BlueSky Statisitcs (ein R-Menü) kaufen
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BlueSky Statistics ist ein voll funktionsfähiges Statistik-Anwendungs- und Entwicklungsframework, das auf dem Open Source R-Projekt basiert:

BlueSky Statistics
- Bietet eine moderne, leistungsstarke Benutzeroberfläche, wie sie auch in den bekannten Statistikprogrammen üblich ist.

- Eröffnet die Leistungsfähigkeit von R für Analysten durch eine intuitive GUI. Umfangreicher Output kann für viele verschiedene gängige statistische Verfahren, Data Mining, Datenmanipulation und Grafikbefehle erstellt werden. Alles ohne Programmierung – auf Knopfdruck!

- Bietet die Bereitstellung eines umfassenden Entwicklungsframeworks für die Entwicklung und Bereitstellung neuer statistischer Module, Anwendungen oder Funktionen mit umfangreichen grafischen Benutzeroberflächen und Ausgaben über intuitive Drag & Drop-Benutzeroberflächen (keine Programmierung erforderlich).

 

BlueSky Statistics unterstützt Sie bei:

  • Migration von teuren statistischen Anwendungen zu R.
  • Erleichtert die R-Lernkurve.
  • Nutzung der in R verfügbaren innovativen Analyseverfahren, ohne Programmieren zu lernen.
  • Ergebnisse automatisch in echten Textverarbeitungstabellen auszugeben.
  • Erstellung Ihrer eigenen Menüs und Dialogfelder für Ihre R-Funktionen.

 

Für anspruchsvolle Benutzer bietet BlueSky Statistics ein umfassendes Framework für die Anwendungsentwicklung, mit dem neue Module oder neue statistische Funktionen mit intuitiven Drag & Drop-Schnittstellen versehen werden können. Mit wenigen Klicks können diese Module in der BlueSky Statistics-Anwendung installiert oder an einen Kollegen verteilt werden. Davon profitiert sowohl den Autor als auch der Anwender.

Unser Ziel ist es, einen Marktplatz zu schaffen, auf dem Benutzer ihre Analysefunktionen und -module effizient teilen können.

 

 

BlueSky Statistics beinhaltet folgende Module:


BlueSky Statistics Application
 

  • Open, browse, edit multiple datasets, create new datasets, add/remove variables, add/remove factor levels, recode, bin.... ALL via the intuitive graphical user interface.
  • Access popular statistics, machine learning, data mining, data manipulation, and exploratory data analysis functions.
  • Access the output of the analysis in a rich graphical user interface that supports interactive tables, copy and paste into Office applications as true tables, and export to popular formats like HTML, PDF.
  • Run R programs directly and access its output.



 

 BlueSky Statistics R Command Editor

Users of the BlueSky Statistics application are not constrained to using the graphical user interface. The syntax editor allows users to:

  • Type in and execute R syntax directly.
  • Run R programs in automated or batch mode.
  • Inspect the R syntax that any of the functions available in BlueSky Statistics applications generate when executed.
  • Learn R programming by allowing you to not only type in R syntax but also inspect the R syntax generated by BlueSky Statistics's menus and dialog boxes.
  • Create, open and save R programs for reuse.




 

BlueSky Statistics Output Viewer

The BlueSky Output Viewer allows you to share the results of your analysis including graphs, tables with your peers, management team or customers who don't have the BlueSky Statistics application. This gives consumers of the analytics the rich interactivity available in the BlueSky Statistics application.


BlueSky Statistics Dialog Designer

The BlueSky Statistics Dialog designer is an application development framework that allows you to create statistical modules or functions with a rich graphical user interface and output for any existing R function in any package or any new R function or package you create.
The BlueSky Statistics Dialog designer allows you to create and save the user interface and output definition for the statistics module or function in a zipped file.

Für weitere Informationen zur Software BlueSky, gehen Sie auf den Link:

https://www.uni-marburg.de/de/fb21/erzwinst/arbeitsbereiche/eb-ajb/ajb/forschung/einfuehrung-in-bluesky-statistics_10_20.pdf

 

BlueSky Anwendung

Öffnen, ansehen, editieren mehrere Datensätze, Erstellen neues Datensätze, hinzufügen und löschen von Variablen, hinzufügen und löschen von Faktorstufen, Recodieren, Klassifizieren.... ALLES mit einem intuitiven graphischen Interface.

  • Zugriff auf viele statistische Verfahren, Machine Learning, Data Mining, Datenmanipulation und explorative Analyse Funkionen.
  • Ein graphisches Interface das Ihnen den Zugriff auf den Output erlaubt, z.B. interaktive Tabellen, kopieren und einfügen in Office als echte Tabellen sowie der Export in Formate wie HTML und PDF.
  • R Programme können direkt gestartet werden und auf deren Ausgabe kann zugegriffen werden.

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BlueSky R Befehls Editor

Users of the BlueSky Statistics application are not constrained to using the graphical user interface. The syntax editor allows users to:

  • Type in and execute R syntax directly.
  • Run R programs in automated or batch mode.
  • Inspect the R syntax that any of the functions available in BlueSky Statistics applications generate when executed.
  • Learn R programming by allowing you to not only type in R syntax but also inspect the R syntax generated by BlueSky Statistics's menus and dialog boxes.
  • Create, open and save R programs for reuse.







BlueSky Output Viewer

The BlueSky Output Viewer allows you to share the results of your analysis including graphs, tables with your peers, management team or customers who don't have the BlueSky Statistics application. This gives consumers of the analytics the rich interactivity available in the BlueSky Statistics application.





BlueSky Dialog Designer

The BlueSky Dialog designer is an application development framework that allows you to create statistical modules or functions with a rich graphical user interface and output for any existing R function in any package or any new R function or package you create.
The BlueSky Dialog designer allows you to create and save the user interface and output definition for the statistics module or function in a zipped file.


 

Minimum system requirements

Minimum functioning specifications

Hardware Requirement Applicable operating system
Disk space Minimum free disk space. 20 GB of available hard-disk space. All supported Windows operating systems
Display A monitor with 1024×768 resolution or higher All supported Windows operating systems
Media drives A DVD-ROM drive is required if you are installing from the installation disk. All supported Windows operating systems
Memory Minimum RAM 4 GB All supported Windows operating systems
Processor Intel® Pentium® or Pentium-class processor or higher (for 32-bit Windows)  x64 (AMD 64 and EM64T) processor family (for 64-bit Windows) All supported Windows operating systems
 

 

Open Source Edition (Free)

Fully featured analytical workbench that provides :

  • An intuitive graphical user interface, attractive interactive output for hundreds of frequently used exploratory analysis, data preparation, visualization, basic and advanced modeling techniques including model scoring.
  • Automatic R syntax generation for hundreds of frequently used exploratory analysis, data preparation, visualization and modeling techniques.
  • R syntax editor that allows you to write and execute R code and see richly formatted output.
  • Save and share output in PDF, HTML.
  • Technical support is available via community forums.
License : AGPL 3.0

 

Commercial Edition

1. BlueSky Statistics Commercial Desktop:
The BlueSky Statistics Commercial Desktop provides all the capabilities of the open source edition plus:
  • Access to priority support, 24 hr response time during business hours
  • Service Level Agreements for delivering application support and hot fixes for critical issues
  • Prioritized bug fixes and feature requests
2. BlueSky Statistics Commercial Server:
The BlueSky Statistics Commercial Desktop provides all the capabilities of the open source edition plus:
  • Support for Citrix and Terminal server
  • Access to priority support, 24 hr response time during business hours
  • Service Level Agreements for delivering application support and hot fixes for critical issues
  • Prioritized feature requests
    Open Source   Commercial Edition
Run on terminal server   X  
Install unlimited dialogs/extensions   X  
Technical support   X  
Enterprise features (Database and customization etc.)   X  

Category

Sub Category

Description

Open Source

Commercial

Data Management

Open Dataset

IBM SPSS (*.sav)

   

Excel 2003

   

Excel 2007-2010

   

Comma separated (*.csv)

   

DBF (*.DBF)

   

SAS (*.sas7bdat)

   

DAT (*.DAT)

   

Txt (*.txt)

         
 

Load Data

From R package

         
 

Database Connectivity

MSSQL

   

PostgreSQL

   

MySQL

   

MS-Access

         
 

Dataset Save formats

IBM SPSS (*.sav)

   

Excel 2007-2010

   

Comma separated (*.CSV)

   

DBF (*.DBF)

   

RObj (*.RData)

         

Data Preparation

 

Fully functional data grid

 

For Variables

Binning

   

Compute

   

Compute, apply a function across all rows

   

Compute Dummy Variables

   

Conditional Compute

   

Conditional Compute, if-then

   

Conditional Compute, if-then-else

   

Concatenate multiple variabels

   

Convert to factors

   

Dates

   

Delete variables

         
   

Factor Levels

   

-- Add New Levels

   

-- Display Levels

   

-- Drop Unused Levels

   

-- Label NA as 'Missing'

   

-- Lumping into 'Other'

   

-- Reorder by Occurence in Dataset

   

-- Reorder by One Other Variable

   

-- Reorder Levels Manually

   

-- Specify levels to keep or replace by 'Other'

         
   

Missing value analysis

   

Rank variables

   

Recode

   

Standardize

   

Transform

   

Weight

 

For Dataset

Aggregate

   

Merge

   

Merge Datasets

   

Re-order variables alphabetically

   

Reshape

   

Sample

   

Sort

   

Stack Datasets

   

Subset

   

Transpose

         

Descriptive Statistics

 

Numerical summary analysis

   

Factor variable analysis

   

Frequencies

   

Summary by variable

   

Summary (group by multiple variables)

   

Numerical statistical analysis

   

Dataset Comparison

   

Dataset Description

         

Analysis

Tables

Basic

   

Advanced

         

Survival Analysis

 

Kaplan-Meier Estimation, compare groups

   

Kaplan-Meier Estimation, one group

         

Distribution, Continuous

Beta Distribution

Beta Probabilities

   

Beta Quantiles

   

Plot Beta Distribution

   

Sample from Beta Distribution

 

Cauchy Distribution

Cauchy Probabilities

   

Cauchy Quantiles

   

Plot Cauchy Distribution

   

Sample from Cauchy Distribution

 

Chi-squared Distribution

Chi-squared Probabilities

   

Chi-squared Quantiles

   

Plot Chi-squared Distribution

   

Sample from Chi-squared Distribution

 

Exponential Distribution

Exponential Probabilities

   

Exponential Quantiles

   

Plot Exponential Quantiles

   

Sample from Exponential Distribution

 

F Distribution

F Probabilities

   

F Quantiles

   

Plot F Distribution

   

Sample from F Distribution

 

Gamma Distribution

Gamma Probabilities

   

Gamma Quantiles

   

Plot Gamma Distribution

   

Sample from Gamma Distribution

 

Gumbel Distribution

Gumbel Probabilities

   

Gumbel Quantiles

   

Plot Gumbel Distribution

   

Sample from Gumbel Distribution

 

Logistic Distribution

Logistic Probabilities

   

Logistic Quantiles

   

Plot Logistic Distribution

   

Sample from Logistic Distribution

 

Lognormal Distribution

Lognormal Probabilities

   

Lognormal Quantiles

   

Plot Lognormal Distribution

   

Sample from Lognormal Distribution

 

Normal Distribution

Normal Probabilities

   

Normal Quantiles

   

Plot Normal Distribution

   

Sample from Normal Distribution

 

t Distribution

t Probabilities

   

t Quantiles

   

Plot t Distribution

   

Sample from t Distribution

 

Uniform Distribution

Uniform Probabilities

   

Uniform Quantiles

   

Plot Uniform Distribution

   

Sample from Uniform Distribution

 

Weibull Distribution

Weibull Probabilities

   

Weibull Quantiles

   

Plot Weibull Distribution

   

Sample from Weibull Distribution

Distribution, Discrete

Binomial Distribution

Binomial Probabilities

   

Binomial Quantiles

   

Binomial Tail Probabilities

   

Plot Binomial Distribution

   

Sample from Binomial Distribution

 

Geometric Distribution

Geometric Probabilities

   

Geometric Quantiles

   

Geometric Tail Probabilities

   

Plot Geometric Distribution

   

Sample from Geometric Distribution

 

Hypergeometric Distribution

Hypergeometric Probabilities

   

Hypergeometric Quantiles

   

Hypergeometric Tail Probabilities

   

Plot Hypergeometric Distribution

   

Sample from Hypergeometric Distribution

 

Negative Binomial Distribution

Negative Binomial Probabilities

   

Negative Binomial Quantiles

   

Negative Binomial Tail Probabilities

   

Plot Negative Binomial Distribution

   

Sample from Negative Binomial Distribution

 

Poisson Distribution

Poisson Probabilities

   

Poisson Quantiles

   

Poisson Tail Probabilities

   

Plot Poisson Distribution

   

Sample from Poisson Distribution

Graphics and Visualizations

 

Bar charts

   

Boxplots

   

Bulls Eye

   

Contour plot

   

Density plots

   

Frequency charts

   

Heatmap

   

Histogram

   

Line charts

   

Maps

   

Pie charts

   

Plot of means

   

P-P plots

   

Q-Q plots

   

Scatterplot

   

Stem and leaf plot

   

Strip chart

   

Violin plot

         

Statistical analysis

 

Correlation test

   

Shapiro-Wilk normality test

         
 

Compare means

T-Test, Independent samples

   

T-Test, One samples

   

T-Test, Paired samples

   

ANCOVA

   

Multi-way ANOVA

   

One-way ANOVA

   

One-way ANOVA with Blocks

   

One-way ANOVA with Random Blocks

Agreement analysis

 

Bland-Altman Plot

   

Cohen's Kappa

   

Concordance Correlation Coefficient

*

   

Concordance Correlation Coefficient, multiple raters

*

   

Diagnostic Testing

   

Fleiss' Kappa

   

Intraclass Correlation Coefficients

Factor analysis

 

Principal component analysis

   

Factor analysis

         

Split datasets for analysis

 

Split

   

Remove split

         

Split datastes for modeling

 

Random split

   

Stratified sampling

         

Contrasts

 

Contrasts Display

   

Contrasts Set