and 
extRemes (version >= 2.0) and in2extRemes1 are R packages for statistically analyzing extreme values of a data set. in2extRemes is a point-and-click software tool that operates many of the command-line functions from the extRemes (version >= 2.0) package.2
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You may want to join the User Group for extRemes. The user group is intended for users to discuss issues about EVA and/or extRemes/in2extRemes with each other. Issues regarding bugs, etc., should still be sent to the maintainer (currently, Eric Gilleland).
CRAN landing pages for extRemes and in2extRemes.
1These software packages were funded by the National Science Foundation (NSF) through the NCAR Weather and Climate Impact Assessment Science Initiative with additional support from the NCAR Geophysical Statistics Project (GSP).
2 Originally, extRemes (versions < 2.0) was (primarily) point-and-click software running functions from the R package, ismev. The new package, in2extRemes, now takes on the point-and-click role, and extRemes has only command-line functions. The package ismev is still available on CRAN.
Extreme value analysis (EVA) is used primarily to quantify the stochastic behavior of a process at unusually large (or small) values. Particularly, such analyses usually req uire estimation of the probability of events that are more extreme than any previously observed. Many fields use EVA including: meteorology, hydrology, finance and ocean wave m odeling to name just a few.
Many statistical analyses concern sums or averages of random variables, and often rely upon limiting results such as the Central Limit Theorem to justify use of the normal ( or bell-shaped) distribution. When interest is in extremes, the bulk of the data may be misleading, and the normal distribution is not appropriate. A similar theorem to the Cen tral Limit Theorem, the Extremal Types Theorem, provides justification for using a family of distributions (in the univariate setting, similar results hold for multivariate ana lysis) known as the generalized extreme value (GEV) distribution. This, or analagous results for threshold excesses, are often the focus of EVA.
For more about EVA, see Rick Katz's page on extremes
Back to TopTo install R, please visit The R Project for Statistical Computing for instructions. If you are intending to use the point-and-click software package in2extRemes, it relies on the Tcl/Tk tools, so be sure to install R with those tools (generally not the default!). They are not needed for extRemes version >= 2.0. Note also that for Mac users, the X Quartz package must be installed.
Once R has been installed, open an R session. To install in2extRemes, type:
install.packages("in2extRemes")
The above command will install extRemes automatically, as well as other required packages. To install only extRemes, type:
install.packages("extRemes")
Installation need be done only once, but you can make sure you have the most recent version of your packages by typing update.packages() at any time (after installing them) from your R prompt.
Once installed, the packages must be loaded into your R session (every time) in order to use them. To load in2extRemes, type:
library(in2extRemes)
The above command will automatically load extRemes and all other required packages. If you are only using extRemes, then load using library(extRemes).
To begin using the in2extRemes window interface, type:
in2extRemes()
Select File > Block Maxima
Select FortCollinsPrecip from Data Object
Select Prec from Find block maxima of ...
Select year from Blocks
Type bmFtPrec in Save As (in R) field, and click OK.
Fit the GEV to the annual maximum precipitation (inches) in Fort Collins, Colorado, U.S.A.
Select Analyze > Extreme Value Distributions
Select bmFtPrec from Data Object
Select Prec from Response
Select Generalized Extreme Value (GEV) from Model Type
Check Plot diagnostics checkbox
Type inches in Response Units field, and click OK
Back to Top9th International Extreme Value Analysis Conference Satellite Workshop on Statistical Computing for Extremes, 14 June 2015, Ann Arbor, Michigan. (Univariate part: Presentation slides, pdf, Practice Problems, pdf)
Gilleland E. Short course: An introduction to the analysis of extreme values using R and extRemes. Graybill VIII/6th International Conference on Extreme Value Analysis. Colorado State University, Fort Collins, Colorado. 22-26 June 2009.
Gilleland E. Intense course for young researchers on R Statistical software for climate research with an introduction to extreme value analysis, Interdisciplinary Workshop: Effects of climate change: coastal systems, policy implications, and the role of statistics Workshop. 16-20 March 2009, Preluna Hotel and Spa, Sliema, Malta.
Katz RW, 2009: "Problem application: Exercise session on analyses of extremes." American Meteorological Society Short Course on Statistics of Extreme Events, Phoenix, AZ.
Katz RW, 2008: "Background on extreme value theory with emphasis on climate applications." Short Course on Statistics of Extremes in Climate Change, Michigan State University.
Katz RW, 2008: "Application of extreme value theory to climate change." Short Course on Statistics of Extremes in Climate Change, Michigan State University.
Back to TopYou can see how to cite the R programming language in a paper or presentation using the following command in your R session:
citation()
Similarly, to cite extRemes, use:
citation("extRemes")
And, to cite in2extRemes, use:
citation("in2extRemes")
Gilleland, E. and R. W. Katz, 2006: Analyzing seasonal to interannual extreme weather and climate variability with the extremes toolkit (extRemes), 18th Conference on Climate Variability and Change, 86th American Meteorological Society (AMS) Annual Meeting, 29 January - 2 February, 2006, Atlanta, Georgia. P2.15 (pdf).
Gilleland, E. and R.W. Katz, 2011: New Software to analyze how extremes change over time, Eos, 92 (2), 11 January 2011, 13 - 14.
Gilleland, E., M. Ribatet and A. G. Stephenson, 2013: A software review for extreme value analysis. Extremes, 16 (1), 103 - 119, DOI: 10.1007/s10687-012-0155-0 (available online at http://www.springerlink.com/openurl.asp?genre=article&id=doi:10.1007/s10687-012-0155-0).
Stephenson, A. G. and E. Gilleland, 2006: Software for the Analysis of Extreme Events: The Current State and Future Directions, Extremes 8, 87 - 109.
Abeysirigunawardena, D. S., E. Gilleland, D. Bronaugh, 2009: Extreme wind regime responses to climate variability and change in the inner-south-coast of British Columbia Canada. Atmosphere-Ocean, 47, (1), 41 - 61.
Bodini, A. and Q. A. Cossu, 2008: Analysis of precipitation trends during 2nd half of the 20th century in an area of Sardinia (Italy) at high hydrogeological risk. CNR-IMATI Technical Report 08.11. Available at http://www.mi.imati.cnr.it/iami/papers/08-11.pdf
de Oliveira, M. M. F., N. F. F. Ebecken, J. L. F. de Oliveira and E. Gilleland, 2010: Generalized extreme wind speed distributions in South America over the Atlantic O cean region. Theor. Appl. Climatol., 104, (3 - 4), 377 - 385, doi:10.1007/s00704-010-0350-3.
Guttorp, P. and Xu J., 2010: Climate change, trends in extremes, and model assessment for a long temperature time series from Sweden. Environmetrics, 22, (3), 456 - 463, doi:10.1002/env.1099.
Heaton, M. J., M. Katzfuss, S. Ramachandar, K. Pedings, E. Gilleland, E. Mannshardt-Shamseldin, and R. L. Smith, 2010: Spatio-temporal models for large-scale indicators of extreme weather. Environmetrics, 22, 294 - 303, doi:10.1002/env.1050.
Helminen, J., A. Venäläinen and A. Vajda, 2005. The occurence of extreme precipitation values in Finland during summer (May-September). 4th Conference on Extreme Value Analysis: Probabilistic and Statistical Models and their Applications, 15 - 19 August, 2005, Gothenburg, Sweden (pdf).
Katz, R. W., G. S. Brush, and M. B. Parlange, 2004: Statistics of extremes: Modeling ecological disturbances. Ecology, 86, 1124 - 1134, doi: 10.1890/04-0606.
Lu, J., 2007: Local effects of global warming. Masters Thesis, Dept. of Mathematics and Statistics, Texas Tech University, Clyde F. Martin, Committee Chair. Selected as Women's Studies Best Graduate Student Paper.
Mares, C., I. Mares, and A. Stanciu, 2009: Extreme value analysis in the Danube lower basin discharge time series in the twentieth century. Theor. Appl. Climatol., 95, 223 - 233, doi:10.1007/s00704-008-0001-0.
Pirazzoli, P. A. and A. Tomasin, 2007: Estimation of return periods for extreme sea levels: a simplified empirical correction of the joint probabilities method with examples from the French Atlantic coast and three ports in the southwest of the UK. Ocean Dynamics, 57, 91 - 107.
Pirazzoli, P. A., A. Tomasin, and A. Ullmann, 2006: Extreme sea levels in two northern Mediterranean areas. J. Mediterranean Geography, 108, 59 - 68. Available at: http://mediterranee.revues.org/170
Sanabria, L. A. and R. P. Cechet, 2007: A statistical model of severe winds. Geoscience Australia Record 2007/12. Available at: http://www.ga.gov.au/image_cache/GA10911.pdf
Schleip, C., D. P. Ankerst, A. B&oouml;ck, N. Estrella, and A. Menzel, 2012: Comprehensive methodological analysis of long-term changes in phenological extremes in Germany. Global Change Biology, 18, 2349 - 2364, doi:10.1111/j.1365-2486.2012.02701.x.
Unkašević, M. and I. Tošić, 2009: Changes in extreme daily winter and summer temperatures in Belgrade. Theor. Appl. Climatol., 95, 27 - 38, doi:10.1007/s00704-007-0364-7.
Venäläinen, A., K. Jylhä, T. Kilpeläinen, S. Saku, H. Tuomenvirta, A. Vajda, and K. Ruosteenoja, 2009: Recurrence of heavy precipitation, dry spells and deep snow cover in Finland based on observations. Boreal Environment Research, 14, 166 - 172.
Walter, M. D., 2008: Application of the Statistical Theory of Extreme Values to Heat Waves. Significant Opportunities in Atmospheric Research and Science (SOARS) Program.
Wise, E. K., 2009: Climate-based sensitivity of air quality to climate change scenarios for the southwestern United States. Int. J. Climatol., 29, 87 - 97, doi:10.1002/joc.1713.
Wolters, D., 2007: A method to investigate the time evolution of the probability distribution of high temperature extremes in The Netherlands, applied to the extremes in the period of May 2006-April 2007. MSc study project, Wageningen University, supervisors: dr. Leo Kroon and drs. Paul Torfs.
de Alba, E., J. Zúñiga, and M. A. Ramírez Corzo, 2008. Measurement and transfer of catastrophic risks: A simulation analysis. Institute of Insurance and Pension Research (IIPR) Report 2008-03.
Denny, M. W., 2008. Limits to running speed in dogs, horses and humans. J. Experim. Biol., 211, 3836 - 3849, doi:10.1242/jeb.024968.
Fujita, M., 2007. Separation and Airspace Safety Panel (SASP) meeting of the working group of the whole. Twelfth meeting of the International Civil Aviation Organization, Santiago, Chile, 5-16 November 2007 (pdf)
OBrien, E. J., A. Bordallo-Ruiz and B. Enright, 2014. Lifetime maximum load effects on short-span bridges subject to growing traffic volumes. Structural Safety, 50, 113 - 122, DOI: 10.1016/j.strusafe.2014.05.005
Saito, S., S. Aburatani, and K. Horimoto, 2008. Network evaluation from the consistency of the graph structure with the measured data. BMC Systems Biology, 2:84, 14 pp., doi:10.1186/1752-0509-2-84 (pdf)
Back to TopVersion 2.1-3: Fixed a bug with the default prior distribution for the Martins and Stedinger GMLE whereby the shape parameter range was being secluded to the range [0,1] instead of [0.5, 0.5] as intended (thanks to Antoine Chapon for finding and fixing this bug for me!). Fixed a bug in the findpars function for Bayesian estimation whereby if no location parameter was found, the nloc object was not being defined, which caused the function to error out. It should no longer error out for this issue. Also fixed technical issues with testing for classes.
Version 2.1-2: I had to remove abba because of changes in R that caused problems with the underlying C code. Perhaps we'll figure out how to fix it one day and put it back in.
Version 2.1: New references added to the xbooter and xtibber help pages: Namely,
Gilleland, E., 2020. Bootstrap methods for statistical inference. Part I: Comparative forecast verification for continuous variables. Journal of Atmospheric and Oceanic Technology, 37 (11), 2117 - 2134, doi: 10.1175/JTECH-D-20-0069.1.
Gilleland, E., 2020. Bootstrap methods for statistical inference. Part II: Extreme-value analysis. Journal of Atmospheric and Oceanic Technology, 37 (11), 2135 - 2144, doi: 10.1175/JTECH-D-20-0070.1.
Version 2.0-12: Minor bug fix caused by a change in R to the new xtibber function.
Version 2.0-11: New (parametric) boostrap inference functionality has been added via xbooter and xtibber. Both of which are wrapper functions to booter from package distillery. The original parametric bootstrap remains (and will remain) available via the ci function. A minimal amount of bivariate EVD fitting has been added in the form of fitting bivariate POT models to threshold excesses. The logistic and mixed beta dependence functions are available with bootstrap resampling for inference. See the help files for fbvpot and bvpotbooter.
Version 2.0-10: The dependency on the car package has been removed. The only noticeable change is that the qq-plot for the inter-exceedance times of PP model fit against the theoretical quantiles of the mean-one exponential distribution (the so-called Z-plot, as well as the W-plot) has confidence bands that are calculated differently (and will likely be wider). The bands are now calculated the same way as the qq2 option from the
Version 2.0-9: A user-found bug in the summary function to fevd under the Bayesian setting that caused the function to error out has been fixed.
Version 2.0-8: A bug in the MRL plot code was found by a user in the calculation of the confidence intervals, and this problem has been fixed. Another user-found bug involved the GMLE profile likelihood that did not utilize the penalty term for the GMLE, thereby giving erroneous values. This problem has also been fixed.
Some user suggestions have been incorporated, including: (i) fevd plot function now invisibly returns the values plotted provided the type argument is not "primary" (i.e., the default); (ii) the fevd plot function (and threshrange.plot) will now return the par arguments back to what they were before the function was called; (iii) a bug in the qq2 type of plot was found and fixed; and (iv) ellipses are now passed to the ci function in the threshrange.plot function so that other ci methods can be used.
The version also contains the new citation information for the package.
Version 2.0-7: A new heat wave magnitude index function (hwmid) has been added; as supplied by Simone Russo. It calculates the index described in the recently published paper: Russo, S., J. Sillmann, E. Fischer, 2015. Top ten European heatwaves since 1950 and their occurrence in the coming decades. Environmental Research Letters, 10, 124003, doi:10.1088/1748- 9326/10/12/124003.
Version 2.0-6: The print method for the ci function was printing incorrect information for the normal approximation of stationary models for the parameters when only one parameter's CI's are estimated. Additionally, the initial values to the profile likelihood function (profliker) were not generally optimal making it difficult to get good profile likelihoods (and subsequently CIs). These bugs have been fixed.
Version 2.0-5: A couple of users found two different bugs in fevd when using the GMLE method option (primarily for models with parameters that vary). Essentially, the old code should work fine for models whose parameters do not vary, though it was strictly incorrect. This version fixes that problem, and extends the Martins and Stedinger GMLE framework to that in, e.g., El Adlouni et al. (2007, WRR, 43: W03410, doi: 10.1029/2005WR004545). Also, a typo in the fevd help file concerning the time.units argument has been fixed. It used to say that it would take plural names, such as "minutes", when in fact, it wants them to be singular, e.g. "minute".
Version 2.0-4: The function pevd erroneously converted the scale parameter to that of the GP, then switched the type argument to "GEV" instead of "GP." Now, it no longer converts the scale parameter, but still switches the type argument to "GEV" in order to assume that that is what was intended by the user.
A function, written by Simone Russo, called hwmi is introduced, which calculates the "Heat Wave Magnitude Index."
A function called abba, written by Alec Stephenson, is introduced. It is an experimental function that implements the MCMC methodology for fitting spatial extreme value models as proposed in Stephenson et al. (2015, J. Appl. Meteorol. and Climatol., in press).
Version 2.0-3: Minor bug introduced from previous version fixed.
Version 2.0-2: A user-reported bug in qevd was found whereby the lower.tail argument was backwards for the POT models (it was correct for the BM models). This bug has been fixed. Additionally, another user-reported mis-communication from the fevd help file concerning the "GMLE" optimization method has been corrected.
Back to TopBelow are frequently asked questions from extRemes (versions < 2.0) that are still applicable, and anticipated frequently to-be-asked questions for extRemes version >= 2.0. Additional FAQ's for extRemes will be added as they come in.
Q: What is the difference between extRemes and in2extRemes?
A: The former has command-line only code that includes considerably more functionality, while the latter is easy-to-use point-and-click software that operates some of the functions of extRemes. One need not know much about R to operate in2extRemes.
Q: What about ismev? Can I still use it? How does it differ from extRemes?
A: The package ismev is still available. It has command-line only code for doing EVA, and is associated with the text by Coles. The primary difference between those functions and the ones in extRemes is that ismev functions use matrices for adding covariates into parameter estimates whereas extRemes employs formulas (similar to gpd from package texmex). The latter also contains additional estimation procedures (e.g., Bayesian and penalized likelihood, or GMLE) as well as more diagnostic plots; especially for non-stationary point process models. Additionally, extRemes has functions for diagnosing tail dependence. Finally, extRemes makes more use of method functions, such as ci from package distillery for obtaining confidence intervals for parameter and return level estimates including effective return levels, plot, summary, print and more. The new version of extRemes allows for the log link function in the scale parameter, but otherwise, only the identity link can be used. Therefore, if it is important to include more general link functions in your model, ismev will be the package you will need to use.
Q: I installed extRemes version >= 2.0, but do not get any GUI window to appear. What happened?
A: extRemes no longer contains any GUI window functions. The GUI windows are now fully contained in the new in2extRemes package.
Q: I installed the newest version of R and in2extRemes, but I get an error message when I try to load in2extRemes into R.
A: Make sure you have the Tcl/Tk tools installed. They are not included with R releases, and must be installed separately from R. The process for this depends on your OS and also changes from time to time, so please check the R project web site for current and relevant instructions on how to add the Tcl/Tk tools.
Q: I get an error message that says:
Error in solve.default(x$hessian) :
Lapack routine dgesv: system is exactly singular
What does this mean? What can I do about it?
A: The model you're trying to fit is perhaps ill-formed. At least, the estimated Hessian is not invertible, and therefore the various optimization routines cannot work. See the help file for optim for more information. You may simply need to try a different model form. When fitting an EVD to numerous data sets using commands instead of the GUI windows (e.g., in a loop), I have found it necessary to wrap a try function around the fitting command. It is also possible to test whether or not a fit succeeded using, e.g., if( class(fit) != "try-error")...
Q: Why does the main dialog window not appear when I type library(in2extRemes)? How can I get it to appear?
A: The GUI dialog is not opened upon calling library(in2extRemes). The window can be opened by typing in2extremes() at the R command prompt.
Q: Why does the return level plot have return periods less than one year?
A: Although the return level plot's abscissa is labeled "Return Period", it is actually an (asymptotic) approximation, which is not very accurate at lower values. One can plot the exact return periods, but at the expense of losing the exact lienarity when the shape parameter is identically zero, which is arguably more important. Common practice is to label the axis as "Return Period."
Rick Katz's page on extremes
Reference list for spatial and spatio-temporal extreme value analysis
Back to TopThe extRemes and in2extRemes software packages are made possible by funding from the Weather and Climate Impacts Assessment Science Program, which is funded by the National Science Foundation. Much of the style of the new version (>= 2.0) was inspired by that of the texmex package. We thank Chris Paciorek for helping to de-bug the initial releases of version 2, as well as for contributing some additional functionality to the package (e.g. confidence intervals for effective return levels). Some additional functionality has been supplied by the following people.
Peter Guttorp (quantile plotting functions with confidence bands, such as qqplot).
Alec Stephenson (abba, a fairly new spatial EVA method).
Simone Russo (hwmi, a heat wave index function).
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