Aster models are exponential family regression models for life history analysis. They are like generalized linear models except that elements of the response vector can have different families (e. g., some Bernoulli, some Poisson, some zero-truncated Poisson, some normal) and can be dependent, the dependence indicated by a graphical structure. Discrete time survival analysis, zero-inflated Poisson regression, and generalized linear models that are exponential family (e. g., logistic regression and Poisson regression with log link) are special cases. Main use is for data in which there is survival over discrete time periods and there is additional data about what happens conditional on survival (e. g., number of offspring). Uses the exponential family canonical parameterization (aster transform of usual parameterization). Unlike the aster package, this package does dependence groups (nodes of the graph need not be conditionally independent given their predecessor node), including multinomial and two-parameter normal as families. Thus this package also generalizes mark-capture-recapture analysis.
CHANGES IN 0.2
hornworm dataset (data for Eck, et al.)
fixes to validasterdata
check for zero truncated Poisson did not check that y_j >= y_p(j). Fixed.
check names of input data frame did not note that reshape (which this function uses) will take a column named "id" to be the idvar whether or not this is intended. Now warns.
CHANGES IN 0.2-1
CHANGES IN 0.3
make constancy return a sparse matrix
fix constancy to also have directions of constancy for nodes whose predecessors are almost surely zero
fix transformUnconditional to deal with sparse constancy matrices
modernize registration of native routines