DRLAP2
Dynamic Reinforcement Learning and Adaptive Progressive Censoring
Implements Maximum Likelihood Estimation (MLE) and Bayesian Markov Chain Monte Carlo (MCMC) sampling algorithms for progressive censoring models, with support for dynamic reinforcement learning environment simulation and accelerated computational routines written in C++.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.1 |
rolling linux/jammy R-4.5 | DRLAP2_0.1.1.tar.gz |
55.3 KiB |
0.1.1 |
rolling linux/noble R-4.5 | DRLAP2_0.1.1.tar.gz |
56.0 KiB |
0.1.1 |
rolling source/ R- | DRLAP2_0.1.1.tar.gz |
5.9 KiB |
0.1.1 |
latest linux/jammy R-4.5 | DRLAP2_0.1.1.tar.gz |
55.3 KiB |
0.1.1 |
latest linux/noble R-4.5 | DRLAP2_0.1.1.tar.gz |
56.0 KiB |
0.1.1 |
latest source/ R- | DRLAP2_0.1.1.tar.gz |
5.9 KiB |
0.1.1 |
2026-04-23 source/ R- | DRLAP2_0.1.1.tar.gz |
0 B |