Least squares adjustment software
JuliaGrid is an easy-to-use power system simulation tool for researchers and educators provided as a Julia package.
Fast CPU and GPU Python implementations of Improved Kernel Partial Least Squares (PLS) by Dayal and MacGregor (1997) and Fast Partition-Based Cross-Validation With Centering and Scaling for XTX and XTY by Engstrøm and Jensen (2025). This package also includes options to use sample weights for PLS modeling.
MATGRID is an easy-to-use power system simulation tool for researchers and educators provided as a MATLAB package.
Computation of training set (X^T * W * X) and (X^T * W * Y) or (X^T * X) and (X^T * Y) in a cross-validation setting using the fast algorithms by Engstrøm and Jensen (2025).
Power-system analysis in Julia: AC power flow, IEC 60909 short circuit, N-1 contingency, WLS state estimation, CGMES and MATPOWER round-trip.
Comparison of a few different methods for estimating surface normals in a point cloud.
Software-defined GNSS positioning engine. Compute position entirely in software from raw RTCM3 MSM4 observations and broadcast ephemeris — Keplerian orbits, Saastamoinen tropo, Klobuchar iono, Hatch-smoothed pseudoranges, multi-constellation WLS. DGNSS and RTK planned.
Generalized least squares method which takes uncertainties in both dependent and independent variables.
Least squares adjustment software
Models parcel prices across Pittsburgh to isolate the effect of location by controlling for parcel characteristics. Uses WLS regression and ANCOVA to estimate adjusted prices, then links price variation to crime, school quality, and geography.
Stata implementation of Weighted Fixed Effects (WFE) estimators for causal inference with panel data (Imai & Kim, 2021, Political Analysis).(Public Preview, testing...)(Disclaimer: CURRENTLY WIP)
Predicting Car prices given the various characteristics
Generalized Bingham (yield stress + Bingham + power law) flow-curve fitting by a consistency-condition determinant method — Python package and browser app, with weighted least squares, confidence intervals and nested-model F-tests.
Analysis and comparison of Weighted Least Squares (WLS) and Ordinary Least Squares (OLS). Covers heteroscedasticity, Feasible WLS, Huber robust regression, and validates methods with a Monte Carlo simulation
A revisited study of steady-state process data reconciliation: a 2022 Excel/GEKKO experiment preserved, with a transparent and tested Python implementation. Reconciled values are model-constrained estimates, not ground truth.
R script for exploratory factor analysis of morphometric factors with the psych package: correlation matrix, principal-axis, WLS and alpha factoring with oblimin rotation, Horn parallel analysis, ICLUST item clustering and omega bifactor solutions. Mariana Trench; Lemenkova (2018), Turkish J. Maritime and Marine Sciences 4(2):146-155.
Maple-based statistical modelling of pressure–depth relationships across six wells, including OLS/WLS regression, reservoir effects, model diagnostics, and pressure-gradient estimation.
Python scripts fitting weighted least squares (WLS) regression models with statsmodels for several variables, on Mariana Trench morphometry. Reusable statistical-modelling example.
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