Introduction | Example 1: Nonlinear Regression with Correlated Errors | Profile Likelihood | Example 2: Generalized Cox-Ingersoll-Ross Process | Bayesian Inference | Appendix: Conjugate Posteriors for LMNs | References
Package: LMN 1.1.2
LMN: Inference for Linear Models with Nuisance Parameters
Efficient Frequentist profiling and Bayesian marginalization of parameters for which the conditional likelihood is that of a multivariate linear regression model. Arbitrary inter-observation error correlations are supported, with optimized calculations provided for independent-heteroskedastic and stationary dependence structures.
Authors:
LMN_1.1.2.tar.gz
LMN_1.1.2.zip(r-4.7)LMN_1.1.2.zip(r-4.6)LMN_1.1.2.zip(r-4.5)
LMN_1.1.2.tgz(r-4.6-x86_64)LMN_1.1.2.tgz(r-4.6-arm64)LMN_1.1.2.tgz(r-4.5-x86_64)LMN_1.1.2.tgz(r-4.5-arm64)
LMN_1.1.2.tar.gz(r-4.7-arm64)LMN_1.1.2.tar.gz(r-4.7-x86_64)LMN_1.1.2.tar.gz(r-4.6-arm64)LMN_1.1.2.tar.gz(r-4.6-x86_64)
LMN_1.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
LMN/json (API)
| # Install 'LMN' in R: |
| install.packages('LMN', repos = c('https://mlysy.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/mlysy/lmn/issues
Last updated from:3ad355244e. Checks:13 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 140 | ||
| linux-devel-x86_64 | OK | 142 | ||
| source / vignettes | OK | 204 | ||
| linux-release-arm64 | OK | 133 | ||
| linux-release-x86_64 | OK | 150 | ||
| macos-release-arm64 | OK | 136 | ||
| macos-release-x86_64 | OK | 164 | ||
| macos-oldrel-arm64 | OK | 119 | ||
| macos-oldrel-x86_64 | OK | 180 | ||
| windows-devel | OK | 118 | ||
| windows-release | OK | 167 | ||
| windows-oldrel | OK | 101 | ||
| wasm-release | OK | 141 |
Exports:list2mniwlmn_logliklmn_marglmn_postlmn_priorlmn_proflmn_suff
Dependencies:fftwR6RcppRcppEigenSuperGauss
