LAM (version 0.6-19)

LAM-package: tools:::Rd_package_title("LAM")

Description

tools:::Rd_package_description("LAM")

Author

tools:::Rd_package_author("LAM")

Maintainer: tools:::Rd_package_maintainer("LAM")

Details

The LAM package contains the following main functions:

• A general fitting method for mean and covariance structure for multivariate normally distributed data is the mlnormal function. Prior distributions or regularization methods (lasso penalties) are also accommodated. Missing values on dependent variables can be treated by applying the full information maximum likelihood method implemented in this function.

• A general (but experimental) Metropolis-Hastings sampler for Bayesian analysis based on MCMC is implemented in the amh function. Deterministic optimization of the posterior distribution (maximum posterior estimation or penalized maximum likelihood estimation) can be conduction with the pmle function which is based on stats::optim.

References

Cole, S. R., Chu, H., & Greenland, S. (2013). Maximum likelihood, profile likelihood, and penalized likelihood: a primer. American Journal of Epidemiology, 179(2), 252-260. tools:::Rd_expr_doi("10.1093/aje/kwt245")

Longford, N. T. (1987). A fast scoring algorithm for maximum likelihood estimation in unbalanced mixed models with nested random effects. Biometrika, 74(4), 817-827. tools:::Rd_expr_doi("10.1093/biomet/74.4.817")

Roberts, G. O., & Rosenthal, J. S. (2001). Optimal scaling for various Metropolis-Hastings algorithms. Statistical Science, 16(4), 351-367. tools:::Rd_expr_doi("10.1214/ss/1015346320")

Examples

  ##  > library(LAM)
  ##  ## LAM 0.0-4 (2017-03-03 16:53:46)
  ##
  ##   __         ______     __    __
  ##  /\ \       /\  __ \   /\ "-./  \
  ##  \ \ \____  \ \  __ \  \ \ \-./\ \
  ##   \ \_____\  \ \_\ \_\  \ \_\ \ \_\
  ##    \/_____/   \/_/\/_/   \/_/  \/_/
  ##