Packaged: 2020-02-04 14:55:51 UTC; cesar.renteria

Packaged: 2020-03-20 01:51:09 UTC; cesar.renteria

Authors@R: person("Cesar", "Renteria", email = "cesar.renteria@cide.edu", role = c("aut", "cre"))
Suggests: knitr, markdown, testthat (>= 2.1.0)

Title: Minimum distance estimation in an imprecise probability model
Description: A minimum distance estimator is calculated for an
imprecise probability model. The imprecise probability model
consists of upper coherent previsions whose credal sets are
given by a finite number of constraints on the expectations.
The parameter set is finite. The estimator chooses that
parameter such that the empirical measure lies next to the
corresponding credal set with respect to the total variation

Description: A minimum distance estimator is calculated for an imprecise probability model.
The imprecise probability model consists of upper coherent previsions whose credal sets are given
by a finite number of constraints on the expectations. The parameter set is finite. The estimator chooses
that parameter such that the empirical measure lies next to the corresponding credal set with respect to

Description: Provides color palettes from Impressionism and post-Impressionism artworks. This package allows to select colors combinations while looking at the original paintings where colors were sampled from.

Title: Calculate Birth Year-Specific Probabilities of Immune Imprinting
Description: Reconstruct birth-year specific probabilities of immune imprinting to influenza A, using the methods of Gostic et al. (2016) <doi:10.1126/science.aag1322>. Plot, save, or export the calculated probabilities for use in your own research. By default, the package calculates subtype-specific imprinting probabilities, but with user-provided frequency data, it is possible to calculate probabilities for arbitrary kinds of primary exposure to influenza A, including primary vaccination and exposure to specific clades, strains, etc.
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), devtools

Title: Classification Trees with Imprecise Probabilities
Description: Creation of imprecise classification trees. They rely on
probability estimation within each node by means of either the
imprecise Dirichlet model or the nonparametric predictive
inference approach. The splitting variable is selected by the
strategy presented in Fink and Crossman (2013)
but also the original imprecise information gain of Abellan and