add_leaf_branch | Add a leaf branch to an existing tree tree_old |

add_multichotomous_tip | Add a leaf branch to an existing tree tree_old to make a multichotomus branch |

add_one_sample | Functions to simulate trees and node parameters from a DDT process. Add a branch to an existing tree according to the branching process of DDT |

add_root | Add a singular root node to an existing nonsingular tree |

attach_subtree | Attach a subtree to a given DDT at a randomly selected location |

A_t_inv_one | Compute divergence function |

A_t_inv_two | Compute divergence function |

a_t_one | Compute divergence function |

a_t_one_cum | Compute divergence function |

a_t_two | Compute divergence function |

a_t_two_cum | Compute divergence function |

compute_IC | Compute information criteria for the DDT-LCM model |

create_leaf_cor_matrix | Create a tree-structured covariance matrix from a given tree |

data_synthetic | Synthetic data example |

ddtlcm_fit | MH-within-Gibbs sampler to sample from the full posterior distribution of DDT-LCM |

div_time | Sample divergence time on an edge uv previously traversed by m(v) data points |

draw_mnorm | Efficiently sample multivariate normal using precision matrix from x ~ N(Q^{-1}a, Q^{-1}), where Q^{-1} is the precision matrix |

expit | The expit function |

exp_normalize | Compute normalized probabilities: exp(x_i) / sum_j exp(x_j) |

H_n | Harmonic series |

initialize | Initialize the MH-within-Gibbs algorithm for DDT-LCM |

initialize_hclust | Estimate an initial binary tree on latent classes using hclust() |

initialize_poLCA | Estimate an initial response profile from latent class model using poLCA() |

initialize_randomLCM | Provide a random initial response profile based on latent class mode |

J_n | Compute factor in the exponent of the divergence time distribution |

logit | The logistic function |

logllk_ddt | Calculate loglikelihood of a DDT, including the tree structure and node parameters |

logllk_ddt_lcm | Calculate loglikelihood of the DDT-LCM |

logllk_div_time_one | Compute loglikelihood of divergence times for a(t) = c/(1-t) |

logllk_div_time_two | Compute loglikelihood of divergence times for a(t) = c/(1-t)^2 |

logllk_lcm | Calculate loglikelihood of the latent class model, conditional on tree structure |

logllk_location | Compute log likelihood of parameters |

logllk_tree_topology | Compute loglikelihood of the tree topology |

log_expit | Numerically accurately compute f(x) = log(x / (1/x)). |

parameter_diet | Parameters for the HCHS dietary recall data example |

plot.ddt_lcm | Create trace plots of DDT-LCM parameters |

plot.summary.ddt_lcm | Plot the MAP tree and class profiles of summarized DDT-LCM results |

plot_tree_with_barplot | Plot the MAP tree and class profiles (bar plot) of summarized DDT-LCM results |

plot_tree_with_heatmap | Plot the MAP tree and class profiles (heatmap) of summarized DDT-LCM results |

predict.ddt_lcm | Prediction of class memberships from posterior predictive distributions |

predict.summary.ddt_lcm | Prediction of class memberships from posterior summaries |

print.ddt_lcm | Print out setup of a ddt_lcm model |

print.summary.ddt_lcm | Print out summary of a ddt_lcm model |

proposal_log_prob | Calculate proposal likelihood |

quiet | Suppress print from cat() |

random_detach_subtree | Metropolis-Hasting algorithm for sampling tree topology and branch lengths from the DDT branching process. |

reattach_point | Attach a subtree to a given DDT at a randomly selected location |

result_diet_1000iters | Result of fitting DDT-LCM to a semi-synthetic data example |

sample_class_assignment | Sample individual class assignments Z_i, i = 1, ..., N |

sample_c_one | Sample divergence function parameter c for a(t) = c / (1-t) through Gibbs sampler |

sample_c_two | Sample divergence function parameter c for a(t) = c / (1-t)^2 through Gibbs sampler |

sample_leaf_locations_pg | Sample the leaf locations and Polya-Gamma auxilliary variables |

sample_sigmasq | Sample item group-specific variances through Gibbs sampler |

sample_tree_topology | Sample a new tree topology using Metropolis-Hastings through randomly detaching and re-attaching subtrees |

simulate_DDT_tree | Simulate a tree from a DDT process. Only the tree topology and branch lengths are simulated, without node parameters. |

simulate_lcm_given_tree | Simulate multivariate binary responses from a latent class model given a tree |

simulate_lcm_response | Simulate multivariate binary responses from a latent class model |

simulate_parameter_on_tree | Simulate node parameters along a given tree. |

summary.ddt_lcm | Summarize the output of a ddt_lcm model |

WAIC | Compute WAIC |