H_n                     Harmonic series
J_n                     Compute factor in the exponent of the
                        divergence time distribution
WAIC                    Compute WAIC
a_t_one                 Compute divergence function
a_t_two                 Compute divergence function
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
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
exp_normalize           Compute normalized probabilities: exp(x_i) /
                        sum_j exp(x_j)
expit                   The expit function
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
log_expit               Numerically accurately compute f(x) = log(x /
                        (1/x)).
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
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_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_class_assignment
                        Sample individual class assignments Z_i, i = 1,
                        ..., N
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
