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Title:

Some Notes on the Past and Future of Lisp-Stat

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University of California at Los Angeles, Department of Statistics

Year of Publication:

2005-01-01T00:00:00Z

Document Type:

article

Language:

English

Subjects:

LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:S...

LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H Minimize

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http://www.jstatsoft.org/v13/i09/paper

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Title:

Some Notes on the Past and Future of Lisp-Stat

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Lisp-Stat was originally developed as a framework for experimenting with dynamic graphics in statistics. To support this use, it evolved into a platform for more general statistical computing. The choice of the Lisp language as the basis of the system was in part coincidence and in part a very deliberate decision. This paper describes the backgr...

Lisp-Stat was originally developed as a framework for experimenting with dynamic graphics in statistics. To support this use, it evolved into a platform for more general statistical computing. The choice of the Lisp language as the basis of the system was in part coincidence and in part a very deliberate decision. This paper describes the background behind the choice of Lisp, as well as the advantages and disadvantages of this choice. The paper then discusses some lessons that can be drawn from experience with Lisp-Stat and with the R language to guide future development of Lisp-Stat, R, and similar systems. Minimize

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Title:

Code analysis and parallelizing vector operations in R

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Static analysis, Compilation, Software testing, Parallel computation

Static analysis, Compilation, Software testing, Parallel computation Minimize

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mritc: A Package for MRI Tissue Classification

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This paper presents an R package for magnetic resonance imaging (MRI) tissue classification. The methods include using normal mixture models, hidden Markov normal mixture models, and a higher resolution hidden Markov normal mixture model fitted by various optimization algorithms and by a Bayesian Markov chain Monte Carlo (MCMC) method. Functions...

This paper presents an R package for magnetic resonance imaging (MRI) tissue classification. The methods include using normal mixture models, hidden Markov normal mixture models, and a higher resolution hidden Markov normal mixture model fitted by various optimization algorithms and by a Bayesian Markov chain Monte Carlo (MCMC) method. Functions to obtain initial values of parameters of normal mixture models and spatial parameters are provided. Supported input formats are ANALYZE, NIfTI, and a raw byte format. The function slices3d in misc3d is used for visualizing data and results. Various performance evaluation indices are provided to evaluate classification results. To improve performance, table lookup methods are used in several places, and vectorized computation taking advantage of conditional independence properties are used. Some computations are performed by C code, and OpenMP is used to parallelize key loops in the C code. Minimize

Publisher:

University of California, Los Angeles

Year of Publication:

2011-10-01T00:00:00Z

Document Type:

article

Language:

English

Subjects:

higher resolution hidden Markov normal mixture model ; Bayesian Markov chain Monte Carlo ; table lookup ; conditional independence ; OpenMP ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathem...

higher resolution hidden Markov normal mixture model ; Bayesian Markov chain Monte Carlo ; table lookup ; conditional independence ; OpenMP ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H Minimize

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310 Collections of general statistics *(computed)*

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http://www.jstatsoft.org/v44/i07/paper

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Title:

Computing and Displaying Isosurfaces in R

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This paper presents R utilities for computing and displaying isosurfaces, or three-dimensional contour surfaces, from a three-dimensional array of function values. A version of the marching cubes algorithm that takes into account face and internal ambiguities is used to compute the isosurfaces. Vectorization is used to ensure adequate performanc...

This paper presents R utilities for computing and displaying isosurfaces, or three-dimensional contour surfaces, from a three-dimensional array of function values. A version of the marching cubes algorithm that takes into account face and internal ambiguities is used to compute the isosurfaces. Vectorization is used to ensure adequate performance using only R code. Examples are presented showing contours of theoretical densities, density estimates, and medical imaging data. Rendering can use the rgl package or standard or grid graphics, and a set of tools for representing and rendering surfaces using standard or grid graphics is presented. Minimize

Publisher:

University of California at Los Angeles, Department of Statistics

Year of Publication:

2008-09-01T00:00:00Z

Document Type:

article

Language:

English

Subjects:

marching cubes algorithm ; density estimation ; medical imaging ; surface illumination ; shading ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC...

marching cubes algorithm ; density estimation ; medical imaging ; surface illumination ; shading ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H Minimize

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050 General serial publications *(computed)*

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http://www.jstatsoft.org/v28/i01/paper

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Title:

Computing and Displaying Isosurfaces in R

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This paper presents R utilities for computing and displaying isosurfaces, or three-dimensional contour surfaces, from a three-dimensional array of function values. A version of the marching cubes algorithm that takes into account face and internal ambiguities is used to compute the isosurfaces. Vectorization is used to ensure adequate performanc...

This paper presents R utilities for computing and displaying isosurfaces, or three-dimensional contour surfaces, from a three-dimensional array of function values. A version of the marching cubes algorithm that takes into account face and internal ambiguities is used to compute the isosurfaces. Vectorization is used to ensure adequate performance using only R code. Examples are presented showing contours of theoretical densities, density estimates, and medical imaging data. Rendering can use the rgl package or standard or grid graphics, and a set of tools for representing and rendering surfaces using standard or grid graphics is presented. Minimize

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R topics documented: flatProfile. 1 plotProfileCallGraph. 3 printProfileCallGraph. 4 profileCallGraph2Dot. 6 readProfileData. 8

R topics documented: flatProfile. 1 plotProfileCallGraph. 3 printProfileCallGraph. 4 profileCallGraph2Dot. 6 readProfileData. 8 Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2011-06-17

Source:

http://cran.at.r-project.org/web/packages/proftools/proftools.pdf

http://cran.at.r-project.org/web/packages/proftools/proftools.pdf Minimize

Document Type:

text

Language:

en

Subjects:

Rprof ; summaryRprof ; readProfileData ; plotProfileCallGraph ; printProfileCallGraph ; profileCallGraph2Dot Examples

Rprof ; summaryRprof ; readProfileData ; plotProfileCallGraph ; printProfileCallGraph ; profileCallGraph2Dot Examples Minimize

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Draw an Isosurface, a Three Dimension Contour Plot Description Usage

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R topics documented: contour3d. 2 drawScene. 5 image3d. 8 kde3d. 9 lighting. 10 parametric3d. 11 pointsTetrahedra. 13 slices3d. 14 surfaceTriangles. 16 teapot. 17 triangles. 17

R topics documented: contour3d. 2 drawScene. 5 image3d. 8 kde3d. 9 lighting. 10 parametric3d. 11 pointsTetrahedra. 13 slices3d. 14 surfaceTriangles. 16 teapot. 17 triangles. 17 Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2011-06-21

Source:

http://cran.at.r-project.org/web/packages/misc3d/misc3d.pdf

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Document Type:

text

Language:

en

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

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Suggests rgl, tkrplot, MASS Description A collection of miscellaneous 3d plots, including isosurfaces. License GPL Repository CRAN Date/Publication 2013-01-25 15:57:56

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R topics documented: computeContour3d. 2 contour3d. 3 drawScene. 6 exportScene. 9 image3d. 10 kde3d. 11 lighting. 12 linesTetrahedra. 13

R topics documented: computeContour3d. 2 contour3d. 3 drawScene. 6 exportScene. 9 image3d. 10 kde3d. 11 lighting. 12 linesTetrahedra. 13 Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2013-07-24

Source:

http://cran.at.r-project.org/web/packages/misc3d/misc3d.pdf

http://cran.at.r-project.org/web/packages/misc3d/misc3d.pdf Minimize

Document Type:

text

Language:

en

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

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Title:

EXACT TRANSITION PROBABILITIES FOR THE INDEPENDENCE METROPOLIS SAMPLER Richard L. Smith

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. A recent result of Jun Liu's has shown how to compute explicitly the eigenvalues and eigenvectors for the Markov chain derived from a special case of the Hastings sampling algorithm, known as the indepdendence Metropolis sampler. In this note, we show how to extend the result to obtain exact n-step transition probabilities for any n. This is d...

. A recent result of Jun Liu's has shown how to compute explicitly the eigenvalues and eigenvectors for the Markov chain derived from a special case of the Hastings sampling algorithm, known as the indepdendence Metropolis sampler. In this note, we show how to extend the result to obtain exact n-step transition probabilities for any n. This is done first for a chain on a finite state space, and then extended to a general (discrete or continuous) state space. The paper concludes with some implications for diagnostic tests of convergence of Markov chain samplers. 1. Introduction Suppose we want to generate a Monte Carlo sample from a discrete distribution j ; j = 1; :::; m. The distribution is not easy to sample from directly, and it is possible that the functional form of j may be known only up to some unspecified normalizing constant, which will be inconvenient to compute directly if m is very large. One strategy is as follows: pick some other distribution fp j ; j = 1; :::; mg whi. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-13

Source:

http://www.unc.edu/depts/statistics/postscript/rs/exact.ps

http://www.unc.edu/depts/statistics/postscript/rs/exact.ps Minimize

Document Type:

text

Language:

en

DDC:

519 Probabilities & applied mathematics *(computed)*

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