OUwie                   Generalized Hansen models
OUwie.anc               Estimate ancestral states given a fitted OUwie
                        model
OUwie.boot              Parametric bootstrap function
OUwie.contour           Generates data for contour plot of likelihood
                        surface
OUwie.dredge            Generalized Detection of shifts in OU process
OUwie.fixed             Generalized Hansen model likelihood calculator
OUwie.format            Format data and tree for OUwie
OUwie.sim               Generalized Hansen model simulator
check.identify          A test of regime identifiability
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.
fix.kappa               Adjust tree for matrix condition
getModelAvgParams       Model average the parameter estimates over
                        severl hOUwie fits.
getModelTable           Generate a table from a set of hOUwie models
                        describing their relative fit to data.
getOUParamStructure     Generate a continuous model parameter structure
hOUwie                  Fit a joint model of discrete and continuous
                        characters via maximum-likelihood.
hOUwie.fixed            Fit a joint model of discrete and continuous
                        characters via maximum-likelihood with fixed
                        regimes.
hOUwie.recon            Reconstruct the marginal probability of
                        discrete node states under the hOUwie model.
hOUwie.sim              Simulate a discrete and continuous character
                        following a Markov and Ornstein-Uhlenbeck
                        model.
hOUwie.thorough         Rerun a set of hOUwie models with the best
                        mappings of the set.
hOUwie.walk             Sample points from along a ridge for a hOUwie
                        model
plot.OUwie.contour      Contour plot
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.
print.dentist           Print dentist print summary of output from
                        dent_walk
summary.dentist         Summarize dentist Display summary of output
                        from dent_walk
tree                    An example dataset
