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This is an additional class of ruby with conduct processing of missing value to NArray which is a numeric multi-dimensional array class.

Miscellaneous functions and classes to facilitate programming. Misc requires NArray, a multi-dimensional numeric array class for ruby.

A class of units of physical quantities for Ruby. This class covers most functionality of UNIDATA's UDUNITS Library, however, with a more sophisticated handling of string expressions. UDUNITS always decomposes units into the four base units and discards the original string expressions. Therefore, 'hPa' always becomes '100 kg.m-1.sec-1', and 'day' always becomes '86400 sec'. On the other hand, this library tries to keep the original expressions as much as possible by default, while allowing partial to complete decompositions if needed.

SAGA - short hand for "System for Automated Geoscientific Analyses" - is a free, hybrid, cross-platform GIS software. The heart of SAGA is its C++ and thus object oriented Application Programming Interface (API), providing data object definitions and computational methods for raster, vector and tabular data. As a normal user, you will not get into touch with the API. But as an interested scientist or coder you will soon discover its great flexibility.

A S-Lang module to plot graphs using grace. Grace is a popular plotting tool that produces publication quality plots. The S-Lang module interacts with grace via pipes.

SNNS (Stuttgart Neural Network Simulator) is a software simulator for neural networks on Unix workstations developed at the Institute for Parallel and Distributed High Performance Systems (IPVR) at the University of Stuttgart. The goal of the SNNS project is to create an efficient and flexible simulation environment for research on and application of neural nets. The SNNS simulator consists of two main components: 1) simulator kernel written in C 2) graphical user interface under X The simulator kernel operates on the internal network data structures of the neural nets and performs all operations of learning and recall. It can also be used without the other parts as a C program embedded in custom applications. It supports arbitrary network topologies and the concept of sites. SNNS can be extended by the user with user defined activation functions, output functions, site functions and learning procedures, which are written as simple C programs and linked to the simulator kernel. The graphical user interface XGUI (X Graphical User Interface), built on top of the kernel, gives a 2D and a 3D graphical representation of the neural networks and controls the kernel during the simulation run. In addition, the 2D user interface has an integrated network editor which can be used to directly create, manipulate and visualize neural nets in various ways.

SPOOLES is a library for solving sparse real and complex linear systems of equations, written in the C language using object oriented design. At present, there is the following functionality: 1. Compute multiple minimum degree, generalized nested dissection and multisection orderings of matrices with symmetric structure. 2. Factor and solve square linear systems of equations with symmetric structure, with or without pivoting for stability. The factorization can be symmetric LDLT, Hermitian LDLH, or nonsymmetric LDU. A direct factorization or a drop tolerance factorization can be computed. The factors and solve can be done in serial mode, multithreaded with Solaris or POSIX threads, or with MPI. 3. Factor and solve overdetermined full rank systems of equations using a multifrontal QR factorization, in serial or using POSIX threads. 4. Solve square linear systems using a variety of Krylov iterative methods. The preconditioner is a drop tolerance factorization, with or without pivoting for stability.

STP is a constraint solver (also referred to as a decision procedure or automated prover) aimed at solving constraints generated by program analysis tools, theorem provers, automated bug finders, intelligent fuzzers and model checkers. STP has been used in many research projects at Stanford, Berkeley, MIT, CMU and other universities. It is also being used at many companies such as NVIDIA, some startup companies, and by certain government agencies. The input to STP are formulas over the theory of bit-vectors and arrays (This theory captures most expressions from languages like C/C++/Java and Verilog), and the output of STP is a single bit of information that indicates whether the formula is satisfiable or not. If the input is satisfiable, then it also generates a variable assignment to satisfy the input formula.

SuiteSparse is a set of sparse matrices libraries. It contains: * AMD: symmetric approximate minimum degree * BTF: permutation to block triangular form (beta) * CCOLAMD: constrained column approximate minimum degree * COLAMD: column approximate minimum degree * CHOLMOD: sparse supernodal Cholesky factorization and update/downdate * KLU: sparse LU factorization, for circuit simulation (beta) * LDL: a simple LDL^T factorization * UMFPACK: sparse multifrontal LU factorization * UFconfig: common configuration for all of the above * CSparse: a concise sparse matrix package * CXSparse: and extended version of CSparse

TestU01 is a software library, implemented in the ANSI C language, and offering a collection of utilities for the empirical statistical testing of uniform random number generators. The library implements several types of random number generators in generic form, as well as many specific generators proposed in the literature or found in widely-used software. It provides general implementations of the classical statistical tests for random number generators, as well as several others proposed in the literature, and some original ones. These tests can be applied to the generators predefined in the library and to user-defined generators. Specific tests suites for either sequences of uniform random numbers in [0,1] or bit sequences are also available. Basic tools for plotting vectors of points produced by generators are provided as well. Additional software permits one to perform systematic studies of the interaction between a specific test and the structure of the point sets produced by a given family of random number generators. That is, for a given kind of test and a given class of random number generators, to determine how large should be the sample size of the test, as a function of the generator's period length, before the generator starts to fail the test systematically.