Title: Maximum Likelihood Inference on Multi-State Trees
Authors@R: person("Joelle", "Barido-Sottani", email = "joelle.barido-sottani@m4x.org", role = c("aut", "cre"))
Description: Inference of a multi-states birth-death model from a phylogeny, comprising a number of states N, birth and death rates for each state and on which edges each state appears. Inference is done using a hybrid approach: states are progressively added in a greedy approach. For a fixed number of states N the best model is selected via maximum likelihood. Reference: J. Barido-Sottani and T. Stadler (2017) <doi:10.1101/215491>.
Author: Joelle Barido-Sottani [aut, cre]

Description: Inference of a multi-states birth-death model from a phylogeny, comprising a number of states N, birth and death rates for each state and on which edges each state appears. Inference is done using a hybrid approach: states are progressively added in a greedy approach. For a fixed number of states N the best model is selected via maximum likelihood. Reference: J. Barido-Sottani, T. G. Vaughan and T. Stadler (2018) <doi:10.1098/rsif.2018.0512>.

Title: Variational Autoencoder Models for IRT Parameter Estimation
person("Geoffrey", "Converse", email = "converseg@gmail.com", role = c("aut", "cre", "cph")),
person("Suely", "Oliveira", email = "suely-oliveira@uiowa.edu", role = c("ctb", "ths")),
person("Mariana", "Curi", email = "mcuri@icmc.usp.br", role = c("ctb"))
Description: Based on the work of Curi, Converse, Hajewski, and Oliveira (2019) <doi:10.1109/IJCNN.2019.8852333>. This package provides easy-to-use functions which create a variational autoencoder (VAE) to be used for parameter estimation in Item Response Theory (IRT) - namely the Multidimensional Logistic 2-Parameter (ML2P) model. To use a neural network as such, nontrivial modifications to the architecture must be made, such as restricting the nonzero weights in the decoder according to some binary matrix Q. The functions in this package allow for straight-forward construction, training, and evaluation so that minimal knowledge of 'tensorflow' or 'keras' is required.
Note: The developer version of 'keras' should be used, rather than the
CRAN version. The latter will cause tests to fail on an initial
run, but work on subsequent tries. To avoid this, use
devtools::install_github("rstudio/keras"). The user also must
Imports: keras (>= 2.3.0), reticulate (>= 1.0), tensorflow (>= 2.2.0),
Config/reticulate: list( packages = list( list(package = "keras", pip =
TRUE), list(package = "tensorflow", pip = TRUE), list(package =
"tensorflow-probability", pip = TRUE) ) )
Author: Geoffrey Converse [aut, cre, cph],