ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R

Kernel smoothing is one of the most widely used non-parametric data smoothing techniques. We introduce a new R package ks for multivariate kernel smoothing. Currently it contains functionality for kernel density estimation and kernel discriminant analysis. It is a comprehensive package for bandwidth matrix selection, implementing a wide range of data-driven diagonal and unconstrained bandwidth selectors.

ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R

Kernel smoothing is one of the most widely used non-parametric data smoothing techniques. We introduce a new R package ks for multivariate kernel smoothing. Currently it contains functionality for kernel density estimation and kernel discriminant analysis. It is a comprehensive package for bandwidth matrix selection, implementing a wide range of data-driven diagonal and unconstrained bandwidth selectors

Relaxing door-to-door matching reduces passenger waiting times: a workflow for the analysis of driver GPS traces in a stochastic carpooling service

Carpooling has the potential to transform itself into a mass transportation mode by abandoning its adherence to deterministic passenger-driver matching for door-to-door journeys, and by adopting instead stochastic matching on a network of fixed meeting points. Stochastic matching is where a passenger sends out a carpooling request at a meeting point, and then waits for the arrival of a self-selected driver who is already travelling to the requested meeting point. Crucially there is no centrally dispatched driver. Moreover, the carpooling is assured only between the meeting points, so the onus is on the passengers to travel to/from them by their own means. Thus the success of a stochastic carpooling service relies on the convergence, with minimal perturbation to their existing travel patterns, to the meeting points which are highly frequented by both passengers and drivers. Due to the innovative nature of stochastic carpooling, existing off-the-shelf workflows are largely insufficient for this purpose. To fill the gap in the market, we introduce a novel workflow, comprising of a combination of data science and GIS (Geographic Information Systems), to analyse driver GPS traces. We implement it for an operational stochastic carpooling service in south-eastern France, and we demonstrate that relaxing door-to-door matching reduces passenger waiting times. Our workflow provides additional key operational indicators, namely the driver flow maps, the driver flow temporal profiles and the driver participation rates

Joint Modeling and Registration of Cell Populations in Cohorts of High-Dimensional Flow Cytometric Data

In systems biomedicine, an experimenter encounters different potential sources of variation in data such as individual samples, multiple experimental conditions, and multi-variable network-level responses. In multiparametric cytometry, which is often used for analyzing patient samples, such issues are critical. While computational methods can identify cell populations in individual samples, without the ability to automatically match them across samples, it is difficult to compare and characterize the populations in typical experiments, such as those responding to various stimulations or distinctive of particular patients or time-points, especially when there are many samples. Joint Clustering and Matching (JCM) is a multi-level framework for simultaneous modeling and registration of populations across a cohort. JCM models every population with a robust multivariate probability distribution. Simultaneously, JCM fits a random-effects model to construct an overall batch template -- used for registering populations across samples, and classifying new samples. By tackling systems-level variation, JCM supports practical biomedical applications involving large cohorts