Power analysis and sample size calculation for Welch and Hsu (Hedderich and Sachs (2018), ISBN:978-3-662-56657-2) t-tests including Monte-Carlo simulations of empirical power and type-I-error. Power and sample size calculation for Wilcoxon rank sum and signed rank tests via Monte-Carlo simulations. Power and sample size required for the evaluation of a diagnostic test(-system) (Flahault et al. (2005), <doi:10.1016/j.jclinepi.2004.12.009>; Dobbin and Simon (2007), <doi:10.1093/biostatistics/kxj036>) as well as for a single proportion (Fleiss et al. (2003), ISBN:978-0-471-52629-2; Piegorsch (2004), <doi:10.1016/j.csda.2003.10.002>; Thulin (2014), <doi:10.1214/14-ejs909>) and comparing two negative binomial rates (Zhu and Lakkis (2014), <doi:10.1002/sim.5947>).

Maximum Likelihood Inference on Multi-State Trees
person("Joelle", "Barido-Sottani", email = "joelle.barido-sottani@m4x.org", role = c("aut", "cre"))
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>.

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>.

Variational Autoencoder Models for IRT Parameter Estimation
c(
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"))
)
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.
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
have an installation of 'Python 3'.
keras (>= 2.3.0), reticulate (>= 1.0), tensorflow (>= 2.2.0),
tfprobability (>= 0.11.0)
TensorFlow (https://www.tensorflow.org), Keras
(https://keras.io), TensorFlow Probability
(https://www.tensorflow.org/probability)
list( packages = list( list(package = "keras", pip =
TRUE), list(package = "tensorflow", pip = TRUE), list(package =
"tensorflow-probability", pip = TRUE) ) )
Geoffrey Converse [aut, cre, cph],
Suely Oliveira [ctb, ths],
Mariana Curi [ctb]