Date of Graduation
5-2026
Document Type
Thesis
Degree Name
Bachelor of Science in Computer Science
Degree Level
Undergraduate
Department
Computer Science and Computer Engineering
Advisor/Mentor
Matthew Patitz
Committee Member
Reetam Majumder
Second Committee Member
Matthew Patitz
Third Committee Member
Chris Farnell
Abstract
Splines are used for representing complex functions. In statistics, splines can be used for distributional shapes that are difficult to model by traditional parametric approaches. Ramsay (1) uses M-Spline bases to estimate continuous distributions. Semi-Parametric Quantile Regression (SPQR), developed by Xu and Reich (2), models conditional distributions where a neural network is used to estimate the basis function weights that depend on covariates. (3) implements a package for SPQR in R. We build on this by implementing a version of SPQR in Python with PyTorch. By using PyTorch, we can use more sophisticated deep learning architectures than those available in the R version. Additionally, we pro- vide use cases and a real-life example to illustrate how to use the Python package.
Keywords
Neural Networks; Loss Functions; Python; Software Engineering; Artificial Intelligence; Density Estimation
Citation
Eddy, C., & Majumder, R. (2026). pySPQR: A Python Package for Density Estimation using Deep Learning. Electrical Engineering and Computer Science Undergraduate Honors Theses Retrieved from https://scholarworks.uark.edu/elcsuht/29
Included in
Applied Statistics Commons, Artificial Intelligence and Robotics Commons, Data Science Commons, Numerical Analysis and Scientific Computing Commons, Software Engineering Commons, Statistical Models Commons
