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.
| Description | Monash authors | Average downloads per month | |
|---|---|---|---|
| forecast: Forecasting Functions for Time Series and Linear Models | The 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 | 422,229 |
| Ggally: extension to ggplot2 | The purpose of this function is to quickly plot the coefficients of a model. | Di Cook | 110,077 |
| naniar: Data Structures, Summaries, and Visualisations for Missing Data | naniar 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-Wild | 45,757 |
| distributional | Vectorised distribution objects with tools for manipulating, visualising, and using probability distributions | Mitchell O'Hara-Wild | 38,368 |
| tsfeatures | Methods 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-Wild | 32,426 |
| tsibble: Tidy Temporal Data Frames and Tools | Provides 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 O’Hara-Wild | 29,056 |
| hts: Hierarchical and Grouped Time Series | Provides 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 Hyndman | 24,588 |
| hdrcde: Highest Density Regions and Conditional Density Estimation | Computation 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 Cheng | 24,540 |
| rainbow: Bagplots, Boxplots and Rainbow Plots for Functional Data | Visualising functional data and identifying functional outliers. | Rob J Hyndman | 23,818 |
| fds | Functional data sets. | Rob J Hyndman | 23,617 |
| fpp2 | All 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 Hyndman | 21,795 |
| fma | All data sets from "Forecasting: methods and applications" by Makridakis, Wheelwright and Hyndman (Wiley, 3rd ed., 1998) | Rob J Hyndman | 19,919 |
| expsmooth | Data sets from the book "Forecasting with exponential smoothing: the state space approach" by Hyndman, Koehler, Ord and Snyder (Springer, 2008). | Rob J Hyndman | 19,552 |
| thief: Temporal Hierarchical Forecasting | Methods and tools for generating forecasts at different temporal frequencies using a hierarchical time series approach. | Rob J Hyndman | 18,188 |
| fabletools: Core Tools for Packages in the ’fable’ Framework | Provides 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 Athanasopoulos | 16,546 |
| rticles: Article Formats for R Markdown | A suite of custom R Markdown formats and templates for authoring journal articles and conference submissions. | Rob J Hyndman | 16,014 |
| feasts: Feature Extraction and Statistics for Time Series | Provides 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 Cook | 14,523 |
| fable: Forecasting Models for Tidy Time Series | Provides 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 | 14,372 |