Pulsed high-power arc heater with improved cathode and triggering mechanism

System employs pulsed, constricted arc heater capable of multi-MW power, permitting quasi-stationary flow conditions during latter half of pulse of about 5 msec. System description is given

A Common Platform for Graphical Models in R: The gRbase Package

The gRbase package is intended to set the framework for computer packages for data analysis using graphical models. The gRbase package is developed for the open source language, R, and is available for several platforms. The package is intended to be widely extendible and flexible so that package developers may implement further types of graphical models using the available methods. The gRbase package consists of a set of S version 3 classes and associated methods for representing data and models. The package is linked to the dynamicGraph package (Badsberg 2005), an interactive graphical user interface for manipulating graphs. In this paper, we show how these building blocks can be combined and integrated with inference engines in the special cases of hierarchical loglinear models. We also illustrate how to extend the package to deal with other types of graphical models, in this case the graphical Gaussian models.

Formulating State Space Models in R with Focus on Longitudinal Regression Models

We provide a language for formulating a range of state space models with response densities within the exponential family. The described methodology is implemented in the R-package sspir. A state space model is specified similarly to a generalized linear model in R, and then the time-varying terms are marked in the formula. Special functions for specifying polynomial time trends, harmonic seasonal patterns, unstructured seasonal patterns and time-varying covariates can be used in the formula. The model is fitted to data using iterated extended Kalman filtering, but the formulation of models does not depend on the implemented method of inference. The package is demonstrated on three datasets.

deal: A Package for Learning Bayesian Networks

deal is a software package for use with R. It includes several methods for analysing data using Bayesian networks with variables of discrete and/or continuous types but restricted to conditionally Gaussian networks. Construction of priors for network parameters is supported and their parameters can be learned from data using conjugate updating. The network score is used as a metric to learn the structure of the network and forms the basis of a heuristic search strategy. deal has an interface to Hugin.

Experimental and evaluation studies of a coaxial plasma gun accelerator Final report

Pulsed coaxial plasma gun accelerators in space thrustor developmen