So once you’re learned one, it will be easier to learn the next one.Ģ. There are many similarities in the logic and wording they use even if the interface is different. The first one you learn is the hardest to learn. I think I’ve used at least a dozen different statistics packages since my first stats class. Many times the stat package used in a class is chosen for its shallow learning curve, not its ability to handle advanced analyses that are encountered in research. The default is to use whatever software they used in your statistics class–at least you know the basics.Īnd this might turn out pretty well, but chances are it will fail you at some point. These two commercial programs are used for more numerically-oriented problems.In addition to the five listed in this title, there are quite a few other options, so how do you choose which statistical software to use?

An alternative to BUGS is JAGS ('Just Another Gibbs Sampler'). Packages exist that integrate the functionality in R with BRugs and R2OpenBUGS. While OpenBUGS was developed from the commercial software WinBUGS, the efforts for further development are now concentrated on OpenBUGS. WinBUGS, and the related open source project OpenBUGS, are tools especially for Bayesian statistics. Alternatives to Amos include LISREL, Mplus and SmartPLS (for partial least-squares). A less powerful alternative for time series analysis is the free software JMulTi, which is implemented in JAVA.Ī relatively easy to use program for modeling and estimating structural equation models.

It is primarily used in university economics departments, and economic research institutes.

High bar of entry due to being a “full” programming languageĮViews is particularly established in econometrics, and focuses on working with time series data.

Some development environments for statistics are still in their infancy.

Not all statistical methods are available.

Future-proof due to continued growth in use in scientific and commercial fields.

Professional development environments are available.

Visualizations are appealing and easy to create.

Extremely good community support from a large and constantly-growing community.

Fully integratable (Git, teradata, PySpark, Hadoop, KNIME).

Full availability of the latest Deep Learning and Machine Learning methods.

Constant development by a large developer community.A large number of stable packages in the data science sector and beyond.Mature programming language, resulting in unit tests and debugging functionalities, for example.Offers the potential for object-oriented, structured, and functional concepts.Powerful, fully-functional programming language.

Sas jmp vs spss