StempCens
Spatio-Temporal Estimation and Prediction for Censored/Missing Responses
It estimates the parameters of spatio-temporal models with censored or missing data using the SAEM algorithm (Delyon et al., 1999). This algorithm is a stochastic approximation of the widely used EM algorithm and is particularly valuable for models in which the E-step lacks a closed-form expression. It also provides a function to compute the observed information matrix using the method developed by Louis (1982). To assess the performance of the fitted model, case-deletion diagnostics are provided.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.2.0 |
rolling linux/jammy R-4.5 | StempCens_1.2.0.tar.gz |
220.0 KiB |
1.2.0 |
rolling linux/noble R-4.5 | StempCens_1.2.0.tar.gz |
226.4 KiB |
1.2.0 |
rolling source/ R- | StempCens_1.2.0.tar.gz |
76.8 KiB |
1.2.0 |
latest linux/jammy R-4.5 | StempCens_1.2.0.tar.gz |
220.0 KiB |
1.2.0 |
latest linux/noble R-4.5 | StempCens_1.2.0.tar.gz |
226.4 KiB |
1.2.0 |
latest source/ R- | StempCens_1.2.0.tar.gz |
76.8 KiB |
1.2.0 |
2026-04-26 source/ R- | StempCens_1.2.0.tar.gz |
76.8 KiB |
1.2.0 |
2026-04-23 source/ R- | StempCens_1.2.0.tar.gz |
76.8 KiB |
1.2.0 |
2026-04-09 windows/windows R-4.5 | StempCens_1.2.0.zip |
630.4 KiB |
1.1.0 |
2025-04-20 source/ R- | StempCens_1.1.0.tar.gz |
70.5 KiB |