Hierarchical Semiparametric Regression of Test Statistics
Long Qu <longor@iastate.edu>, Dan Nettleton
<dnett@iastate.edu>, Jack Dekkers <jdekkers@iastate.edu>
R (>= 2.5.1), Matrix , Iso(>= 0.0-5), splines, fda, MASS
This package implements methods for hierarchical
semiparametric regression models for test statistics.
Specifically, test statistics given the null/alternative
hypotheses are modeled parametrically, whereas the unobservable
status of null/alternative hypotheses are modeled using
nonparametric addtive logistic regression over covariates.

This package implements methods for hierarchical
semiparametric regression models for test statistics.
Specifically, test statistics given the null/alternative
hypotheses are modeled parametrically, whereas the unobservable
status of null/alternative hypotheses are modeled using
nonparametric additive logistic regression over covariates.

This package implements methods for hierarchical semiparametric regression models for test statistics. Specifically, test statistics given the null/alternative hypotheses are modeled parametrically, whereas the unobservable status of null/alternative hypotheses are modeled using nonparametric additive logistic regression over covariates.

Matrix , Iso(>= 0.0-5), splines, fda, stats, graphics, methods
Methods for hierarchical semiparametric regression models for test statistics are implemented in this package. Specifically, test statistics given the null/alternative hypotheses are modeled parametrically, whereas the unobservable status of null/alternative hypotheses are modeled using nonparametric additive logistic regression over covariates.

Sets up and executes a HiSSE model (Hidden State Speciation and Extinction) on a phylogeny and character distribution to test for hidden shifts in trait dependent rates of diversification.

Sets up and executes a HiSSE model (Hidden State Speciation and Extinction) on a phylogeny and character sets to test for hidden shifts in trait dependent rates of diversification.