Statistical Functions
- mathjs:
math.js offers an extensive set of mathematical functions, including statistical functions such as mean, median, mode, variance, and standard deviation, along with advanced mathematical capabilities like calculus and algebraic operations.
- stats.js:
stats.js does not provide statistical functions but focuses on performance metrics, offering real-time statistics about frame rates and memory usage in web applications.
- jstat:
jStat provides a variety of statistical functions, including descriptive statistics, probability distributions, and hypothesis testing. It supports common distributions like normal, binomial, and Poisson, making it suitable for basic statistical analysis.
- simple-statistics:
simple-statistics includes essential statistical functions like mean, median, mode, variance, and regression analysis. It is designed for simplicity and ease of use, making it ideal for quick statistical calculations.
- numeric:
numeric.js focuses on numerical methods and linear algebra, providing functions for matrix operations, eigenvalues, and numerical integration. It is designed for applications that require high-performance numerical computations.
Complexity and Size
- mathjs:
math.js is a more extensive library, which may introduce additional complexity due to its wide range of features. It is suitable for applications that require advanced mathematical capabilities but may be overkill for simple tasks.
- stats.js:
stats.js is extremely lightweight and focused solely on performance monitoring, making it easy to include in any project without impacting overall size.
- jstat:
jStat is lightweight and easy to integrate into projects without significant overhead. It is designed for users who need basic statistical functions without the complexity of larger libraries.
- simple-statistics:
simple-statistics is designed to be minimalistic and easy to use, making it a good choice for developers who want a straightforward library for basic statistical tasks without unnecessary complexity.
- numeric:
numeric.js is focused on numerical analysis and is relatively lightweight compared to other comprehensive libraries. It provides a good balance between functionality and performance for numerical computations.
Learning Curve
- mathjs:
math.js has a steeper learning curve due to its extensive capabilities and syntax. However, it provides comprehensive documentation that can help users navigate its features effectively.
- stats.js:
stats.js is very easy to use, with a simple API focused on performance monitoring. Developers can quickly integrate it into their applications without a steep learning curve.
- jstat:
jStat has a gentle learning curve, making it accessible for developers who are new to statistics. Its straightforward API allows users to quickly implement statistical functions without extensive background knowledge.
- simple-statistics:
simple-statistics is designed for ease of use, with a simple API that allows developers to perform statistical calculations quickly. It is an excellent choice for beginners or those needing quick results.
- numeric:
numeric.js is relatively easy to learn for those familiar with numerical methods and linear algebra. Its API is straightforward, but users may need some background in numerical analysis to fully utilize its capabilities.
Performance
- mathjs:
math.js is versatile but may have performance trade-offs due to its extensive feature set. It is optimized for a wide range of mathematical operations, but users should be mindful of potential performance impacts in highly demanding applications.
- stats.js:
stats.js is optimized for real-time performance monitoring, providing minimal overhead while delivering accurate statistics about application performance.
- jstat:
jStat is optimized for performance in statistical calculations, making it suitable for applications that require efficient processing of statistical data without significant overhead.
- simple-statistics:
simple-statistics is efficient for basic statistical calculations, making it suitable for applications that require quick and simple analysis without the need for complex computations.
- numeric:
numeric.js is designed for high-performance numerical computations, particularly in linear algebra. It is optimized for speed and efficiency, making it suitable for scientific computing and applications that require intensive numerical analysis.
Use Cases
- mathjs:
math.js is suitable for applications that require extensive mathematical computations, such as scientific simulations, engineering calculations, and data analysis. It is versatile enough to handle a wide range of mathematical tasks.
- stats.js:
stats.js is specifically designed for performance monitoring in web applications, making it ideal for developers who want to optimize their applications' performance and ensure smooth user experiences.
- jstat:
jStat is ideal for projects that require basic statistical analysis, such as data visualization, A/B testing, and simple modeling applications. It is particularly useful for developers who need quick access to statistical functions.
- simple-statistics:
simple-statistics is perfect for projects that require quick statistical calculations, such as data reporting, analytics dashboards, and educational tools. It is designed for users who need straightforward statistical functions without complexity.
- numeric:
numeric.js is best for applications focused on numerical analysis, such as simulations, optimization problems, and scientific computing. It is particularly useful in fields like physics and engineering.