Software I've written
This page provides links to R packages I have (co)authored. The most recent versions of most packages are on github. Most packages are also on CRAN.
Tidy time series analysis and forecasting packages
tsibbledata: Example datasets for tsibble
tsibblestats: Analysis of tidy time series
fable: Forecasting tidy time series
sugrrants: Supporting Graphs for Analysing Time Series. Tools for plotting temporal data using the tidyverse and grammar of graphics framework. [CRAN]
Other time series analysis and forecasting packages
bfast: Automatic identification of breaks for additive season and trend, designed for use with remote sensing data. [CRAN]
forecast: Methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling. [CRAN]
hts: Methods for visualizing, analysing and forecasting hierarchical time series. [CRAN]
robets: Forecasting time series with robust exponential smoothing. [CRAN]
stR: STR time series decomposition. The methods are described in Dokumentov & Hyndman (2016) STR: A Seasonal-Trend Decomposition Procedure Based on Regression. [CRAN]
thief: Temporal Hierarchical Forecasting. The methods are described in Forecasting with temporal hierarchies, co-authored with George Athanasopoulos, Nikolaos Kourentzes and Fotios Petropoulos. [CRAN]
tsfeatures: Time Series Feature Extraction.
tsgeneration: Efficient generation of time series with diverse and controllable characteristics
Time series data packages
expsmooth: Data sets from Hyndman, Koehler, Ord and Snyder (2008) Forecasting with exponential smoothing: the state space approach, Springer: Berlin. [CRAN]
fma: Data sets from Makridakis, Wheelwright and Hyndman (1998) Forecasting: methods and applications, Wiley & Sons: New York. [CRAN]
fpp: Data sets from Hyndman & Athanasopoulos (2014) Forecasting: principles and practice, OTexts: Melbourne. [CRAN]
fpp2: Data sets from Hyndman & Athanasopoulos (2017) Forecasting: principles and practice, 2nd edition, OTexts: Melbourne. [CRAN]
Mcomp: 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). [CRAN]
ozdata: Tools to import and explore Australian data sets. Data imported from data.gov.au and ausmacrodata.org.
tscompdata: Time Series Competition Data
Functional data and demography packages
addb: Nine data sets, taken from the Australian Demographic Data Bank version 3.2b, courtesy of Len Smith.
demography: Provides a large number of functions for handling 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. [CRAN]
fds: Functional data sets useful for testing new methods. [CRAN]
ftsa: Methods and tools for modelling and forecasting functional time series. [CRAN]
rainbow: Plots for functional data: rainbow plot, functional bagplot, functional HDR boxplot. [CRAN]
smoothAPC: Smoothing demographic data with period and cohort effects. The method uses bivariate thin plate splines, bivariate lasso-type regularization, and allows for both period and cohort effects. Thus the mortality rates are modelled as the sum of four components: a smooth bivariate function of age and time, smooth one-dimensional cohort effects, smooth one-dimensional period effects and random errors. The methods are described in Dokumentov & Hyndman (2014) Bivariate data with ridges: two-dimensional smoothing of mortality rates. [CRAN]
Other packages
cricketdata: International Cricket Data.
eechidna: Exploring Election and Census Highly Informative Data Nationally for Australia. Data from the 2013 and 2016 Australian Federal Elections and the 2011 and 2016 Australian Censuses for each House of Representatives electorate, along with some tools for visualizing and analysing the data. [CRAN]
emma: The evolutionary model-based multiresponse approach (EMMA) is a novel methodology to process optimisation and product improvement. The approach is suitable to contexts in which the experimental cost and/or time limit the number of implementable trials. [CRAN]
hdrcde: Tools for the computation of highest density regions in one and two dimensions, kernel estimation of univariate density functions conditional on one covariate, and multimodal regression. [CRAN]
MEFM: A set of tools for implementing the Monash Electricity Forecasting Model based on the paper by Hyndman and Fan (2010).