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Title:

Econometrics with Octave

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GNU Octave is an open-source implementation of a (mostly Matlab compatible) high-level language for numerical computations. This review briefly introduces Octave, discusses applications of Octave in an econometric context, and illustrates how to extend Octave with user-supplied C++ code. Several examples are provided. Copyright © 2000 John Wiley...

GNU Octave is an open-source implementation of a (mostly Matlab compatible) high-level language for numerical computations. This review briefly introduces Octave, discusses applications of Octave in an econometric context, and illustrates how to extend Octave with user-supplied C++ code. Several examples are provided. Copyright © 2000 John Wiley & Sons, Ltd. Minimize

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Analysis of Integrated and Cointegrated Time Series with R (2nd Edition)

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Applied Econometrics with R

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Computational Statistics: An Introduction to R

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R in a Nutshell

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Rcpp: Seamless R and C++ Integration

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The Rcpp package simplifies integrating C++ code with R. It provides a consistent C++ class hierarchy that maps various types of R objects (vectors, matrices, functions, environments, . . . ) to dedicated C++ classes. Object interchange between R and C++ is managed by simple, flexible and extensible concepts which include broad support for C++ S...

The Rcpp package simplifies integrating C++ code with R. It provides a consistent C++ class hierarchy that maps various types of R objects (vectors, matrices, functions, environments, . . . ) to dedicated C++ classes. Object interchange between R and C++ is managed by simple, flexible and extensible concepts which include broad support for C++ Standard Template Library idioms. C++ code can both be compiled, linked and loaded on the fly, or added via packages. Flexible error and exception code handling is provided. Rcpp substantially lowers the barrier for programmers wanting to combine C++ code with R. Minimize

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University of California, Los Angeles

Year of Publication:

2011-04-01T00:00:00Z

Document Type:

article

Language:

English

Subjects:

R ; C++ ; foreign function interface ; .Call ; 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:Statis...

R ; C++ ; foreign function interface ; .Call ; 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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Rcpp: Seamless R and C++ Integration

Description:

The Rcpp package simplifies integrating C++ code with R. It provides a consistent C++ class hierarchy that maps various types of R objects (vectors, matrices, functions, environments, . . . ) to dedicated C++ classes. Object interchange between R and C++ is managed by simple, flexible and extensible concepts which include broad support for C++ S...

The Rcpp package simplifies integrating C++ code with R. It provides a consistent C++ class hierarchy that maps various types of R objects (vectors, matrices, functions, environments, . . . ) to dedicated C++ classes. Object interchange between R and C++ is managed by simple, flexible and extensible concepts which include broad support for C++ Standard Template Library idioms. C++ code can both be compiled, linked and loaded on the fly, or added via packages. Flexible error and exception code handling is provided. Rcpp substantially lowers the barrier for programmers wanting to combine C++ code with R. Minimize

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A Simple Example

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We assume a recent version of R such that install.packages(c("Rcpp","RInside","inline")) gets us current versions of the packages. RProtoBuf need the Protocol Buffer library and headers (which is not currently available on Windows / MinGW). All examples shown should work ’as is ’ on Unix-alike OSs; most will also work on Windows provided a compl...

We assume a recent version of R such that install.packages(c("Rcpp","RInside","inline")) gets us current versions of the packages. RProtoBuf need the Protocol Buffer library and headers (which is not currently available on Windows / MinGW). All examples shown should work ’as is ’ on Unix-alike OSs; most will also work on Windows provided a complete R development environment The Reference Classes examples assume R 2.12.0 and Rcpp 0.8.7. We may imply a using namespace Rcpp; in some of the C++ examples. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2013-10-09

Source:

http://dirk.eddelbuettel.com/papers/googleOct2010.pdf

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text

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en

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random: An R package for true random numbers

Description:

Simulation techniques are a core component of scientific computing and, more specifically, computational statistics. All simulation methods—Monte Carlo methods, bootstrapping, estimation by simulation to name but a few—rely on ‘computer-generated randomness ’ (more on this below). In practice, this means sequences of random numbers. Generating ‘...

Simulation techniques are a core component of scientific computing and, more specifically, computational statistics. All simulation methods—Monte Carlo methods, bootstrapping, estimation by simulation to name but a few—rely on ‘computer-generated randomness ’ (more on this below). In practice, this means sequences of random numbers. Generating ‘good ’ (for a suitable metric) random Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2010-07-25

Source:

http://cran.at.r-project.org/web/packages/random/vignettes/random-intro.pdf

http://cran.at.r-project.org/web/packages/random/vignettes/random-intro.pdf Minimize

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text

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en

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Title:

Benchmarking Single- and Multi-Core BLAS Implementations and GPUs for use with R

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We provide timing results for common linear algebra subroutines across BLAS (Basic Linear Algebra Subprograms) and GPU (Graphics Processing Unit)-based implementations. Several BLAS implementations are compared. The first is the unoptimised reference BLAS which provides a baseline to measure against. Second is the Atlas tuned BLAS, configured fo...

We provide timing results for common linear algebra subroutines across BLAS (Basic Linear Algebra Subprograms) and GPU (Graphics Processing Unit)-based implementations. Several BLAS implementations are compared. The first is the unoptimised reference BLAS which provides a baseline to measure against. Second is the Atlas tuned BLAS, configured for single-threaded mode. Third is the development version of Atlas, configured for multi-threaded mode. Fourth is the optimised and multi-threaded Goto BLAS. Fifth is the multi-threaded BLAS contained in the commercial Intel MKL package. We also measure the performance of a GPU-based implementation for R (R Development Core Team 2010a) provided by the package gputools (Buckner et˜al. 2010). Several frequently-used linear algebra computations are compared across BLAS (and LAPACK) implementations and via GPU computing: matrix multiplication as well as QR, SVD and LU decompositions. The tests are performed from an end-user perspective, and ‘net ’ times (including all necessary data transfers) are compared. While results are by their very nature dependent on the hardware of the test platforms Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2012-05-06

Source:

http://cran.r-project.org/web/packages/gcbd/vignettes/gcbd.pdf

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text

Language:

en

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MacKinnon

MacKinnon Minimize

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