Search Results for "building"

Swift Numerics is a foundational library that extends the Swift standard library with essential numerical protocols, types, and functions needed for scientific and systems programming. It defines generic abstractions over real and complex numbers so algorithms can be written once and work across concrete floating-point types. The package includes RealModule utilities and a full Complex type with the expected arithmetic and transcendental functions, bridging a long-standing gap for numerics...

...It is not a polished textbook full of full proofs; rather it offers clean definitions, theorem statements, intuitive motivations, and informal sketches of why things work, with the goal of building conceptual understanding. The coverage spans undergraduate and early graduate topics, designed to show how different areas of math fit together—linear algebra, analysis, topology, number theory, and more—without going deeply into every subtopic. Because it is written in LaTeX (with supporting Asymptote or other tools), readers can compile their own version, and the repository integrates diagrams, flowcharts, and supplementary files.

NAVAL-SEM is a fully offline desktop application for PLS-SEM and CB-SEM research. Designed for researchers, PhD scholars, professors, and analysts, NAVAL-SEM provides visual model building, bootstrapping, HTMT, fit indices, mediation analysis, MGA, and export support without requiring cloud access or subscriptions. Key features include: • PLS-SEM and covariance-based SEM • Visual drag-and-drop model builder • Bootstrapping and mediation analysis • HTMT, AVE, CR, Cronbach’s Alpha • Multi-group analysis (MGA) • R/Python/Lavaan export • Fully offline execution for sensitive datasets Available for Windows, macOS, and Linux.

A Python environment for large and sparse linear and nonlinear optimization using intuitive interfaces to linear algebra tools and subproblem solvers written in low-level languages. It provides building blocks to experiment with new ideas and algorithms.

...It provides algorithms for unsupervised and supervised learning, which may be done with continuous or discrete labels. Post analysis, the toolkit further automates building the visual representations for the trained model.