ComputeCI               Compute confidence around rate estimates
ConvertPhangornReconstructions
                        Convert phangorn reconstruction to a vector
ancRECON                Ancestral state reconstruction
ancRECON_slice          Ancestral state reconstruction for a particular
                        time
compute_joint_ci        Compute joint ancestral-state reconstructions
                        by sampling
corDISC                 Correlated evolution binary traits
corHMM                  Hidden Rates Model
corHMMDredge            Automatic Discovery of Optimal Discrete
                        Character Models via Simulated Annealing
corHMMDredgeBase        Fit a Single Penalized Hidden Markov Model
dent_likelihood         Dents the likelihood surface This takes any
                        values that are better (lower) than the desired
                        negative log likelihood and reflects them
                        across the best_neglnL + delta line, "denting"
                        the likelihood surface.
dent_propose            Propose new values This proposes new values
                        using a normal distribution centered on the
                        original parameter values, with desired
                        standard deviation. If any proposed values are
                        outside the bounds, it will propose again.
dent_walk               Sample points from along a ridge This "dents"
                        the likelihood surface by reflecting points
                        better than a threshold back across the
                        threshold (think of taking a hollow plastic
                        model of a mountain and punching the top so
                        it's a volcano). It then uses essentially a
                        Metropolis-Hastings walk to wander around the
                        new rim. It adjusts the proposal width so that
                        it samples points around the desired
                        likelihood.  This is better than using the
                        curvature at the maximum likelihood estimate
                        since it can actually sample points in case the
                        assumptions of the curvature method do not
                        hold. It is better than varying one parameter
                        at a time while holding others constant because
                        that could miss ridges: if I am fitting 5=x+y,
                        and get a point estimate of (3,2), the reality
                        is that there are an infinite range of values
                        of x and y that will sum to 5, but if I hold x
                        constant it looks like y is estimated very
                        precisely. Of course, one could just fully
                        embrace the Metropolis-Hastings lifestyle and
                        use a full Bayesian approach.
fitCorrelationTest      Test for correlation
getCVTable              Print Method for corhmm.kfold Class Objects
getFullMat              Combines several rate class index matrices
getModelTable           Summarize Model Statistics for a List of corHMM
                        Objects
getStateMat4Dat         Produce an index matrix and legend from a
                        dataset
get_batch_profile_lik   Perform Batch Profile Likelihood Analysis for
                        Multiple Parameters
kFoldCrossValidation    Perform K-Fold Cross-Validation for corHMM
                        Models
makeSimmap              Simulate a character history
node_importance_by_cluster
                        Identify nodes that distinguish clusters of
                        reconstructions
phylo_aware_dist_fast   Compile a faster version of the reconstruction
                        distance
phylo_aware_dist_local
                        Phylogenetically aware distance between two
                        reconstructions
plot.dentist            Plot the dented samples This will show the
                        univariate plots of the parameter values versus
                        the likelihood as well as bivariate plots of
                        pairs of parameters to look for ridges.
plotDredgeTrace         Plot the Trace of a corHMMDredge Simulated
                        Annealing Search
plotMKmodel             Plot a Markov model
plotRECON               Plot ancestral state reconstructions
plot_batch_profile_lik
                        Plot Batch Profile Likelihoods
plot_clustered_phylo    Plot competing ancestral histories on a
                        phylogeny
plot_raw_recon          Raster plot of a sample of joint
                        reconstructions
plot_stacked_densities
                        Stacked log-likelihood densities by cluster
plot_transition_summary
                        Plot Summary Statistics of Stochastic Character
                        Map Transitions
primates                Example datasets
print.dentist           Print dentist print summary of output from
                        dent_walk
rayDISC                 Evolution of categorical traits
simMarkov               Simulate a character on the tree
summarize_transition_stats
                        Summarize Transition Information from
                        Stochastic Character Maps
summary.dentist         Summarize dentist Display summary of output
                        from dent_walk
tipHomology             The Probability of a Tip State Being Homologous
