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  • 1

    segment

    Solve the Viterbi algorithm in a data stream

    It is often necessary to assign a series of discrete values to continuosly variable data sequenced by time, position, etc., thereby parsing the data into fewer and larger segments of variable width. The 'segment' utility takes an input data stream as a Hidden Markov Model and applies the Viterbi algorithm to find the most likely segmentation path through the data.
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  • 2

    slycat

    Web-based data science analysis and visualization platform.

    This is Slycat - a web-based data science analysis and visualization platform, created at Sandia National Laboratories. The goal of the Slycat project is to develop processes, tools and techniques to support data science, particularly analysis of large, high-dimensional data.
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  • 3
    A Matlab toolbox for interfacing with the pure JAVA numerical library Snifflib. This toolbox provides convenience m-files for interoperability with Snifflib from within an active Matlab session running a JAVA virtual machine.
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  • 4
    snlanalytic is a small Python script that takes a stem-and-leaf plot as input and returns basic statistics (sum, mean, median, mode) to the user.
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    Let your volunteer coordinators do their best work.

    For non-profit organizations requiring a software solution to keep track of volunteers

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  • 5

    spectralHMM

    A spectral method for inferring selection from time series data

    ***WARNING*** This software was migrated to: https://github.com/popgenmethods/spectralHMM Support and updates will only be available at this new address. This software implements the algorithms described in the following paper: Steinrücken, M., Bhaskar, A. and Song, Y.S. A novel spectral method for inferring general diploid selection from time series genetic data. Annals of Applied Statistics, Vol. 8, No. 4 (2014) 2203-2222
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  • 6
    statTools

    statTools

    Command-line tools for simple statistics

    statTools contains command-line tools for simple statistics
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  • 7
    statistics software
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  • 8

    statspy

    Python module for statistics built on top of NumPy/SciPy

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  • 9
    stkpp

    stkpp

    C++ Statistical ToolKit

    STK++ (http://www.stkpp.org) is a versatile, fast, reliable and elegant collection of C++ classes for statistics, clustering, linear algebra, arrays (with an Eigen-like API), regression, dimension reduction, etc. Some functionalities provided by the library are available in the R environment as R functions (http://cran.at.r-project.org/web/packages/rtkore/index.html). At a convenience, we propose the source packages on sourceforge. The library offers a dense set of (mostly) template classes in C++ and is suitable for projects ranging from small one-off projects to complete data mining application suites.
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  • 10

    tcsi

    Stata command for evaluating seasonality

    tcsi is a Stata command for evaluating seasonality according to the transportation cost approach by G. L. Lo Magno, M. Ferrante and S. De Cantis.
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  • 11
    Measurement uncertainties with Python.
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  • 12
    This project aims to provide open source software implementing the Wedge algorithm for the estimation of parameters in dynamical systems models. This project also seeks to create tutorials and resources for those using Wedge.
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  • 13
    Worst Cases is a python script that enables calculations on worst case objects. Like a calculator operating on worst cases (e.g. [1,2,3]) rather than on real numbers (2.35) WARNING: This program is released as is, and has never been extensively teste
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  • 14

    GENIE (GEne-geNe IntEraction)

    GPU based Parallel Gene-Gene Interaction Analysis

    Gene-gene interaction in genetic association studies is computationally intensive when a large number of SNPs are involved. Most of the latest Central Processing Units (CPUs) have multiple cores, whereas Graphics Processing Units (GPUs) also have hundreds of cores and have been recently used to implement faster scientific software. However, currently there are no genetic analysis software packages that allow users to fully utilize the computing power of these multi-core devices for genetic interaction analysis for binary traits. Here we present a novel software package GENIE, which utilizes the power of multiple GPU or CPU processor cores to parallelize the interaction analysis. Citation: Chikkagoudar, S., Wang, K., & Li, M. (2011). GENIE: a software package for gene-gene interaction analysis in genetic association studies using multiple GPU or CPU cores. BMC research notes, 4(1), 158.
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