Abstract: The Choquet, Sugeno, and Shilkret integrals with respect to monotone measures, as well as their generalization – the universal integral, stand for a useful tool in decision support systems. In this paper we propose a general construction method for aggregation operators that may be used in assessing output of scientists. We show that the most often currently used indices of bibliometric impact, like Hirsch's h, Woeginger's w, Egghe's g, Kosmulski's MAXPROD, and similar constructions, may be obtained by means of our framework. Moreover, the model easily leads to some new, very interesting functions.
stringi is THE R package for correct, fast, and simple string processing in each locale and native charset. Another alpha release (for testing purposes) can be automatically downloaded by calling in R:
source('http://stringi.rexamine.com/install.R') # Message from the future: the link is outdated
The auto-installer gives access to a Windows i386/x64 build for R 3.0 or allows building the package from sources on Linux or MacOS.
UPDATE@2013-11-13. Version 0.1-10 now available.
Includes some bugfixes. Moreover, on Linux/UNIX
./configure now first
tries to read build settings from
(as the usage of
icu-config is deprecated).
UPDATE@2013-11-16. Version 0.1-11 now available.
ICU4C is now statically linked on Windows, so there is no need
to download any additional libraries – a binary version is
now available for R 2.15.X and 3.0.X. Moreover, on platforms where
packages are built from sources, the
now tries to find ICU4C automagically.
UPDATE@2013-11-21. Build of version 0.1-11 now available for OS X (x64) and R 3.0. Have fun.
UPDATE@2014-02-15. Version 0.1-20 (source and Win_build only) now available. Now it does not depend on any external ICU library (the library source code is included).
The alpha release (for testing purposes) is available here (includes Windows i386/x64 build for R 3.0). Any comments and suggestions are welcome!
Gagolewski M., Scientific Impact Assessment Cannot be Fair, Journal of Informetrics 7(4), 2013, pp. 792-802.
Abstract: In this paper we deal with the problem of aggregating numeric sequences of arbitrary length that represent e.g. citation records of scientists. Impact functions are the aggregation operators that express as a single number not only the quality of individual publications, but also their author's productivity.
We examine some fundamental properties of these aggregation tools. It turns out that each impact function which always gives indisputable valuations must necessarily be trivial. Moreover, it is shown that for any set of citation records in which none is dominated by the other, we may construct an impact function that gives any a priori-established authors' ordering. Theoretically then, there is considerable room for manipulation in the hands of decision makers.
We also discuss the differences between the impact function-based and the multicriteria decision making-based approach to scientific quality management, and study how the introduction of new properties of impact functions affects the assessment process. We argue that simple mathematical tools like the h- or g-index (as well asother bibliometric impact indices) may not necessarily be a good choice when it comes to assess scientific achievements.
** FuzzyNumbers Package CHANGELOG ** *************************************************************************** 0.3-1 /2013-06-23/ * piecewiseLinearApproximation() - general case (any knot.n) for method="NearestEuclidean" now available. Thus, method="ApproximateNearestEuclidean" is now deprecated. * New binary arithmetic operators, especially for PiecewiseLinearFuzzyNumbers: +, -, *, / * New method: fapply() - applies a function on a PLFN using the extension principle * New methods: as.character(); also used by show(). This function also allows to generate LaTeX code defining the FN (toLaTeX arg thanks to Jan Caha). * as.FuzzyNumber(), as.TriangularFuzzyNumber(), as.PowerFuzzyNumber(), and as.PiecewiseLinearFuzzyNumber() are now S4 methods, and can be called on objects of type numeric, as well as on various FNs * piecewiseLinearApproximation() and as.PiecewiseLinearFuzzyNumber() argument `knot.alpha` now defaults to equally distributed knots (via given `knot.n`). If `knot.n` is missing, then it is guessed from `knot.alpha`. * PiecewiseLinearFuzzyNumber() now accepts missing `a1`, `a2`, `a3`, `a4`, and `knot.left`, `knot.right` of length `knot.n`+2. Moreover, if `knot.n` is not given, then it is guessed from length(knot.left). If `knot.alpha` is missing, then the knots will be equally distributed on the interval [0,1]. * alphacut() now always returns a named two-column matrix. evaluate() returns a named vector. * New function: TriangularFuzzyNumber - returns a TrapezoidalFuzzyNumber. * Function renamed: convert.side to convertSide, convert.alpha to convertAlpha, approx.invert to approxInvert * Added a call to setGeneric("plot", function(x, y, ...) ... to avoid a warning on install * The FuzzyNumbers Tutorial has been properly included as the package's vignette * DiscontinuousFuzzyNumber class has been marked as **EXPERIMENTAL** in the manual * Man pages extensively updated * FuzzyNumbers devel repo moved to GitHub