# Doubly truncated data analysis

The following source code and source code examples are about Doubly truncated data analysis that implements different algorithms for analyzing randomly truncated data.

The following source code and examples are a collection of utilities for analyzing censored and uncensored data from generalized Birnbaum-Saunders distributions.

The following source code and examples are used for implementing several generalized F-statistics.

The following package and source code is used for Fractal Image Data Generator that generates image data for fractalson the complex plane in the given region and resolution.

The following package and source code implement Kolmogorov-Zurbenko Fourier transform based periodograms and smoothing methods.

The following package and source code are Skewed Student-t Distribution, which can generate Density, distribution function, quantile function and random generation.

The following R package and source code is used for General discrete triangular distribution.

The following R package and source code is used Function estimation via Unbalanced Haar wavelets. Top-down and bottom-up algorithms are used for nonparametric function estimation in Gaussian noise.

The following R package and R code is about Wavelet change point detection for array CGH data.

The following R package and R code is about wavelet routines that calculate single sets of wavelet multiple correlations and cross-correlations out of n variables.

Piecewise Linear Estimation is a very important denoising algorithm.The following source code focus on its implementation and show its performance by comparing it with several other acclaimed algorithms.

This package and source code is about random generation and parameter estimation for the discrete inverse Weibull distribution

This package implements a method for normalization, testing, and false discovery rate estimation for RNA-sequencing data

extreme value analysis (EVA) is a branch of statistics dealing with the extreme deviations from the median of probability distributions.This package is used for doing extreme value analysis.

Canonical correlation analysis is a popular statistical method for the study of the correlations between two sets of variables. This package implements the functions associated with Fast Regularized Canonical Correlation Analysis.

The following source code and r examples are collection of functions for fitting distributions to given data or by known quantiles.

The following are Spectral Analysis for Physical Applications.

The following are Wavelet Methods for Time Series Analysis.