iterLap
Approximate Probability Densities by Iterated Laplace Approximations
The iterLap (iterated Laplace approximation) algorithm approximates a general (possibly non-normalized) probability density on R^p, by repeated Laplace approximations to the difference between current approximation and true density (on log scale). The final approximation is a mixture of multivariate normal distributions and might be used for example as a proposal distribution for importance sampling (eg in Bayesian applications). The algorithm can be seen as a computational generalization of the Laplace approximation suitable for skew or multimodal densities.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.1-4 |
rolling linux/jammy R-4.5 | iterLap_1.1-4.tar.gz |
68.6 KiB |
1.1-4 |
rolling linux/noble R-4.5 | iterLap_1.1-4.tar.gz |
68.6 KiB |
1.1-4 |
rolling source/ R- | iterLap_1.1-4.tar.gz |
11.5 KiB |
1.1-4 |
latest linux/jammy R-4.5 | iterLap_1.1-4.tar.gz |
68.6 KiB |
1.1-4 |
latest linux/noble R-4.5 | iterLap_1.1-4.tar.gz |
68.6 KiB |
1.1-4 |
latest source/ R- | iterLap_1.1-4.tar.gz |
11.5 KiB |
1.1-4 |
2026-04-26 source/ R- | iterLap_1.1-4.tar.gz |
11.5 KiB |
1.1-4 |
2026-04-23 source/ R- | iterLap_1.1-4.tar.gz |
11.5 KiB |
1.1-4 |
2026-04-09 windows/windows R-4.5 | iterLap_1.1-4.zip |
75.3 KiB |
1.1-4 |
2025-04-20 source/ R- | iterLap_1.1-4.tar.gz |
11.5 KiB |