# Histogram(2D) c project and source code

The following C project contains the C source code and C examples used for Histogram(2D). This will generate 2D-Bar chart according to the inputs given by the User.

The following Matlab project contains the source code and Matlab examples used for wilcoxon rank sum test and box plots for genes compounds etc in healthy vs. disease.
Perform a Wilcoxon rank-sum test or Mann-Whitney U-test (non-parametric test).

The following Matlab project contains the source code and Matlab examples used for plots a histogram with a gaussian fit to the data. .
M-Files which plot a histogram with a gaussian fit to the data and write the information to the screen as well as on the command line.

The following Matlab project contains the source code and Matlab examples used for kruskal-wallis' nonparametric analysis of variance. .
Kruskal-Wallis' nonparametric analysis of variance test the difference among groups and it is applicable for equal or unequal sample sizes and if there are tied ranks. This file uses both the chi-squared and F approximations.

The following Matlab project contains the source code and Matlab examples used for fligner-policello non-parametric two-samples robust-rank order test. .
Fligner-Policello is a non-parametric test of two combined random variables with continuous cumulative distribution.

The following Matlab project contains the source code and Matlab examples used for conover's two sample squared ranks test for equality of variance.
Performs the nonparametric squared ranks test on quality of variance for two samples.

The following Matlab project contains the source code and Matlab examples used for kolmogorov distribution functions.
Add to Your statistic toolbox this functions:
KolmCdf - Kolmogorov cumulative distribution function:
F(x)=sum((-1)^n*exp(-2*n^2*x^2),n,-inf,inf);
KolmPdf - Kolmogorov probability distribution function;
KolmInv - Inverse of the Kolmogorov cumulative distribution function.

The following Matlab project contains the source code and Matlab examples used for cochran's q test for margins homogeneity. .
This m-file performs the Cochran's Q test for multi-way tables which each variable has two levels, that is, for 2x2.

The following Matlab project contains the source code and Matlab examples used for this function generates an overlapping histogram of multiple populations. .
This function generates an overlapping histogram of multiple populations.

The following Matlab project contains the source code and Matlab examples used for kernel density estimation of 2 dim with sj bandwidth.
2 dimenstion Kernel density Estimation with Sheater Jones bandwidth for each variable.
Also the Sheater Jones bandwidth selection
method will work for the univariate.

The following Matlab project contains the source code and Matlab examples used for plot a histogram outline .
Plots the outline of a histogram, instead of the usual bar format.
h = histoutline(xx, num, varargin) Plot a histogram outline.
Example:
data = randn(100,1);
histoutline(data, 50);

The following Matlab project contains the source code and Matlab examples used for plot a 'grouped' univariate histogram.
Function HISTG ('grouped histogram') adapts Matlab's HIST to cases where data vector includes values from several observed groups, and lets one color-code group membership on the overall histogram.

The following Matlab project contains the source code and Matlab examples used for plot a univariate histogram.
Function HISTF ('flexible histogram') extends Matlab's HIST by letting one bound displayed percentiles, set x-axis limits and y-axis maximum, set x bin size, and draw a marker line at given x position.

The following Matlab project contains the source code and Matlab examples used for anderson-darling test for assessing normality of a sample data. .
The Anderson-Darling test (Anderson and Darling, 1952) is used to test if a sample of data comes from a specific distribution.

The following Matlab project contains the source code and Matlab examples used for anderson-darling test for assessing weibull distribution of a sample data. .
The Anderson-Darling test (Anderson and Darling, 1952) is used to test if a sample of data comes from a specific distribution.

The following Matlab project contains the source code and Matlab examples used for anderson-darling test for assessing exponential distribution of a sample data. .
The Anderson-Darling test (Anderson and Darling, 1952) is used to test if a sample of data comes from a specific distribution.

The following Matlab project contains the source code and Matlab examples used for cochran q test.
H = COCHRANQTEST(X) performs the non-parametric Cochran's Q-test on the hypothesis that the K columns of N-by-K matrix have the same number of successes and failures.

The following Matlab project contains the source code and Matlab examples used for anderson-darling k-sample procedure to test whether k sampled populations are identical. .
Anderson and Darling (1952, 1954) introduced a goodness-of-fit statistic to test the hypothesis that a random sample comes from a continuous population with a specified distribution function.

The following Matlab project contains the source code and Matlab examples used for chi square test contingency tables.
CHISQUARECONT takes as input a 2x2 matrix that represents a 2x2 contingency table and
calculates the probability of obtaining the observed and each of the more extreme tables
based on the pearson chi square test which is based on the chi square distribution.

The following Matlab project contains the source code and Matlab examples used for update pdf estimation.
Based on the Gaussian kernel density estimation, it is possible to update the PDF estimation upon receiving new data by using the same bandwidth.

The following Matlab project contains the source code and Matlab examples used for multivariant kernel regression and smoothing.
This function implements multivariant Gaussian kernel regression and smoothing.

The following Matlab project contains the source code and Matlab examples used for efficient kernel smoothing regression using kd tree.
Kernel regression is a power full tool for smoothing, image and signal processing, etc.

The following Matlab project contains the source code and Matlab examples used for local linear kernel regression.
This is the local linear version of the kernel smoothing regression function: http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=19195&objectType=FILE
The local linear estimator improves the regression behaviour near the edges of the region over which the data have been collected.

The following Matlab project contains the source code and Matlab examples used for plot cumulative distribution and apply kuiper test. .
plotcdfkuiper(x,a,b,cdf,varargin)
Plot the cumulative probability distribution for a set of variates x between limits a,b and compare with the theoretical cumulative distribution function using the Kuiper test.

The following Matlab project contains the source code and Matlab examples used for calculate number of bins for histogram.
Two files are included:
CALCNBINS, which calculates the "ideal" number of bins to use in a histogram, using three possible methods.

The following Matlab project contains the source code and Matlab examples used for kernel smoothing regression.
Non-parametric regression is widely used in many scientific and engineering areas, such as image processing and pattern recognition.

The following Matlab project contains the source code and Matlab examples used for conditional nonparametric kernel density.
Ref: Sharma A 2000 J. of Hydrology (Part3-nonparametric probablistic forecast model)

The following Matlab project contains the source code and Matlab examples used for kernel density estimation.
% fast and accurate state-of-the-art
% bivariate kernel density estimator
% with diagonal bandwidth matrix.

The following Matlab project contains the source code and Matlab examples used for histogram transformation.
[transformedHistogram,newX]=transformHistogram(originalHistogram,originalX,formula)
transformHistogram transforms the histogram stored in the 1D array 'orignalHistogram' according to the formula 'formula'.

The following Matlab project contains the source code and Matlab examples used for mcnemar's exact test. .
This m-file performs the conditional as well as the Chi-squared corrected for discontinuity McNemar's exact test for two dependent (correlated) samples that can occur in matched-pair studies with a dichotomous (yes-no) response.