plotParameterFan

Plot fanchart of the parameters

poissonRes2death

Map poisson deviance residuals into deaths

Plot a forecast from a Stochastic Mortality Model

processStartValues

Process the initial parameter supplied by the user

residuals.fitStMoMo

Extract deviance residuals of a Stochastic Mortality Model

Plot the residuals of a Stochastic Mortality Model

Create a Renshaw and Haberman (Lee-Carter with cohorts) mortality model

simulate.iarima

Simulate independent arima series

Simulate a Multivariate Random Walk with Drift

Do a scatter plot of a matrix according to age-period-cohorts

central2initial

Transform StMoMoData from central to initial exposures

Generic method for bootstrapping a fitted Stochastic Mortality Model

Create StMoMoData object from demogdata object

bootstrap.fitStMoMo

Bootstrap a fitted Stochastic Mortality Model

Create a new Stochastic Mortality Model

Copy of unexported function arima.string from forecast

Create a Cairns-Blake-Dowd mortality model

Create an Age-Period-Cohort mortality model

Map Binomial deviance residuals into deaths

England and Wales male mortality data

Extract coefficients from a fitted Stochastic Mortality Model

Fit a Stochastic Mortality Model

computeLogLikPoisson

Compute Poisson loglikelihod

computeDeviancePoisson

Compute Poisson deviance

computeLogLikBinomial

Compute Binomial loglikelihod

Extract cohort from an age-period array

Fit a Renshaw and Haberman (Lee-Carter with cohorts) mortality model