Cloudstat - Cloud Statistical Apps Builder

CloudStat is the cloud environment for building statistical applications and data analysis collaboratively.

1. Data Analysis in the Cloud.
2. Anywhere, anytime, across multiple platforms.
3. No download, installation, upgrades and maintenance.
4. Up to 100 instances with 60.5 Gb RAM each.
5. Enable resourses sharing in private group or public.
6. SSL and AES-256 bit encryption top security standard.

Pricing Plans

(Free for public apps)

Try Here Now:

Result:

No Result

Features

Free Basic

You can use CloudStat for free forever, upgrade if you need more.

Advanced Collaboration

Any number of users can work together simultaneously on data analysis!

Lower Learning Curve

Complete R notes and questions with interactive tutorial videos!

High Security Standard

Your data security is our top priority through secure 256 bit encryption!

High Performance Computing

Analyze your Big Data with High Performance Cloud Infrastructure!

Dedicated Support

Consultant and training in learning R and advanced data analysis!

Cloud Statistical Apps Builder

CloudStat is the cloud environment for building statistical applications and data analysis collaboratively.

Sample Codes

Ω Simple

Ω paixudd

Ω Better way to Generate Random Strong Passwords with R sample function from ASCII Characters

Ω Generating Random Strong Passwords with R sample function from ASCII Characters

Ω ULKs Beispiel

Ω Testbeispiel

Ω 2 to the power of 3 to the power of 4 in R (JerWei Zhang)

Ω Crunchbase API: Scrapping Companies info from Crunchbase with R Packages

Ω TechCrunch Latest 500 Tweets (Real-time)

Ω Step-by-step Cosine function Plot with R

Ω We Feel Fine Analysis

Ω We Feel Fine Analysis

Ω Startup Asia Singapore (Penn Olson) Twitter Sentiment Analysis Result

Ω Quiz Answer for Lesson 1: Overview of R Language

Ω seq

Ω Aggregate Demand with Random Parameters

Ω Random matrix gplots contains the heatmap

Ω Testing the difference in coefficients of two linear regression​s

Ω Normality and the Box–Cox transformation of GCSE score

Ω Normal Q–Q plots of average GCSE score

Ω Histograms of log-transformed forward scatter measurements

Ω Kernel density plot of eruption times of the Old Faithful geyser

Ω Bar Chart: Survival among different subgroups of passengers on the Titanic

Ω Yield of three varieties of oats (A 3 x 4 split−plot experiment with 6 blocks)

Ω Multiway dot plot: Barley experiment, Minnesota

Ω Split-plot experiment: Yield of oats

Ω Bayesian QTL mapping via Linearized Likelihood

Ω Residuals from QTL models

Ω Plots by chromosome location

Ω Get loglikelihoods for many models of a common form

Ω MCMC sampling of multigene models

Ω Create marker map specifications

Ω One and Two Gene Models Using Linearized Posterior

Ω Estimates the marginal parameter modes using kernel density estimation

Ω Multivariate Generalised Linear Mixed Models

Ω Transforms posterior distribution of covariances into correlations

Ω Random Generation from a Truncated Normal Distribution

Ω Posterior distribution of eigenvalues

Ω Beeswarm Boxplot (with ggplot2)

Ω Scatter Dot Beeswarm Box Violin Plot

Ω A tag cloud (word cloud, or weighted list in visual design)

Ω Image scatter plot matrix

Ω Produces an image lag plot matrix of large timeseries

Ω Graphical parameter settings

Ω Graphical low-level parameters

Ω A list of popular names for colors

Ω Correlation matrix ellipses

Ω Correlogram : Correlation Ellipses

Ω Scatterplots with smoothed densities color representation

Ω Visualization of a Correlation Matrix

Ω Two histogram are plotted back to each other

Ω Assignments of a Self Organizing Map, SOM

Ω SOM representatives star plot

Ω Kohonen Self-Organizing Maps

Ω Partitions of a qda classification

Ω Plot a Thermometers graphic with R

Ω Quantile-Quantile plot for precip data against normal distribution

Ω Quick hack to transform histogram into cumulative histogram

Ω Empirical Cumulative Distribution Function for a random sample of size 30 fo the standard normal distribution

Ω Effect displays for generalized linear models

Ω Circular density estimate

Ω Distribution of a categorical variable y changes over a numerical variable x

Ω (Extended) sieve displays for n-way contingency tables

Ω Produces a nice-looking 3D scatterplot Bunny

Ω Spinograms are an extension of histograms

Ω Scatterplot3d: 3D barplot

Ω Meta analysis plot (forest plot)

Ω Density estimation by hexagonal bins

Ω hexbin 2d generalization of boxplot

Ω Rainbow in the 3D RGB space

Ω R Colors in the 3D RGB space

Ω Bivariate Highest density regions (Kernel density)

Ω Highest density regions (kernel method)

Ω Plot variations around the idea of a boxplot

Ω Frequency polygon: density estimator based on histogram

Ω ROC curve with cross validation

Ω the quick brown fox jumps over the lazy dog

Ω Testing the difference in coefficients of two linear regressions