KRLS
Kernel-Based Regularized Least Squares
Package implements Kernel-based Regularized Least Squares (KRLS), a machine learning method to fit multidimensional functions y=f(x) for regression and classification problems without relying on linearity or additivity assumptions. KRLS finds the best fitting function by minimizing the squared loss of a Tikhonov regularization problem, using Gaussian kernels as radial basis functions. For further details see Hainmueller and Hazlett (2014).
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0-0 |
rolling linux/jammy R-4.5 | KRLS_1.0-0.tar.gz |
77.6 KiB |
1.0-0 |
rolling linux/noble R-4.5 | KRLS_1.0-0.tar.gz |
77.5 KiB |
1.0-0 |
rolling source/ R- | KRLS_1.0-0.tar.gz |
36.3 KiB |
1.0-0 |
latest linux/jammy R-4.5 | KRLS_1.0-0.tar.gz |
77.6 KiB |
1.0-0 |
latest linux/noble R-4.5 | KRLS_1.0-0.tar.gz |
77.5 KiB |
1.0-0 |
latest source/ R- | KRLS_1.0-0.tar.gz |
36.3 KiB |
1.0-0 |
2026-04-26 source/ R- | KRLS_1.0-0.tar.gz |
36.3 KiB |
1.0-0 |
2026-04-23 source/ R- | KRLS_1.0-0.tar.gz |
36.3 KiB |
1.0-0 |
2026-04-09 windows/windows R-4.5 | KRLS_1.0-0.zip |
80.0 KiB |
1.0-0 |
2025-04-20 source/ R- | KRLS_1.0-0.tar.gz |
36.3 KiB |