Revision 1061

branches/c++11-std-thread/NEWS.txt (revision 1061)
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CHANGES IN ADMB VERSION 11.0:
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  SIGNIFICANT USER-VISIBLE CHANGES:
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    * The compilation scripts (adcomp, adlink, admb) compile in
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      "safe" mode by default.
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    * [anything that works differently from before, given the
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      same input]
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  NEW FEATURES:
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    * Functions from user-contributed packages can be loaded from
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      directory 'contrib', [maybe indicate how that is done].
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    * New class 'init_table' to read input data into a matrix.
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    * New function sumsq() to calculate sum of squared values,
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      equivalent to norm2().
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    * New constant PI for 3.141593... Makes models more portable
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      than using M_PI or other compiler-specific constants.
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    * New shell command 'ad2csv' to write binary files to screen
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      or to a file.
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    * [many more]
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  DEPRECATED AND DEFUNCT:
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  INSTALLATION:
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    * [not the whole installation procedure, only how the
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      procedure has changed]
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  BUG FIXES:
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CHANGES IN ADMB VERSION 10.1:
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CHANGES IN ADMB VERSION 10.0:
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CHANGES IN ADMB VERSION 9.1:
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etc.
branches/c++11-std-thread/README.txt (revision 1061)
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ADMB Project
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Version trunk
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Release date
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Overview
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========
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The ADMB (Automatic Differentiation Model Builder) software suite is an
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environment for nonlinear statistical modeling enabling rapid model
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development, numerical stability, fast and efficient computation, and high
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accuracy parameter estimates. AD Model Builder is a high level language built
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around the AUTODIF Library, a C++ language extension which transparently
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implements reverse mode automatic differentiation. A closely related software
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package, ADMB-RE, implements random effects in nonlinear models.
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Donations
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=========
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ADMB is an open source project that is freely available for download. If you
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would like to contribute or donate funds, please contact users@admb-project.org.
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Cool ADMB T-shirts are also available for purchase.
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How to Cite ADMB
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================
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Fournier, D.A., H.J. Skaug, J. Ancheta, J. Ianelli, A. Magnusson, M.N. Maunder,
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A. Nielsen, and J. Sibert. 2012. AD Model Builder: using automatic
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differentiation for statistical inference of highly parameterized complex
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nonlinear models. Optim. Methods Softw. 27:233-249.
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It can be downloaded for free from the publisher:
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http://tandfonline.com/doi/abs/10.1080/10556788.2011.597854
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Table of Contents
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=================
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 * Release Notes
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   - Changes
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 * Requirements
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 * Downloading
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 * Installation from Binary Distribution
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   - Linux and GCC
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   - MacOS and XCode
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   - Windows and GCC
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   - Windows and Microsoft Visual C++ 2010 Express
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 * Installation from Source Code
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   - Linux/MacOS and GCC
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   - Linux and Intel Compiler
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   - Linux and Solaris Studio
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   - MacOS and Clang
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   - Windows and GCC
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