Science is the effort of seeking to comprehend how the physical world works. From observation and experimentation, science uses physical evidence of natural phenomena to compile data and analyze the collated information.
In modern research it is essential for scientists to keep abreast of the latest statistical software. Just like the fast moving world of research, developments in statistical software and methods continue to abound. Making full use of the improvements in computer software helps to advance the pace of research.
Science really prospers and advances when individuals share the results of their experiments with others in the scientific community. There is a certain logic that scientific software should therefore be released in a freely distributable environment.
Linux is particularly strong in the field of open source statistical software. The purpose of this article is to identify software for performing statistical analysis. This type of software helps to summarize data in a shorter form, and helps scientists understand a concept or representation and make possible predictions based on this understanding.
Here’s our verdict captured in a legendary LinuxLinks chart. Only free and open source software is included.
We give our strongest recommendations to R, an open source programming language and software environment for statistical computing and graphics, and RStudio. It consists of a language together with a run-time environment with a debugger, graphics, access to system functions, and scripting. If you are looking for a really easy-to-use package, look no further than PSPP and SOFA Statistics. Their presentation are examples to developers of how to design and implement intuitive software.
Let’s explore the 10 statistical analysis tools at hand. For each title we have compiled its own portal page, a full description with an in-depth analysis of its features, a screenshot of the software in action, together with links to relevant resources.
| Statistical Analysis Tools | |
|---|---|
| RStudio | Professional software for R with code editor, debugging & visualization tools |
| R | Environment for statistical computing and graphics |
| statsmodels | Statistical modeling and econometrics in Python |
| gretl | Regression, Econometric and Time-Series Library |
| ROOT | Solves the data analysis challenges of high-energy physics |
| SOFA Statistics | Extremely user-friendly statistics, analysis and reporting package |
| PSPP | Free replacement of the proprietary program, SPSS |
| JASP | Statistical package for both Bayesian and Frequentist statistical methods |
| Pingouin | Simple yet comprehensive functions for commonly used statistical tests |
| jamovi | Real-time statistical spreadsheet |
This article has been updated to reflect the changes outlined in our recent announcement.
If you are looking for other graphical interfaces for R, check out this roundup.
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