Math toolkit includes a C++ and a Python library designed to help developers implement finance and statistics functions calculation support to their applications.
Math toolkit enables you to calculate the compound rate, the present or future value, annuities and more. As for the statistics field, it supports the following functions: median, mean, variance, covariance, standard deviation, linear regression.
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Math toolkit offers easy-to-use mathematical functions for calculating standard deviation of a series of values, which is commonly known as variance. Variance measures the statistical dispersion around the mean. Variance is used when analyzing the distribution of data, in such applications as financial analyses, to determine whether or not the data follows a normal (Gaussian) distribution.
Variance is also used by quants to analyze the performance of products and portfolios.
• Calculate the mean, standard deviation (and other statistical operations) using a matrix or table
• Variance calculation support for: simple data, 2D and 3D matrix
• Variance calculations for different data types
• Simple matrix utility functions (convert, normalize, invert, and symmetrize)
• Variance formula for normaldisplayed (C, C++, Python, Java, Fortran)
• Feature-rich, easy-to-understand Math toolkit supports to do statistics of histogram, 1D, 2D and 3D data. You can customize your data using a matrix.
• Parametric calculations (sine, sine y inverse, cosine, cosine y inverse, tangent, hyperbolic tangent)
• Statistical calculations (Bessel, zeta, floor, ceiling)
• Standard deviation of a series of values, (known as variance)
• Determines the dispersion of a set of numbers around their average (the variance depends on the number of data points and their accuracy)
• Variable exponents (power function and logarithmic)
• Parametric functions (erf(x), exp(-x), sin(x), cos(x), sqrt(x) and log(x) )
• Statistics (mean, variance, sigma, std, min, max, percentiles, variance, mean- absolute deviation and others)
• Binary statistics (binomial, multinomial, Poisson)
• Calculate statistical parameters (size of a series, using a matrix)
• Linear regression (linear, log, exponential and polynomial regression)
– Other benefits
• Returns variance value and the level of significance (p value, or confidence level)
• Standard deviation is a combination of a mean and a standard deviation for a series of numbers
• Calculate the confidence interval (80% accuracy)
• Support from 1 to 10,000, and 100,000, and more (100,
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Math toolkit is a package of application programming interface in matlab, to solve any mathematical or statistical modeling problems.
Math toolkit provides a wide range of functions to solve various mathematical and statistical modeling problems, including:
calculate the compound rate, the present or future value, annuities, periodic payments and more.
draw graph to identify the linear regression.
fill line equations of a nonlinear regression model.
compare two or more linear regression models.
calculate the correlation coefficient and the covariance among several variables.
calculate the hyper plane equations.
calculate the multiple regression model.
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Math Toolkit Crack [Latest]
Math toolkit is a free and open source, cross-platform C++ and Python package for financial and statistical calculations.
Math toolkit Features:
Cross-Platform
Math toolkit is written using C++ and Python which can be executed on both Windows and Linux platforms.
C++ and Python API
Math toolkit provides clear and logical C++ and Python APIs, which allows developers to access and use the functionality of the package.
Closed source
The source code of Math toolkit is completely closed and free of charge.
The libraries can be viewed and downloaded from: www.mathtoolkit.info
Math toolkit Blog:
If you’d like to stay up-to-date with the latest news and announcements of the project, check out the Math toolkit blog.
Math toolkit Twitter:
To keep up to date with the latest news from the Math toolkit team, follow us on Twitter.
Math toolkit Gitlab:
To get in touch with the Math toolkit project team, visit the Gitlab project page.
Useful Links:
C++ and Python source code:
The Math toolkit sources can be viewed at:
The Math toolkit project team and community are very excited to announce a new release of Math toolkit. Math toolkit v1.4.6 is a bug fix release which contains more than thirty-four bug fixes and new features. The purpose of the new release is to introduce more bug fixes and improvements to the existing features in previous releases.
1. Math toolkit v1.4.6 was released on June 20th 2018, for Linux, Windows and Mac OS X.
Math toolkit’s library is a collection of powerful functions used to calculate compound interest, annuities, present and future value, mean and variance, regression and more. It has been written entirely in C++ and Python, which make it cross-platform and can be run on Windows, Mac OS X and Linux.
The Math toolkit project team consists of a small team of about a dozen developers with a wide variety of backgrounds ranging from business, finance, statistics, programming, mathematics, academia, law, marketing, investment and more.
Math toolkit features the following functionality:
Compound interest calculation
Calculation of present and future value
Calculation of annuities
Linear and logarithmic regressions.
Median
What’s New in the?
– Calculate the compound rate
– Calculate the present or future value
– Calculate the annuities
– Linear regression
– Median
– Mean
– Variance
– Covariance
– Standard deviation
In this way, Math toolkit helps developers add financial calculation functions to their desktop or mobile application.
## Use Cases:
– Banking systems
– Social networks
– No matter
– Booking services
– Trading websites
– Entertainment
## Requirements:
– C++11 standard or later.
– Python.
Math toolkit
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