Guest Post: Inference for R
Today, I would like to welcome Ben Hinchliffe as a guest blogger on Data Mining Research. Ben is working at Blue Reference, Inc. and has kindly accepted to write a post about a very interesting tool for R: Inference for R.
As noted throughout this blog, the benefits of R are extensive; owing to its overall extensibility, ease of code development, a large and growing library of statistical software packages, and it’s rapidly increasing use in technical communities. Thus, it is not surprising R is quickly becoming a standard in both academia and industry. In this regard, I am excited to share a new application for R users, called Inference for R.
Inference for R is the first integrated development environment (IDE) designed specifically for R. It helps to reduce time spent on editing R code, managing data, and publishing reports. Inference for R also reduces the effort required to piece together data-analysis parts into a cohesive solution, which in turn reduces the time needed to learn R.
An intelligent editor and debugging environment provides instant feedback on syntax and design errors while code is being written.
Today, I would like to welcome Ben Hinchliffe as a guest blogger on Data Mining Research. Ben is working at Blue Reference, Inc. and has kindly accepted to write a post about a very interesting tool for R: Inference for R.
As noted throughout this blog, the benefits of R are extensive; owing to its overall extensibility, ease of code development, a large and growing library of statistical software packages, and it’s rapidly increasing use in technical communities. Thus, it is not surprising R is quickly becoming a standard in both academia and industry. In this regard, I am excited to share a new application for R users, called Inference for R.
Inference for R is the first integrated development environment (IDE) designed specifically for R. It helps to reduce time spent on editing R code, managing data, and publishing reports. Inference for R also reduces the effort required to piece together data-analysis parts into a cohesive solution, which in turn reduces the time needed to learn R.
An intelligent editor and debugging environment provides instant feedback on syntax and design errors while code is being written.
For publishing, Inference for R converts static Microsoft Word documents, into dynamic documents containing your data, R objects, R code, and text annotations (commentary).
When executed, Inference runs your R code and generates a Results Document that contains the textual, numerical and graphical output of your scripting commands, in addition to formatted text annotations.
With Inference, you can write scripts in R to automate repetitive tasks involving data access, collection, preparation, analysis and reporting with one-button convenience. Similar functionality is available for Excel, and is in the works for PowerPoint.
As an example, Inference enables you to turn an Excel or Word document into a simple GUI for your R application. This allows non-technical users, with only MS Office knowledge, to easily analyze your data and present your data mining results, without any proficiency in R. At the end of the day, any user of Excel and Word can now access the power of R!
The applicability of Inference for R is vast, providing scientists, software programmers, and non-technical MS Office users a time-saving and simple application for reducing and analyzing data in a reproducible fashion, and generating professional quality technical documents. View a three minute overview and download a free trial at http://www.InferenceForR.com.
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