Python library for Numerical Analysis with Maple like output

Our team are the developer of the Stemlab Python library. This library is designed to deliver high-precision computations with output similar to Maple, making it ideal for advanced mathematical modeling and problem-solving. It provides users with an intuitive interface for performing complex numerical analysis, while generating detailed, Maple-like symbolic results. The library has the following submodules

• Mathematics: Offers tools for algebra, calculus, geometry, and so forth.
• Statistics: Provides robust functions for data analysis, probability distributions, and statistical inference, ideal for handling statistical datasets.
• Physics: Facilitates the simulation and analysis of physical systems, covering mechanics, electromagnetism, thermodynamics, e.t.c
• Chemistry: Supports chemical modeling, reaction kinetics, and molecular structure analysis.
• Biology: Includes tools for bioinformatics, population modeling, and genetic algorithms.
• Graphics: Has functions for simple and straightforward plotting using the Matplotlib library.

R dta library for Data Wrangling

An R Library for Efficient Data Management and Manipulation. This library provides simple and efficient tools for data management and manipulation, including functions to clean, transform, and format datasets using popular data-handling methods. It aims to simplify common data-wrangling tasks.

Key functions include:

• dta_bmi() - computes the Body Mass Index (BMI) given weight and height columns in a data frame or tibble.
• dta_freq_mrq() - Frequency table for multiple response questions.
• dta_mrq() - Split multiple response question column into binary columns.
• dta_recode_auto() - Automatically recode categorical variables in a data frame.
• dta_recode() - Recode variables in a data frame based on a dictionary.
• dta_transpose() - Transpose a data frame with specified column as variable names.

JavaScript library for Numerical Analysis and Computations

Our JavaScript library is a versatile tool designed to seamlessly integrate with popular JavaScript frameworks like React, Next.js, and Vue. It provides a comprehensive set of features that enhances web development, ensuring smooth and efficient workflows across different frameworks. Developers can easily incorporate advanced functionalities into their applications, taking advantage of the library's optimized performance and user-friendly components.

Key benefits include:

• Framework compatibility: Works seamlessly with React, Next.js, and Vue.
• Advanced features: Offers a range of tools to enhance web applications.
• Optimized performance: Ensures high efficiency and speed.
• Ease of use: Provides intuitive components for streamlined development.
• Consistent support: Delivers reliable functionality across different JavaScript environments.

The above makes it an essential asset for creating sophisticated web applications.

Additional applications

Below are other other apps that out team have developed.

• rstemlab library: A comprehensive R library designed for numerical analysis and computing in STEM fields. It provides tools for solving equations, performing simulations, analyzing data, and visualizing results, empowering researchers and students in science, technology, engineering, and mathematics. Learn more
• check library: Input Validation and Error Handling Toolkit for R. It enables developers to verify input data types and enforce custom conditions with clear and informative error messages, making the debugging process reliable in addition to improving user experience in R packages and applications. Learn more
• CIE Mathematics 9709 Quiz Generator: A dynamic tool for creating customized quizzes based on the CIE Mathematics 9709 syllabus, designed to facilitate effective exam preparation and practice.
• RShiny App for Visualization: An interactive application that uses GUI to plot R graphics and generate ggplot syntax.