Lumenize - Illuminating the forest AND the trees in your data

  •        48

Copyright (c) 2009-2013, Lawrence S. Maccherone, Jr. Illuminating the forest AND the trees in your data.


tztime : ^1.0.1



Related Projects

SpagoBI - Business Intelligence Suite

  •    Java

SpagoBI is the only entirely open source Business Intelligence suite. It covers all the analytical areas of Business Intelligence projects, with innovative themes and engines. SpagoBI offers a wide range of entirely open source analytical tools like Reporting, OLAP, Chart, Data mining, Real-time monitoring console, ETL.

ReportServer - The Business Intelligence Suite

  •    Java

ReportServer is a modern and versatile business intelligence platform. It supports Pixel-perfect reporting, Ad-hoc analyses, Excel and Word reporting, Integrates with Jasper, Eclipse Birt and Crystal report, Multidimensional OLAP analytics also integrates with Mondrian OLAP, an OLAP server written entirely in Java and optimized for performance even on large datasets. ReportServer comes with a great selection of powerful tools.

mining - Business Intelligence (BI) in Python, OLAP

  •    Python

If you use Mac OSX you can install all dependencies using HomeBrew. For example, to connect to a PostgreSQL database make sure you install a driver like psycopg2. OpenMining supports all databases that the underlying ORM SQLAlchemy supports.

Business Intelligence LAB


Business Intelligence LAB is a Microsoft Business Intelligence Framework that is developped based on real life needs. This repository hosts a set of development done upon SQL Server Business Intelligence Stack. This covers : - Data Access - Sample Cube - Sample OLAP Viewer - ...

classifier-reborn - A general classifier module to allow Bayesian and other types of classifications

  •    Ruby

Classifier Reborn is a general classifier module to allow Bayesian and other types of classifications. It is a fork of cardmagic/classifier under more active development. Currently, it has Bayesian Classifier and Latent Semantic Indexer (LSI) implemented. Here is a quick illustration of the Bayesian classifier.


  •    Java

Pentaho is the open source business intelligence leader. Thousands of organizations globally depend on Pentaho to make faster and better business decisions that positively impact their bottom lines. Download the Pentaho BI Suite today if you want to speed your BI development, deploy on-premise or in the cloud or cut BI licensing costs by up to 90%.

Bayesian-Modelling-in-Python - A python tutorial on bayesian modeling techniques (PyMC3)

  •    Jupyter

Welcome to "Bayesian Modelling in Python" - a tutorial for those interested in learning how to apply bayesian modelling techniques in python (PYMC3). This tutorial doesn't aim to be a bayesian statistics tutorial - but rather a programming cookbook for those who understand the fundamental of bayesian statistics and want to learn how to build bayesian models using python. The tutorial sections and topics can be seen below. Statistics is a topic that never resonated with me throughout university. The frequentist techniques that we were taught (p-values etc) felt contrived and ultimately I turned my back on statistics as a topic that I wasn't interested in.

XChart - Light weight Java library for plotting data

  •    Java

XChart is a light-weight and convenient library for plotting data designed to go from data to chart in the least amount of time possible and to take the guess-work out of customizing the chart style.

Probabilistic-Programming-and-Bayesian-Methods-for-Hackers - aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view

  •    Jupyter

The Bayesian method is the natural approach to inference, yet it is hidden from readers behind chapters of slow, mathematical analysis. The typical text on Bayesian inference involves two to three chapters on probability theory, then enters what Bayesian inference is. Unfortunately, due to mathematical intractability of most Bayesian models, the reader is only shown simple, artificial examples. This can leave the user with a so-what feeling about Bayesian inference. In fact, this was the author's own prior opinion. After some recent success of Bayesian methods in machine-learning competitions, I decided to investigate the subject again. Even with my mathematical background, it took me three straight-days of reading examples and trying to put the pieces together to understand the methods. There was simply not enough literature bridging theory to practice. The problem with my misunderstanding was the disconnect between Bayesian mathematics and probabilistic programming. That being said, I suffered then so the reader would not have to now. This book attempts to bridge the gap.

RapidMiner -- Data Mining, ETL, OLAP, BI

  •    Java

No 1 in Business Analytics: Data Mining, Predictive Analytics, ETL, Reporting, Dashboards in One Tool. 1000+ methods: data mining, business intelligence, ETL, data mining, data analysis + Weka + R, forecasting, visualization, business intelligence

Accord.NET - Machine learning, Computer vision, Statistics and general scientific computing for .NET

  •    CSharp

The Accord.NET project provides machine learning, statistics, artificial intelligence, computer vision and image processing methods to .NET. It can be used on Microsoft Windows, Xamarin, Unity3D, Windows Store applications, Linux or mobile.

brms - brms R package for Bayesian generalized multivariate non-linear multilevel models using Stan

  •    R

The brms package provides an interface to fit Bayesian generalized (non-)linear multivariate multilevel models using Stan, which is a C++ package for performing full Bayesian inference (see The formula syntax is very similar to that of the package lme4 to provide a familiar and simple interface for performing regression analyses. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include non-linear and smooth terms, auto-correlation structures, censored data, missing value imputation, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Multivariate models (i.e. models with multiple response variables) can be fitted, as well. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with posterior predictive checks, leave-one-out cross-validation, and Bayes factors. As a simple example, we use poisson regression to model the seizure counts in epileptic patients to investigate whether the treatment (represented by variable Trt) can reduce the seizure counts and whether the effect of the treatment varies with the baseline number of seizures a person had before treatment (variable log_Base4_c). As we have multiple observations per person, a group-level intercept is incorporated to account for the resulting dependency in the data.

classifier - [UNMAINTAINED] Bayesian classifier with Redis backend

  •    Javascript

Deprecation notice: This library is no longer actively maintained. Try the natural classifier. It doesn't have a Redis backend, but otherwise works even better. The first argument to train() can be a string of text or an array of words, the second argument can be any category name you want.

classifier - A general classifier module to allow Bayesian and other types of classifications.

  •    Ruby

Classifier is a general module to allow Bayesian and other types of classifications. Notice that LSI will work without these libraries, but as soon as they are installed, Classifier will make use of them. No configuration changes are needed, we like to keep things ridiculously easy for you.

stan - Stan development repository (home page is linked below)

  •    R

There are interfaces available in R, Python, MATLAB, Julia, Stata, Mathematica, and for the command line. There are separate repositories in the stan-dev GitHub organization for the interfaces, higher-level libraries and lower-level libraries.

classifier - A general classifier module to allow Bayesian and other types of classifications.

  •    Ruby

A general classifier module to allow Bayesian and other types of classifications.

ThinkBayes2 - Text and code for the second edition of Think Bayes, by Allen Downey.

  •    Jupyter

Think Bayes is an introduction to Bayesian statistics using computational methods. This is the repository for the second edition. The premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics.


  •    C++

Bayes++ is a library of C++ classes that implement numerical algorithms for Bayesian Filtering. They provide tested and consistent numerical methods and the class hierarchy represents the wide variety of Bayesian filtering algorithms and system model