Short answer: Dlog’s SEM is built for single-user, explainable, actionable inference. It estimates stable latent factors from many indicators, re-fits regularly to track changing effects, and ties coefficients back to your journal quotes so recommendations are grounded in lived data. SEM is computed on-device.

Why that matters? multiple indicators per construct (the 61 baseline items) reduce overfitting and let the model recover latent traits (Personality, Character, Resources, Well-Being) rather than relying on noisy single measures. AND, its not just the SEM, its mixed methods: dlog model's coefficients are linked to narrative evidence from your entries, giving both quantitative explanation and concrete journal quotes you can inspect.

I’m planning on adding a technical appendix and a white paper when I launch the Dlog Research edition for universities at some point next year. Meanwhile you can read the model summary here:: https://dlog.pro/#dlogModel and download an example mixed-methods report you can make in the Dlog Labs https://updates.dlog.pro/AnonDoe.pdf.

Thanks again for the interest; if you do register with Dlog email me at johan@dlog.pro and I'll give you a free perpetual license and 1 million tokens to test out Dlog.