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accept_family()
- Compute the acceptable family of linear subsets
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accept_family_binary()
- Compute the acceptable family for binary data
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accept_family_randint()
- Compute the acceptable family of linear subsets for the
random intercept model
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bayeslmm()
- Bayesian linear mixed models (LMMs)
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branch_and_bound()
- Branch-and-bound algorithm for linear subset search
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getXY_randint()
- Compute the pseudo X and Y variables for LMM summarization
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get_coefs()
- Compute the optimal linear coefficients for any covariates
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get_coefs_randint()
- Compute the optimal linear coefficients for any covariates
under a random intercept model
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initHS()
- Initialize the horseshoe prior parameters
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lasso_path()
- (Adaptive) lasso for Bayesian variable selection
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loss_maha()
- Compute the pseudo X and Y variables for LMM summarization
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post_predict()
- Get posterior predictive draws and log-predictive density
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pp_loss()
- Compute the predictive and empirical cross-validated squared error loss
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pp_loss_binary()
- Compute the predictive and empirical cross-validated loss for binary data.
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pp_loss_out()
- Compute the predictive squared error loss on *new* testing points
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pp_loss_randint()
- Compute the predictive and empirical cross-validated Mahalanobis loss
under the random intercept model
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prescreen()
- Marginal pre-screening algorithm
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prescreen_lasso()
- Marginal pre-screening algorithm given lasso output
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proj_posterior()
- Projected predictive distribution for regression coefficients
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proj_posterior_randint()
- Projected predictive distribution for regression coefficients
in the random intercept model
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sampleHS()
- Sampler for horseshoe prior parameters
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simulate_lm()
- Simulate a Gaussian linear model
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simulate_lm_randint()
- Simulate a Gaussian linear model with random intercepts
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var_imp()
- Variable importance for the acceptable family