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Modeling Overdispersion in R

2015
The book Overdispersion Models in SAS by Morel and Neerchal (2012) discusses statistical analysis of categorical and count data which exhibit overdispersion, with a focus on computational procedures using SAS. This document retraces some of the ground covered in the book, which we abbreviate throughout as OMSAS, with the objective of carrying out ...
Raim, Andrew M.   +2 more
openaire   +1 more source

A Nonsequential R & D Search Model

Management Science, 1982
Managers of research and development (R&D) programs are often confronted with questions of what determines R&D spending (i.e. R&D search intensity) and when R&D spending is unprofitable (i.e. Reservation Technology Level). Three determinants of R&D spending are identified: the current technology level, the length of decision ...
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Comparison of R-R interval prediction models.

Medical instrumentation, 1978
Changes in the sequence or pattern of R-R intervals is an integral part of electrocardiogram detection of most clinically important arrhythmias. Moreover, there is inherent interest in these patterns and their method of study as basic physiological phenomena.
T M, Grove   +3 more
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Forward Modelling in R

2015
This chapter contains five solved problems on forward modelling of the behaviour of trace elements using R (see Chaps. 11 and 13 for principles). They include theoretical treatment of batch and fractional crystallization equations, development of REE during tonalite magma fractionation, partial melting of primitive mantle or depleted mantle reservoirs,
Vojtěch Janoušek   +4 more
openaire   +1 more source

Statistical Modeling With R

2022
Abstract For most of the twentieth century through to the present day, statistics has been neatly divided into two theoretical frameworks: classical/frequentist and Bayesian. Scientists typically choose the statistical theoretical framework to analyze their data depending on the nature and complexity of the problem, and based on their ...
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Modelling Quality with R

2015
This chapter provides the necessary background to understand the fundamental ideas of descriptive and inferential statistics. In particular, the basic ideas and tools used in the description both graphical and numerical, of the inherent variability always present in real world are described.
Emilio L. Cano   +2 more
openaire   +1 more source

Reference node r model

Proceedings of the IEEE, 1983
The Roytman and Swamy method of circuit diagnosis is one of many available results found within the reference node r model suggested in this paper. This model provides a focal point upon which concepts including the node-pair functions, the driving point, and transfer relationships can be evaluated.
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Graphical Models with R

2012
Graphical models in their modern form have been around since the late 1970s and appear today in many areas of the sciences. Along with the ongoing developments of graphical models, a number of different graphical modeling software programs have been written over the years.
Højsgaard, Søren   +2 more
openaire   +3 more sources

Reverse Modelling in R

2015
This chapter contains two solved problems on reverse modelling of the behaviour of trace elements using R (see Chap. 12 for principles). One concerns fractionation of tonalitic magma (given the compositions of primitive and fractionated melt and partition coefficients of the principal mineral phases). The other is a reverse problem of garnet lherzolite
Vojtěch Janoušek   +4 more
openaire   +1 more source

Statistical Modelling in R

2009
Abstract R is now the most widely used statistical package/language in university statistics departments and many research organisations. Its great advantages are that for many years it has been the leading-edge statistical package/language and that it can be freely downloaded from the R web site.
Murray Aitkin   +3 more
openaire   +1 more source

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