missPLS
Methods and Reproducible Workflows for Partial Least Squares with Missing Data
Methods-first tooling for reproducing and extending the partial least squares regression studies on incomplete data described in Nengsih et al. (2019) <doi:10.1515/sagmb-2018-0059>. The package provides simulation helpers, missingness generators, imputation wrappers, component-selection utilities, real-data diagnostics, and reproducible study orchestration for Nonlinear Iterative Partial Least Squares (NIPALS)-Partial Least Squares (PLS) workflows.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.2.0 |
rolling linux/jammy R-4.5 | missPLS_0.2.0.tar.gz |
484.7 KiB |
0.2.0 |
rolling linux/noble R-4.5 | missPLS_0.2.0.tar.gz |
484.6 KiB |
0.2.0 |
rolling source/ R- | missPLS_0.2.0.tar.gz |
240.1 KiB |
0.2.0 |
latest linux/jammy R-4.5 | missPLS_0.2.0.tar.gz |
484.7 KiB |
0.2.0 |
latest linux/noble R-4.5 | missPLS_0.2.0.tar.gz |
484.6 KiB |
0.2.0 |
latest source/ R- | missPLS_0.2.0.tar.gz |
240.1 KiB |
0.2.0 |
2026-04-26 source/ R- | missPLS_0.2.0.tar.gz |
240.1 KiB |
0.2.0 |
2026-04-23 source/ R- | missPLS_0.2.0.tar.gz |
240.1 KiB |