Halva—‘grapHical Analysis with Latent VAriables’—is a Python package dedicated to statistical analysis of multivariate ordinal data, designed specifically to handle missing values and latent variables ...

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CEO of Paul M. Wendee & Associates, LLC; Publisher of the Intrinsic Value Wealth Report Newsletter; Founder of the Value Driver Institute. To make sound business and investment decisions, business ...

Unlock automatic understanding of text data! Join our hands-on workshop to explore how Python—and spaCy in particular—helps you process, annotate, and analyze text. This workshop is ideal for data ...

In today’s data-rich environment, business are always looking for a way to capitalize on available data for new insights and increased efficiencies. Given the escalating volumes of data and the ...

If you’re new to Python, one of the first things you’ll encounter is variables and data types. Understanding how Python handles data is essential for writing clean, efficient, and bug-free programs.

Dataset: survey ratings (1–10 scale) Target variable: Writing Methods: CUB (in R), Proportional Odds Model (in Python) Goal: Compare model adequacy and interpret ordinal responses ...

Abstract: Closed-loop control is widely adopted in industrial processes. It brings challenges for dynamic latent modeling and monitoring. Multirate process measurements in real applications even ...

Have you ever found yourself wrestling with Excel formulas, wishing for a more powerful tool to handle your data? Or maybe you’ve heard the buzz about Python in Excel and wondered if it’s truly the ...