Moscow ML vs R
Side-by-side comparison for macOS
Moscow ML
6.0Light-weight implementation of Standard ML
R
8.0Environment for statistical computing and graphics
| Metric | Moscow ML | R |
|---|---|---|
| Category | Developer Tools | Developer Tools |
| AI Score | 6.0 | 8.0 |
| 30-day Installs | - | 1.1K |
| 90-day Installs | 1 | 3.0K |
| 365-day Installs | 11 | 11.8K |
| Version | 2.10.1 | 4.6.0,sonoma |
| Auto-updates | No | No |
| Deprecated | Yes | No |
| GitHub Stars | 361 | — |
| GitHub Forks | 43 | — |
| Open Issues | 49 | — |
| License | — | — |
| Language | Standard ML | — |
| Last GitHub Commit | 2y ago | — |
| First Seen | Aug 9, 2023 | Mar 2, 2019 |
Reviews
Moscow ML
Moscow ML is a lightweight implementation of Standard ML, ideal for teaching and research in functional programming. It offers a compact environment for SML development but lacks auto-updates and has limited recent community discussion.
Moscow ML provides an implementation of Standard ML, a strict functional programming language.
Pros
- + Lightweight and efficient for SML development
- + Suitable for educational and research purposes
- + Open-source with a focus on functional programming
Cons
- - No auto-update feature
- - Limited recent community engagement
R
R is a powerful environment for statistical computing and graphics, widely used in academia and data science. It offers extensive packages and tools for data analysis, visualization, and machine learning, making it indispensable for researchers and statisticians.
R provides an environment for statistical analysis, data visualization, and programming.
Pros
- + Extensive statistical and graphical capabilities
- + Large ecosystem of packages and tools
- + Active and supportive community
Cons
- - Steep learning curve for beginners
- - Some enterprise features require additional tools