Software development

Many academic staff and postgraduate students within Econometrics and Business Statistics are active developers of open-source R packages. Our packages have been downloaded by users all over the world millions of times. Packages with more than 1500 downloads per month are listed below.

 DescriptionMonash authorsAverage downloads per month
forecast: Forecasting Functions for Time Series and Linear ModelsThe R package forecast provides methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.Rob J Hyndman, George
Athanasopoulos, Mitchell O’Hara-Wild
Ggally: extension to ggplot2The purpose of this function is to quickly plot the coefficients of a model.Di Cook110,077
naniar: Data Structures, Summaries, and Visualisations for Missing Datananiar provides principled, tidy ways to summarise, visualise, and manipulate missing data with minimal deviations from the workflows in ggplot2 and tidy data.Di Cook, Mitchell O’Hara-Wild45,757
distributionalVectorised distribution objects with tools for manipulating, visualising, and using probability distributionsMitchell O'Hara-Wild38,368
tsfeaturesMethods for extracting various features from time series data. The features provided are those from Hyndman, Wang and Laptev (2013), Kang, Hyndman and Smith-Miles (2017) and from Fulcher, Little and Jones (2013). Rob J Hyndman, Yangzhuoran Yang, Mitchell O’Hara-Wild32,426
tsibble: Tidy Temporal Data Frames and ToolsProvides a ’tbl_ts’ class (the ’tsibble’) for
temporal data in an data- and model-oriented format. The ’tsibble’ provides tools to easily manipulate and analyse temporal data, such as filling in time gaps and aggregating over calendar periods.
Di Cook, Rob J
Hyndman, Mitchell
hts: Hierarchical and Grouped Time SeriesProvides methods for analysing and forecasting hierarchical and grouped time series. The available forecast methods include bottom-up, top-down, optimal combination reconciliation (Hyndman et al., 2011) and trace minimisation reconciliation (Wickramasuriya et al., 2018).Rob J Hyndman24,588
hdrcde: Highest Density Regions and Conditional Density EstimationComputation of highest density regions in one and two dimensions, kernel estimation of univariate density functions conditional on one covariate and multimodal regression.Rob J Hyndman, Fan
rainbow: Bagplots,
Boxplots and Rainbow Plots for Functional Data
Visualising functional data and identifying functional outliers.Rob J Hyndman23,818
fdsFunctional data sets.Rob J Hyndman23,617
fpp2All data sets required for the examples and exercises in the book "Forecasting: principles and practice" (2nd ed., 2018) by Rob J Hyndman and George Athanasopoulos.Rob J Hyndman21,795
fmaAll data sets from "Forecasting: methods and applications" by Makridakis, Wheelwright and Hyndman (Wiley, 3rd ed., 1998)Rob J Hyndman19,919
expsmoothData sets from the book "Forecasting with exponential smoothing: the state space approach" by Hyndman, Koehler, Ord and Snyder (Springer, 2008).Rob J Hyndman19,552
thief: Temporal Hierarchical ForecastingMethods and tools for generating forecasts at different temporal frequencies using a hierarchical time series approach.Rob J Hyndman18,188
fabletools: Core Tools for Packages in the ’fable’ FrameworkProvides tools, helpers and data structures for developing models and time series functions for ’fable’ and extension packages.Mitchell O’Hara-Wild,
Rob J Hyndman, Di
Cook, George
rticles: Article Formats for R MarkdownA suite of custom R Markdown formats and templates for authoring journal articles and conference submissions.Rob J Hyndman16,014
feasts: Feature Extraction and Statistics for Time SeriesProvides a collection of features, decomposition methods, statistical
summaries and graphics functions for the analysing tidy time series data.
Mitchell O’Hara-Wild,
Rob J Hyndman, Di
fable: Forecasting Models for Tidy Time SeriesProvides a collection of commonly used univariate and multivariate time series forecasting models including automatically selected exponential smoothing (ETS) and autoregressive integrated moving average (ARIMA) models.Mitchell O’Hara-Wild,
Rob J Hyndman