A lean C library for working with point cloud data
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Updated
Aug 15, 2023 - C
A lean C library for working with point cloud data
A data standard to enable right-of-way regulation and two-way communication between mobility companies and local governments.
A Julia package for multivariate statistics and data analysis (e.g. dimension reduction)
Mathematical tools (interpolation, dimensionality reduction, optimization, etc.) written in C 11 with Eigen
an open directory of mobility feeds and operators — powers both Transitland v1 and v2
A JavaScript Library for Dimensionality Reduction
A tidyverse suite for (pre-) machine-learning: cluster, PCA, permute, impute, rotate, redundancy, triangular, smart-subset, abundant and variable features.
Workshop on tidytranscriptomics: Performing tidy transcriptomics analyses with tidybulk, tidyverse and tidyheatmap
Probing Projections introduces a set of interaction and visualisation techniques to make examining dimensionality-reduced datasets easier.
This repo demonstrate a comprehensive modern data stack using popular open-source tools.
Several examples of multivariate techniques implemented in R, Python, and SAS. Multivariate concrete dataset retrieved from https://archive.ics.uci.edu/ml/datasets/Concrete Slump Test. Credit to Professor I-Cheng Yeh.
Python tools for working with MDS Provider data
a JSON-based data schema to catalog mobility/transit/transportation data feeds
PCA and normal mode analysis of proteins
Infrastructure-free Multidimensional Scaling (MDS) for drones swarm localization. Performances comparison with trilateration algorithm
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