R packages and services

The majority of open-source code, software and packages contributed by ACEMS members is in the R language.

R is a leading open-source language and environment globally for both researchers and data scientists, enabling statistical computing, data analysis, graphics, applications specific to a range of domains (such as genetics, econometrics, clinical trials and environmental monitoring), and much more, as shown in the Comprehensive R Archive Network’s CRAN topic areas page. ACEMS R Software and Service highlights for 2020 include:

• at least 10 new R packages created and first published for free download on CRAN
• a total of 107 currently maintained on CRAN
• ACEMS members additionally making contributions of code, data, or packages, to other repositories such as GitHub. See Publicly Available Software.
• Updates being made to open source packages
• ACEMS members provided a range of services to enable end users and the public to learn about and use these R packages and other open source tools, including:
  • speaking at conferences;
  • delivering training workshops;
  • hosting and participating in hackathons; and
  • creating educational resources, including for schools and health professionals.

Each of the lectures were scheduled in the lunchtime timeslot locally, and although timezones were a factor, it was wonderful to attract the broader global mathematical and statistical community including audiences across Australia, as well as North and South America, Europe, Britain, Asia, India, Middle East and New Zealand. ACEMS was incredibly impressed with the interest in the lecture series with more than 1200 individual logons across the 9-lecture series, and with dynamic discussion via the Q & A feature.

New R Packages Created in 2020

ACEMS members added at least ten R packages to CRAN in 2020. See Table 1 below for details. Note that this list is not exhaustive with respect to either R or other open source packages created by ACEMS members. For example, R code and packages are often outputs of collaborative research projects, such as this free software program "Predicting seagrass decline due to cumulative stressors", and may be shared in other repositories such as GitHub.

Table 1: Details of Some New R Packages Created in 2020 by ACEMS members and collaborating authors
R package	Maintainer	Package Title	Description	Downloads Count; monthly average
distributional	Mitchell O'Hara-Wild	Vectorised Probability Distributions	The distributional package allows distributions to be used in a vectorised context.  Vectorised distribution objects with tools for manipulating, visualising, and using probability distributions. Designed to allow model prediction outputs to return distributions rather than their parameters, allowing users to directly interact with predictive distributions in a data-oriented workflow. In addition to providing generic replacements for p/d/q/r functions, other useful statistics can be computed including means, variances, intervals, and highest density regions.	14,6775; 12,231
fable.prophet	Mitchell O'Hara-Wild	Prophet Modelling Interface for 'fable'	Allows prophet models from the 'prophet' package to be used in a tidy workflow with the modelling interface of 'fabletools'. This extends 'prophet' to provide enhanced model specification and management, performance evaluation methods, and model combination tools.	8282; 690
seer	Thiyanga Talagala	Feature-Based Forecast Model Selection	A novel meta-learning framework for forecast model selection using time series features. Many applications require a large number of time series to be forecast. Providing better forecasts for these time series is important in decision and policy making. We propose a classification framework which selects forecast models based on features calculated from the time series. We call this framework FFORMS (Feature-based FORecast Model Selection). FFORMS builds a mapping that relates the features of time series to the best forecast model using a random forest. 'seer' package is the implementation of the FFORMS algorithm. For more details read the paper.	7616; 635
airt	Sevvandi Kandanaarachchi	Evaluation of Algorithm Collections Using Item Response Theory	An evaluation framework for algorithm portfolios using Item Response Theory (IRT). We use continuous and polytomous IRT models to evaluate algorithms and introduce algorithm characteristics such as stability, effectiveness and anomalousness (Kandanaarachchi, Smith-Miles 2020) <doi:10.13140/RG.2.2.11363.09760>.	5230; 436
gratis	Yanfei Kang	Generating Time Series with Diverse and Controllable Characteristics	Generates time series based on mixture autoregressive models. Kang,Y.,Hyndman,R.,Li,F.(2020)<doi:10.1002/sam.11461>.	4412; 368
nortsTest	Asael Alonzo Matamoros	Assessing Normality of Stationary Process	Despite that several tests for normality in stationary processes have been proposed in the literature, consistent implementations of these tests in programming languages are limited. Four normality test are implemented. The Lobato and Velasco's, Epps, Psaradakis and Vavra, and the random projections tests for stationary process. Some other diagnostics such as, unit root test for stationarity, seasonal tests for seasonality, and arch effect test for volatility; are also performed. The package also offers residual diagnostic for linear time series models developed in several packages.	3641; 303
tsibbletalk	Earo Wang	Interactive Graphics for Tsibble Objects	A shared tsibble data easily communicates between htmlwidgets on both client and server sides, powered by 'crosstalk'. A shiny module is provided to visually explore periodic/aperiodic temporal patterns.	2814; 234
composits	Sevvandi Kandanaarachchi	Compositional, Multivariate and Univariate Time Series Outlier Ensemble	An ensemble of time series outlier detection methods that can be used for compositional, multivariate and univariate data. It uses the four R packages 'forecast', 'tsoutliers', 'otsad' and 'anomalize' to detect time series outliers.	2559; 213
DSjobtracker	Thiyanga S. Talagala	What Skills and Qualifications are Required for Data Science Related Jobs?	Dataset containing information about job listings for data science job roles.	1514; 126
brolgar	Nick Tierney	BRowse Over Longitudinal Data Graphically and Analytically in R	Brolgar helps you browse over longitudinal data graphically and analytically in R, by providing tools to: efficiently explore raw longitudinal data; calculate features (summaries) for individuals; and evaluate diagnostics of statistical models This helps you go from a messy “plate of spaghetti” plot to “interesting observations”. The tools and workflows in brolgar are designed to work with a special tidy time series data frame called a tsibble. We can define our longitudinal data in terms of a time series to gain access to some really useful tools. To do so, we need to identify three components: 1. the key variable in your data is the identifier of your individual; 2. the index variable is the time component of your data; 3. he regularity of the time interval (index). Longitudinal data typically has irregular time periods between measurements, but can have regular measurements. Together, time index and key uniquely identify an observation.	1447; 121

Current ACEMS Packages Maintained

ACEMS members maintain a diversity of R Packages which remain popular amongst CRAN’s diversity of international users.  There are currently 107 R Packages created by ACEMS members and maintained on CRAN; Table 2 below provides the details and total user download numbers in descending order. Whilst total downloads are an indicator of value to end users, some packages with smaller downloads may have niche user groups and/or otherwise deliver significant impact from use.

The most popular R Package by download is rmarkdown – with more than 2.4 million total downloads by CRAN users. 

The significant number of downloads of R packages is indicative of the value and utility of these packages.

Total downloads of ACEMS members’ R packages on CRAN number:

• 19,870,453 total downloads of the CRAN packages during 2020
• 38,592,624 total downloads of the CRAN packages since 2019
• 53,735,049 total downloads of the CRAN packages since 2016

Table 2: Downloads for all ACEMS members’ R packages maintained and updated on CRAN
R Package	Maintainer	Current version	User downloads
rmarkdown	Yihui Xie	2.7	24709717
forecast	Rob J Hyndman	8.14	8757355
fracdi	Martin Maechler	1.5-1	5436168
GGally	Barret Schloerke	2.1.1	2753057
KernSmooth	Brian Ripley	2.23-18	1671269
DescTools	Andri Signorell	0.99.41	1497503
expsmooth	Rob J Hyndman	2.3	1374944
fma	Rob J Hyndman	2.4	912293
fpp	Rob J Hyndman	0.5	740974
imputeTS	Steen Moritz	3.2	682535
naniar	Nicholas Tierney	0.6.0	340371
fpp2	Rob J Hyndman	2.4	326577
visdat	Nicholas Tierney	0.5.3	323522
hts	Earo Wang	6.0.1	322147
tsibble	Earo Wang	1.0.1	309589
hdrcde	Rob J Hyndman	3.4	217990
rainbow	Han Lin	3.6	176498
tsfeatures	Rob J Hyndman	1.0.2	174831
thief	Rob J Hyndman	0.3	173083
fabletools	Mitchell O'Hara-Wild	0.3.1	170029
distributional	Mitchell O'Hara-Wild	0.2.2	146775
fable	Mitchell O'Hara-Wild	0.3.0	142023
xaringan	Yihui Xie	0.2	141264
ggmosaic	Haley Jeppson	0.3.3	131162
feasts	Mitchell O'Hara-Wild	0.2.1	128994
fds	Han Lin	1.8	125116
ftsa	Han Lin	6.0	119576
Mcomp	Rob J Hyndman	2.8	112929
demography	Rob J Hyndman	1.22	81649
TSclust	Pablo Montero	1.3.1	78194
bfast	Jan Verbesselt	1.5.7	52654
tsibbledata	Mitchell O'Hara-Wild	0.3.0	47950
LowRankQP	John T Ormerod	1.0.4	47304
feature	Tarn Duong	1.2.15	47202
fpp3	Rob J Hyndman	0.4.0	44904
season	Adrian Barnett	0.3.12	43720
tourr	Di Cook	0.6.0	41058
robets	Ruben Crevits	1.4	39863
SSN	Jay VerHoef	1.1.15	36810
geozoo	Barret Schloerke	0.5.1	36024
CEoptim	Benoit Liquet	1.2	35225
sugrrants	Earo Wang	0.2.8	34381
bayesImageS	Matt Moores	0.6-1	34332
nullabor	Di Cook	0.3.9	34125
geomnet	Sam Tyner	0.3.1	32010
vegawidget	Ian Lyttle	0.3.2	31099
shinycustomloader	Emi Tanaka	0.9.0	29336
vitae	Mitchell O’Hara-Wild	0.4.2	29200
stR	Alexander Dokumentov	0.4	28089
dma	Hana Sevcikova	1.4-0	27096
DescribeDisplay	Di Cook	0.2.7	26813
staplr	Priyanga Dilini Talagala	3.1.1	25893
curvHDR	Matt Wand	1.2-1	25813
dobson	Adrian Barnett	0.4	24995
MissingDataGUI	Xiaoyue Cheng	0.2-5	23737
emma	Laura Villanova	0.1-0	23638
ggenealogy	Lindsay Rutter	1.0.1	23452
edrGraphicalTools	Benoit Liquet	2.2	23120
rwalkr	Earo Wang	0.5.5	23111
binb	Dirk Eddelbuettel	0.0.6	21181
gammSlice	Matt Wand	2.0-2	20755
sgPLS	Benoit Liquet	1.7	20159
eechidna	Jeremy Forbes	1.4.1	20140
queuecomputer	Anthony Ebert	1.1.0	19487
BSL	Ziwen An	3.2.0	18893
ggquiver	Mitchell O’Hara-Wild	0.2.0	17896
MergeGUI	Xiaoyue Cheng	0.2-1	17602
MatTransMix	Xuwen Zhu	0.1.13	17202
MBSGS	Benoit Liquet	1.1.0	17188
smoothAPC	AlexanderD okumentov	0.3	16187
colmozzie	Thiyanga Talagala	1.1.1	16182
binostics	Ursula Laa	0.1.3	14425
diffpriv	Benjamin Rubinstein	0.4.2	14239
mozzie	Thiyanga Talagala	0.1.0	14177
gimmEP	Matt Wand	1.0-3.1	13673
PPforest	Natalia da Silva	0.1.1	13670
quokar	Wenjing Wang	0.1.0	13031
serrsBayes	Matt Moores	0.4-1	12518
ozmaps	Michael Sumner	0.4.0	11692
tourrGui	Di Cook	0.4	11611
HRW	Matt Wand	1.0-4	11085
starmie	Stuart Lee	0.1.2	10678
spinifex	Nicholas Spyrison	0.2.7	10349
taipan	Stephanie Kobakian	0.1.2	10136
gravitas	Sayani Gupta	0.1.3	9742
dobin	Sevvandi Kandanaarachchi	1.0.2	9503
fable.prophet	Mitchell O’Hara-Wild	0.1.0	8282
sugarbag	Stephanie Kobakian	0.1.3	7879
seer	Thiyanga Talagala	1.1.5	7616
stray	Priyanga Dilini Talagala	0.1.1	7506
eventstream	Sevvandi Kandanaarachchi	0.1.0	7425
oddstream	Priyanga Dilini Talagala	0.5.0	6562
syn	Nicholas Tierney	0.1.0	6244
airt	Sevvandi Kandanaarachchi	0.2.0	5230
gratis	Yanfei Kang	0.2.1	4412
spinebil	Ursula Laa	0.1.0	4199
nortsTest	Asael Alonzo Matamoros	1.0.0	3641
tsibbletalk	Earo Wang	0.1.0	2814
composits	Sevvandi Kandanaarachchi	0.1.0	2559
DSjobtracker	Thiyanga Talagala	0.1.1	1514
brolgar	Nicholas Tierney	0.1.0	1447
bayesforecast	Asael Alonzo Matamoros	0.0.1	1166
lookout	Sevvandi Kandanaarachchi	0.1.0	933
spotoroo	Weihao Li	0.1.1	391
nestr	Emi Tanaka	0.1.1	363
ferrn	H. Sherry Zhang	0.0.1	342
MedLEA	Thiyanga Talagala	1.0.1	35

R Services

ACEMS members contribute to the understanding, adoption, and use of open source tools, including R packages, through their work in running R events - such as hackathons (creating and applying R tools), speaking at conferences, delivering training workshops, and producing guides. 

Below is an overview of these types of R Services provided by ACEMS members in 2020.

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