Title: Clustering in Longitudinal Networks
Author: D. Giorgi, C. Matias, T. Rebafka, F. Villers
Description: Stochastic block model used for dynamic graphs represented by Poisson processes.
To model recurrent interaction events in continuous time, an extension of the stochastic block model is proposed where every individual belongs to a latent group and interactions between two individuals follow a conditional inhomogeneous Poisson process with intensity driven by the individuals’ latent groups. The model is shown to be identifiable and its estimation is based on a semiparametric variational expectation-maximization algorithm. Two versions of the method are developed, using either a nonparametric histogram approach (with an adaptive choice of the partition size) or kernel intensity estimators. The number of latent groups can be selected by an integrated classification likelihood criterion.
M. Corneli, P. Latouche and F. Rossi (2016). <doi:10.1016/j.neucom.2016.02.031>.
A. P. Dempster, N. M. Laird and D. B. Rubin (1977). <http://www.jstor.org/stable/2984875>.
M. Jordan, Z. Ghahramani, T. Jaakkola and L. Saul (1999). <doi:10.1023/A:1007665907178>.
C. Matias, T. Rebafka and F. Villers (2018). <doi:10.1093/biomet/asy016>.

Title: Design Clinical Trials using Sequential Predictive Probability
Description: Functions are available to calibrate designs over a range of posterior and predictive thresholds, to plot the various design options, and to obtain the operating characteristics of optimal accuracy and optimal efficiency designs.
Suggests: covr, gt, knitr, rmarkdown, spelling, testthat (>= 3.0.0),
Imports: dplyr, furrr, future, ggplot2, plotly, purrr, tibble,
Author: Emily C. Zabor [aut, cre] (<https://orcid.org/0000-0002-1402-4498>),
Maintainer: Emily C. Zabor <zabore2@ccf.org>

Imports: dplyr, furrr, ggplot2, plotly, purrr, tibble, patchwork, tidyr

person("Paul", "van der Laken", email = "paulvanderlaken@gmail.com", role = c("aut", "cre"))
Description: The PPS is an asymmetric, data-type-agnostic score that can detect
linear or non-linear relationships between two columns. The score ranges
from 0 (no predictive power) to 1 (perfect predictive power). It can be useful
for data exploration purposes, in the same way correlation analysis is.
For more information on PPS, see Wetschoreck (2020) <https://towardsdatascience.com/rip-correlation-introducing-the-predictive-power-score-3d90808b9598>
Packaged: 2021-02-27 20:14:06 UTC; Paul van der Laken
Maintainer: Paul van der Laken <paulvanderlaken@gmail.com>