Title: Regression Functions and Datasets
Description: Functions and Datasets designed to support teaching of
regression with applications to Marketing and Finance

Packaged: 2010-12-21 04:44:55 UTC; perossichi

Packaged: 2011-01-04 19:28:43 UTC; perossichi

Packaged: 2011-01-31 19:04:30 UTC; perossichi

Packaged: 2011-02-26 20:42:19 UTC; perossichi

Description: Functions and Datasets designed to support teaching of regression with applications to Marketing and Finance
Packaged: 2013-10-28 20:01:50 UTC; perossichi

Packaged: 2013-11-01 01:06:20 UTC; perossichi

Packaged: 2013-11-07 00:39:59 UTC; perossichi

Title: Simulation and Reconstruction of PET Images
Maintainer: Joern Schulz Schulz <jschulz78@web.de>
Requires: The R-package 'adimpro' (for reading and writing some
graphic formats, it is used in the demos too).
Description: This package implements different analytic/direct and
iterative reconstruction methods of Peter Toft. It also
offer the possibility to simulate PET data.
License: GPL version 2 or newer. This library is Copyright (C) 2007
Packaged: Sun Apr  1 13:19:55 2007; jschulz

Author: Joern Schulz <jschulz78@web.de> Peter Toft <pto@imm.dtu.dk>
Jesper James Jensen <jjj@oedan.dk> Peter Philipsen
Requires: The R-package 'adimpro' (for reading and writing some graphic
iterative reconstruction methods of Peter Toft. It also offer
Copyright: This package is Copyright (C) 2006-2009 by Joern Schulz and

Copyright: This package is Copyright (C) 2006-2010 by Joern Schulz and

Description: Implementation of different analytic/direct and
iterative reconstruction methods of radon transformed data
such as PET data. It also offer the possibility to simulate PET data.

Depends: R (>= 3.2.0), adimpro (>= 0.8.2), grDevices, graphics, stats,
Copyright: This package is Copyright (C) 2006-2018 by Joern Schulz and

Description: Bayesian supervised predictive classifiers, hypothesis testing, and parametric estimation under Partition Exchangeability are implemented. The two classifiers presented are the marginal classifier (that assumes test data is i.i.d.) next to a more computationally costly but accurate simultaneous classifier (that finds a labelling for the entire test dataset at once based on simultanous use of all the test data to predict each label). We also provide the Maximum Likelihood Estimation (MLE) of the only underlying parameter of the partition exchangeability generative model as well as hypothesis testing statistics for equality of this parameter with a single value, alternative, or multiple samples. We present functions to simulate the sequences from Ewens Sampling Formula as the realisation of the Poisson-Dirichlet distribution and their respective probabilities.
Ali Amiryousefi [aut, cre] (<https://orcid.org/0000-0002-6317-3860>)