ARCH models in Python

A library of statistical distribution sampling and transducing functions

Display and analyze ROC curves in R and S+

MSGARCH R Package

Leibniz equivalence and Liskov substitutability library for Scala.

Computationally efficient confidence intervals for cross-validated AUC estimates in R

statistics module used for the TCI

Sensor for Home Assistant that gets reset at midnight

Analysis of Variance (ANOVA)

Micro Kalman Filter for linear and nonlinear systems

Online statistics implementations, including average, variance and standard deviation; exponentially weighted versions as well.

Php library for red envelope/packet (红包) what is popular among Chinese.

Variance Estimation for Sample Surveys by the Ultimate Cluster Method

Satellite library(fft, variogram,...)

Udacity Machine Learning Course Predicting Boston Housing Prices

Detect Deblurness of image for Openframeworks.

Python implementation of the Nearest Subclass Classifier (NSC), based on Maximum Variance Clusters (MVC).

Compute mean and variance of the univariate truncated normal distribution (works far from the peak).

Perform Levene's statistical test for equality of group variances.

Retrieving, Processing, and Visualizing Data with Python

Select, weight and analyze complex sample data

R codes for Impact of Sampling Rate in Connectivity Analysis (Human Brain Mapping)

Advanced Adaptive Spatial Local Filter

Python crawler that helps fetch temperature of Beijing, China

Stochastic tools, distrubution, analysis

is the degree of variation of a trading price series over time

A repo holding the implementation as well as some theoretical explanation of the important relevant concepts. It is going to be in development for a long long time. I'll keep adding things everytime I have something to add to it, and I have the time for it. One can use it to learn the basics of Machine Learning from kind of scratch.

A small C library for computation of U-statistics

One pass (O(N)) descriptive statistics including variance, means, min & max and more. Can be calculated on-the-fly.

Deep Learning model training visualized using mutual information between the input and the output