B C D E G H I M N P Q R S U X Y Z
| bbPrior | Priors on model space for variable selection problems |
| bfnormmix | Number of Normal mixture components under Normal-IW and Non-local priors |
| bicprior | Class "msPriorSpec" |
| binomPrior | Priors on model space for variable selection problems |
| cil | Treatment effect estimation for linear models via Confounder Importance Learning using non-local priors. |
| coef.mixturebf | Class "mixturebf" |
| coefByModel | Class "msfit" |
| coefByModel-method | Class "msfit" |
| coefByModel-methods | Class "msfit" |
| dalapl | Density and random draws from the asymmetric Laplace distribution |
| ddir | Dirichlet density |
| demom | Non-local prior density, cdf and quantile functions. |
| demom-method | Non-local prior density, cdf and quantile functions. |
| demom-methods | Non-local prior density, cdf and quantile functions. |
| demomigmarg | Non-local prior density, cdf and quantile functions. |
| dimom | Non-local prior density, cdf and quantile functions. |
| diwish | Density for Inverse Wishart distribution |
| dmom | Non-local prior density, cdf and quantile functions. |
| dmomigmarg | Non-local prior density, cdf and quantile functions. |
| dpostNIW | Posterior Normal-IWishart density |
| emomLM | Bayesian variable selection and model averaging for linear and probit models via non-local priors. |
| emomPM | Bayesian variable selection and model averaging for linear and probit models via non-local priors. |
| emomprior | Class "msPriorSpec" |
| eprod | Expectation of a product of powers of Normal or T random variables |
| getBIC | Obtain BIC and EBIC |
| getBIC-method | Obtain BIC and EBIC |
| getBIC-methods | Obtain BIC and EBIC |
| getEBIC | Obtain BIC and EBIC |
| getEBIC-method | Obtain BIC and EBIC |
| getEBIC-methods | Obtain BIC and EBIC |
| groupemomprior | Class "msPriorSpec" |
| groupimomprior | Class "msPriorSpec" |
| groupmomprior | Class "msPriorSpec" |
| groupzellnerprior | Class "msPriorSpec" |
| hald | Hald Data |
| igprior | Class "msPriorSpec" |
| imombf | Moment and inverse moment Bayes factors for linear models. |
| imombf.lm | Moment and inverse moment Bayes factors for linear models. |
| imomknown | Bayes factors for moment, inverse moment and Zellner-Siow g-prior. |
| imomprior | Class "msPriorSpec" |
| imomunknown | Bayes factors for moment, inverse moment and Zellner-Siow g-prior. |
| marginalNIW | Marginal likelihood under a multivariate Normal likelihood and a conjugate Normal-inverse Wishart prior. |
| marginalNIW-method | Marginal likelihood under a multivariate Normal likelihood and a conjugate Normal-inverse Wishart prior. |
| marginalNIW-methods | Marginal likelihood under a multivariate Normal likelihood and a conjugate Normal-inverse Wishart prior. |
| mixturebf | Class "mixturebf" |
| mixturebf-class | Class "mixturebf" |
| modelbbprior | Class "msPriorSpec" |
| modelbinomprior | Class "msPriorSpec" |
| modelcomplexprior | Class "msPriorSpec" |
| modelsearchBlockDiag | Bayesian variable selection for linear models via non-local priors. |
| modelSelection | Bayesian variable selection for linear models via non-local priors. |
| modelunifprior | Class "msPriorSpec" |
| mombf | Moment and inverse moment Bayes factors for linear models. |
| mombf.lm | Moment and inverse moment Bayes factors for linear models. |
| momknown | Bayes factors for moment, inverse moment and Zellner-Siow g-prior. |
| momprior | Class "msPriorSpec" |
| momunknown | Bayes factors for moment, inverse moment and Zellner-Siow g-prior. |
| msfit | Class "msfit" |
| msfit-class | Class "msfit" |
| msfit.coef | Class "msfit" |
| msfit.predict | Class "msfit" |
| msPriorSpec | Class "msPriorSpec" |
| msPriorSpec-class | Class "msPriorSpec" |
| nlpMarginal | Marginal density of the observed data for linear regression with Normal, two-piece Normal, Laplace or two-piece Laplace residuals under non-local and Zellner priors |
| nlpmarginals | Marginal density of the observed data for linear regression with Normal, two-piece Normal, Laplace or two-piece Laplace residuals under non-local and Zellner priors |
| normalidprior | Class "msPriorSpec" |
| palapl | Density and random draws from the asymmetric Laplace distribution |
| pemom | Non-local prior density, cdf and quantile functions. |
| pemomigmarg | Non-local prior density, cdf and quantile functions. |
| pimom | Non-local prior density, cdf and quantile functions. |
| pimomMarginalK | Marginal density of the observed data for linear regression with Normal, two-piece Normal, Laplace or two-piece Laplace residuals under non-local and Zellner priors |
| pimomMarginalU | Marginal density of the observed data for linear regression with Normal, two-piece Normal, Laplace or two-piece Laplace residuals under non-local and Zellner priors |
| pmom | Non-local prior density, cdf and quantile functions. |
| pmomigmarg | Non-local prior density, cdf and quantile functions. |
| pmomLM | Bayesian variable selection and model averaging for linear and probit models via non-local priors. |
| pmomMarginalK | Marginal density of the observed data for linear regression with Normal, two-piece Normal, Laplace or two-piece Laplace residuals under non-local and Zellner priors |
| pmomMarginalU | Marginal density of the observed data for linear regression with Normal, two-piece Normal, Laplace or two-piece Laplace residuals under non-local and Zellner priors |
| pmomPM | Bayesian variable selection and model averaging for linear and probit models via non-local priors. |
| postModeBlockDiag | Bayesian model selection and averaging under block-diagonal X'X for linear models. |
| postModeOrtho | Bayesian model selection and averaging under block-diagonal X'X for linear models. |
| postProb | Obtain posterior model probabilities |
| postProb-method | Obtain posterior model probabilities |
| postProb-methods | Obtain posterior model probabilities |
| postSamples | Extract posterior samples from an object |
| postSamples-method | Extract posterior samples from an object |
| postSamples-methods | Extract posterior samples from an object |
| pplPM | Bayesian variable selection and model averaging for linear and probit models via non-local priors. |
| ppmodel | Bayesian variable selection and model averaging for linear and probit models via non-local priors. |
| priorp2g | Moment and inverse moment prior elicitation |
| qimom | Non-local prior density, cdf and quantile functions. |
| qmom | Non-local prior density, cdf and quantile functions. |
| ralapl | Density and random draws from the asymmetric Laplace distribution |
| rnlp | Posterior sampling for regression parameters |
| rnlp-method | Posterior sampling for regression parameters |
| rnlp-methods | Posterior sampling for regression parameters |
| rpostNIW | Posterior Normal-IWishart density |
| show-method | Class "mixturebf" |
| show-method | Class "msfit" |
| unifPrior | Priors on model space for variable selection problems |
| x.hald | Hald Data |
| y.hald | Hald Data |
| zbfknown | Bayes factors for moment, inverse moment and Zellner-Siow g-prior. |
| zbfunknown | Bayes factors for moment, inverse moment and Zellner-Siow g-prior. |
| zellnerbf | Moment and inverse moment Bayes factors for linear models. |
| zellnerbf.lm | Moment and inverse moment Bayes factors for linear models. |
| zellnerprior | Class "msPriorSpec" |