Machine Learning for International Business with R
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Appendices
References
Introduction
The Data‑Science Pipeline
1
Using Positron for Reproducible Research in R
2
Positron and Quarto: A Unified Multi-Language Workflow
3
APIs for international trade and economics
Machine‑Learning Approaches to Regression
4
Regression Models in Machine Learning with R
5
Elastic‐Net Regression: Theory, Computation, and Social‑Science Applications
Machine‑Learning Approaches to Classification
6
Classification Models in R for Predictive Modeling and Causal Inference
7
Machine Learning in Action – KNN and Lessons Learned
8
Neural Networks in Social Science Research
Learning from Unstructured Data
9
Structuring Unstructured Text Data in R
10
Structuring Audio Data for Qualitative Social Science Analysis (with R and Python via Reticulate)
11
Introduction to Satellite Imagery in Social Science Research
Geospatial Data Science
12
Geospatial Data Visualization in R for Geopolitics and Economics
13
Geospatial Statistics and Econometrics in R
AI and Ethics – Principles, Fairness, and Regulation
Appendices
Summary
References
Appendices
References
References
Code
Summary
Source Code
# References {.unnumbered}
::: {#refs}
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