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

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The hts package provides methods for analysing and forecasting hierarchical time series. [iframe http://cran.r-project.org/web/packages/hts/index.html 600 900]

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

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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. [iframe http://cran.ms.unimelb.edu.au/web/packages/hdrcde/ 600 900]

The ftsa package provides tools for modelling and forecasting functional time series. [iframe http://cran.ms.unimelb.edu.au/web/packages/ftsa/index.html 600 900]

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 [iframe http://cran.ms.unimelb.edu.au/web/packages/rainbow/index.html 600 900]

The fds package provides functional data sets useful for testing new methods. [iframe http://cran.ms.unimelb.edu.au/web/packages/fds/index.html 600 900]

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). [iframe http://cran.ms.unimelb.edu.au/web/packages/Mcomp/index.html 600 900]

Rob J Hyndman, George Athanasopoulos and Han Lin Shang The new version of the hts package (v3.01) has a vignette.

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Automatic identification of breaks for additive season and trend, designed for use with remote sensing data. Based on Verbesselt, Hyndman, Newnham and Culvenor (2010) Detecting trend and seasonal changes in satellite image time series, Remote Sensing of Environment 114(1), 106-115. Verbesselt, Hyndman, Zeileis and Culvenor (2010) Phenological change detection while

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The fpp package for R provides all data sets required for the examples and exercises in the book Forecasting: principles and practice by Rob J Hyndman and George Athanasopoulos. All packages required to run the examples are also loaded. [iframe http://cran.r-project.org/web/packages/fpp/ 600 900]

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). [iframe http://cran.ms.unimelb.edu.au/web/packages/expsmooth/index.html 600 900]

The fma package for R provides all data sets from "Forecasting: methods and applications" by Makridakis, Wheelwright & Hyndman (Wiley, 3rd ed., 1998). [iframe http://cran.ms.unimelb.edu.au/web/packages/fma/index.html 600 900]

Download code Description Perform cubic spline monotonic interpolation of given data points, returning either a list of points obtained by the interpolation or a function performing the interpolation. The splines are constrained to be monotonically increasing (i.e., the slope is never negative). Usage cm.splinefun(x, y = NULL, method = "fmm",

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The addb package contains 9 data sets, taken from the Australian Demographic Data Bank version 3.2b, courtesy of Len Smith. The package demography must also be installed. Current version of addb: 3.223 (29 October 2010) Source Smith, L. (2009) The Australian Demographic Data Bank, 1901-2003: Populations [Computer File]. Canberra: Australian

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Download R code (Code corrections made: 19 March 2007) DESCRIPTION Functions for kernel estimation of ROC curves. Bandwidth selection based on method of Hall and Hyndman (2003). Confidence intervals constructed using the method of Hall, Hyndman and Fan (2003). USAGE ROC.est(x, y, hx, hy, conf=95, intervals=TRUE, ...) REQUIRED ARGUMENTS x

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