Open Source Statistics Software - Page 14
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runjags
The 'runjags' R package and standalone JAGS extension module
This package provides high-level interface utilities for MCMC models via Just Another Gibbs Sampler (JAGS), facilitating the use of parallel (or distributed) processors for multiple chains, automated control of convergence and sample length diagnostics, and evaluation of the performance of a model using drop-k validation or against simulated data. Template model specifications can be generated using a standard lme4-style formula interface to assist users less familiar with the BUGS syntax. A JAGS extension module provides additional distributions including the Pareto family of distributions, the DuMouchel prior and the half-Cauchy prior. -
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seaborn
Statistical data visualization in Python
Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Seaborn helps you explore and understand your data. Its plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots. Its dataset-oriented, declarative API lets you focus on what the different elements of your plots mean, rather than on the details of how to draw them. Behind the scenes, seaborn uses matplotlib to draw its plots. For interactive work, it’s recommended to use a Jupyter/IPython interface in matplotlib mode, or else you’ll have to call matplotlib.pyplot.show() when you want to see the plot. -
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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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sequoia-dap
SEQUOIA ocean data assimilation platform (a SIROCCO suite tool)
Within the SIROCCO suite of numerical tools, the purpose of SDAP is to provide a flexible platform to carry out multivariate assimilation of geophysical data in a numerical model. The program is multi-grid (finite differences or finite elements), multi-algebra (plug-in analysis kernels), multi-model (simple standardized interface). The program supports reduced-order data assimilation methods, as well as Ensemble assimilation approaches such as the Ensemble Kalman Filter. Recent additions include extensions towards simultaneous assimilation and downscaling (AMICO project), and a toolbox for ensemble assessment (SCRUM project). *Please note that due to frequent updates the code is only available via the svn repository. Please get in touch with us to obtain access.* -
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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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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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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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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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statTools
Command-line tools for simple statistics
statTools contains command-line tools for simple statistics -
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Statistical models with python using numpy and scipy. Currently covers linear regression (with ordinary, generalized and weighted least squares), robust linear regression, and generalized linear model, discrete models, time series analysis and other statistical methods.
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statsmodels
Statsmodels, statistical modeling and econometrics in Python
statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. An extensive list of result statistics are available for each estimator. The results are tested against existing statistical packages to ensure that they are correct. The package is released under the open source Modified BSD (3-clause) license. Generalized linear models with support for all of the one-parameter exponential family distributions. Markov switching models (MSAR), also known as Hidden Markov Models (HMM). Vector autoregressive models, VAR and structural VAR. Vector error correction model, VECM. Robust linear models with support for several M-estimators. statsmodels supports specifying models using R-style formulas and pandas DataFrames. -
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statspy
Python module for statistics built on top of NumPy/SciPy
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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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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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The TreeRank project is a R package implementing a Machine Learning algorithm to build tree-based ranking rules from data with binary labels, based on ROC optimization.
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wavg
Calculator of weighted averages.
A simple program to calculate weighted averages. It is developed in C++ using Qt. So far I tried to compile it only on Linux. It can also generate a graph of the data and the average, using gnuplot. -
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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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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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x2x (xbit2xbyte\xbyte2xbit)
Converts xbits to xbytes, and back again if needed.
xbit2xbyte and xbyte2xbit are designed as sample programs for programmers new to C# as well as a learning tool for the author. These two programs also fulfilled a genuine need to convert to xbits (megabit usually) to xbytes (megabytes, again usually) when dealing with things such as cartridge sizes ,which are generally expressed in megabits, and something the author frequently encountered. Thus, xbit can be used to convert whatever bit (mega, giga, etc.) to whatever byte (again, mega, giga, etc.), and the sister program can be used to convert the opposite way. -
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Charting tools, backtesting tools, and data visualization tools for the most popular cryptocurrencies.
