Bayesian Restricted Likelihood Methods

bayesLm | MCMC algorithm for Bayesian Linear Model |

bayesTdistLm | MCMC algorithm for Heavy Tailed (t-distribution) Bayesian... |

beta_ls | Sampling for beta_l's in hierarchical model 5. [beta_l|-]... |

beta_ls_t | Sampling for beta_l's and v_ls in hierarchical T-model |

brlm | brlm: A package for implementing Bayesian Restricted... |

bstar_step | functions for bstar step in hierarchical model |

fitMixture | A standard Bayesian mixture model for outliers using a... |

fitOrderStat | Restricted likelihood Bayesian model using a middle set of... |

fn.attenuation | Computes attenuation factor |

fn.comp.ystst | Compute y** Computes y** from a proposed y* value |

fn.grads | Computes the gradients in the linear regression MCMC |

fn.one.rep.y | Perform a single Metropolis-Hastings step |

fn.radius | Computes radius |

fn.sample.Beta | sampling for beta in hieararchical model [Beta|-] |

hier_models | MCMC functions for the hierarchical versions of Normal Theory... |

hier_samp_funs | sampling mu in hierarchical model of paper. [mu_bstr|-]... |

log.fn.eval.lik | Full likelihood under Gaussian model |

log.prop.den | Caluclate the proposal dens on the log scale Computes... |

mu_rho | Sampling mu_rho [mu_rho|-] |

psi | Psi functions |

psi_rho_samp | Sampling psi_rho: [psi_rho|-] |

quads | quad1 and qaud2 |

re_model | Fit the Normal Theory Random Effects Model |

re_sample_mu | Sampling Functions for random effects model |

restrictedBayesLm | MCMC algorithm for Restricted Likelihood Bayesian Linear... |

rho_samp | sampling for [rho|-] in hierchical model |

rl_direct | Fitting restricted likelihood location-scale model using... |

rlDirectEval | Fitting restricted likelihood location-scale model using... |

rl_importance | Fitting restricted likelihood model using importance sampling... |

rlImportSamp | Fitting restricted likelihood model using importance sampling... |

z_l | [Zl|-]: sample one at a time |

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