Ris on CRAN. The package is one of the most downloaded R extensions and provides a rich set of string processing procedures.
* [WINDOWS SPECIFIC] #270: Strings marked with `latin1` encoding are now converted internally to UTF-8 using the WINDOWS-1252 codec. This fixes problems with - among others - displaying the Euro sign. * [NEW FEATURE] #263: Add support for custom rule-based break iteration, see `?stri_opts_brkiter`. * [NEW FEATURE] #267: `omit_na=TRUE` in `stri_sub<-` now ignores missing values in any of the arguments provided. * [BUGFIX] fixed unPROTECTed variable names and stack imbalances as reported by rchk
Abstract. As cities increase in size, governments and councils face the problem of designing infrastructure and approaches to traffic management that alleviate congestion. The problem of objectively measuring congestion involves taking into account not only the volume of traffic moving throughout a network, but also the inequality or spread of this traffic over major and minor intersections. For modelling such data, we investigate the use of weighted congestion indices based on various aggregation and spread functions. We formulate the weight learning problem for comparison data and use real traffic data obtained from a medium-sized Australian city to evaluate their usefulness.
Abstract. Aggregation theory classically deals with functions to summarize a sequence of numeric values, e.g., in the unit interval. Since the notion of componentwise monotonicity plays a key role in many situations, there is an increasingly growing interest in methods that act on diverse ordered structures.
However, as far as the definition of a mean or an averaging function is concerned, the internality (or at least idempotence) property seems to be of a relatively higher importance than the monotonicity condition. In particular, the Bajraktarević means or the mode are among some well-known non-monotone means.
The concept of a penalty-based function was first investigated by Yager in 1993. In such a framework, we are interested in minimizing the amount of "disagreement" between the inputs and the output being computed; the corresponding aggregation functions are at least idempotent and express many existing means in an intuitive and attractive way.
In this talk I focus on the notion of penalty-based aggregation of sequences of points in Rd, this time for some d≥1. I review three noteworthy subclasses of penalty functions: componentwise extensions of unidimensional ones, those constructed upon pairwise distances between observations, and those defined by measuring the so-called data depth. Then, I discuss their formal properties, which are particularly useful from the perspective of data analysis, e.g., different possible generalizations of internality or equivariances to various geometric transforms. I also point out the difficulties with extending some notions that are key in classical aggregation theory, like the monotonicity property.