Statsar product features
Statsar statistics library provides high-performance .NET
classes for calculating statistics, and analyzing
numerical data. The library has been designed to be as intuitive as possible,
and as is organized around the following product features:
Library design
- Rapid integration: start developing with as little as three lines of code.
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An intuitive and easy to use object model reflecting the underlying numerical
algorithms.
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Organized according to Microsoft .NET platform library design,
following best practices.
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Efficiency at all levels by implementing the lowest order numerical algorithms,
and employing propriety optimization in .NET.
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Professionally designed software, including a complete user guide documenting all
library features, a reference manual and over 25 examples in C# and VB.NET.
Data analysis
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Data manipulation classes including data sheet objects, used to efficiently
store and access numerical data for statistics calculations.
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Import data from virtually any data source including standard ADO.NET data tables,
arrays and lists. Binds to your own custom objects via reflection.
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Includes a robust and efficient CSV reader, ideal for importing data from Microsoft
Excel or for quickly processing high volumes of legacy data.
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Multiple and extensible data filters are included, providing a powerful way to
remove unwanted or missing values.
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Efficiently sort, reorder, permute, insert or remove data according to complex
criteria.
Descriptive statistics
- Compute the count, sum, min or max of an entire data set, or on filtered data.
- Measures of central tendency including mean, median, harmonic mean and geometric mean.
- Variance, standard deviation, and absolute deviation from the mean and median.
- Ranks, percentiles and interquartile range.
- Central moments: skew and kurtosis.
Random number generators
- High-quality random generators using standard .NET methods.
- Mersenne twister pseudorandom numbers.
- Linear congruential generator (LCG).
- Linear feedback shift register.
Probability distributions
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Consistent interface across all distributions by deriving from a common base class.
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Probability density function (PDF), cumulative distribution function (CDF)
and inverse cumulative distribution function (inverse CDF).
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Methods to compute statistics including the mean, variance, skew and kurtosis.
- Random number generation according to multiple probability distributions.
Discrete distributions
- Bernoulli distribution.
- Binomial distribution.
- Discrete uniform distribution.
- Geometric distribution.
- Hypergeometric distribution.
- Negative binomial distribution.
- Poisson distribution.
Continuous distributions
- Beta distribution.
- Cauchy distribution.
- Chi-squared distribution.
- Continuous uniform distribution.
- Erlang distribution.
- Exponential distribution.
- F distribution.
- Gamma distribution.
- Gumbel distribution.
- Laplace distribution.
- Logistic distribution.
- Lognormal distribution.
- Normal distribution.
- Pareto distribution.
- Rayleigh distribution.
- Student's T distribution.
- Triangular distribution.
- Weibull distribution.
Linear regression
- Multiple linear regression and regression analysis.
- Least squares minimization, optimized using efficient matrix techniques.
- Polynomial linear regression.
- Confidence measures and intervals.
Hypothesis tests
- Critical values and confidence measures.
- One sample and two sample testing.
- Z-test and T-test hypothesis testing including p-values.
- Two variance F-test.
- Chi-square test.
- Kolmogorov-Smirnov test.
- Anderson-Darling test.
- Bartlett's test.
- Levene's test.
Analysis of variance
- Analysis of variance (ANOVA).
- Analysis of variance with repeated measures (RANOVA).
- One way and two way testing.
More information...