Rob J Hyndman

The demography package for R contains functions for various demographic analyses. It provides facilities for demographic statistics, modelling and forecasting. In particular, it implements lifetable calculations; Lee-​​​​Carter modelling and variants; functional data analysis of mortality rates, fertility rates, net migration numbers; and stochastic population forecasting.
Examples

funcfor.R: analysis from Hyndman and Ullah (2005)
adelaide.R: analysis from a talk given to the Australian Population Network in Adelaide, November 2004.
Other shorter examples are given in the help files.

Extra data are available in the addb package.

The hts package provides methods for analysing and forecasting hierarchical time series.

The ftsa package provides tools for modelling and forecasting functional time series.

The fds package provides functional data sets useful for testing new methods.

The expsmooth package for R provides data sets from the book “Forecasting with exponential smoothing: the state space approach” by Hyndman, Koehler, Ord and Snyder (Springer, 2008).

The Mcomp package for R provides the 1001 time series from the M-​​​​competition (Makridakis et al. 1982) and the 3003 time series from the IJF-​​​​M3 competition (Makridakis and Hibon, 2000).

The fma package for R provides all data sets from “Forecasting: methods and applications” by Makridakis, Wheelwright & Hyndman (Wiley, 3rd ed., 1998).

The forecast package for R provides methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling. It also includes a handful of data sets from the Time Series Data Library. The package is described in Hyndman and Khandakar (2008).

The rainbow package provides tools for plotting functional data including the rainbow plot, functional bagplot, functional HDR boxplot. The methods are described in Rainbow plots, bagplots and boxplots for functional data

The hdrcde package provides tools for computation of highest density regions in one and two dimensions, kernel estimation of univariate density functions conditional on one covariate, and multimodal regression.