Description: An implementation of the Spiegelhalter (2005) <doi:10.1002/sim.1970> Funnel plots  for reporting standardised ratios, with overdispersion adjustment.
Suggests: testthat (>= 2.1.0), knitr, rmarkdown, COUNT, tidyr
Packaged: 2019-10-09 08:30:13 UTC; Christopher

Packaged: 2020-02-25 15:09:23 UTC; Christopher

Title: Funnel Plots for Comparing Institutional Performance
Description: An implementation of methods presented by Spiegelhalter (2005) <doi:10.1002/sim.1970> Funnel plots for comparing institutional performance, for standardised ratios, ratios of counts and proportions with additive overdispersion adjustment.
Imports: dplyr, ggrepel, ggplot2, scales
Suggests: testthat (>= 2.1.0), knitr, rmarkdown, COUNT, tidyr, covr,

Packaged: 2020-11-30 16:03:47 UTC; Christopher

person("Chris", "Mainey", ,"c.mainey@nhs.net", role= c("aut", "cre"),
Suggests: testthat (>= 3.0.0), knitr, rmarkdown, COUNT, tidyr, covr,
Packaged: 2021-09-14 13:38:03 UTC; Chris.Mainey

Packaged: 2021-09-30 08:55:58 UTC; Chris.Mainey

Title: Solves the generalized Fused Lasso
Description: Solves the Fused Lasso for linear models. The methodology is implemented for regular linear models as well as for binomial endpoints.

Title: Community Detection via Fused Principal Component Analysis
Author: Yang Feng, Richard J. Samworth and Yi Yu
Description: Efficient procedures for community detection in network studies, especially for sparse networks. The algorithms impose penalties on the differences of the coordinates which represent the community labels of the nodes.

Author: Xin Gao, Yuan Zhong, and Raymond J. Carroll
Description: The fusion learning method uses a model selection algorithm to learn from multiple data sets across different experimental platforms through group penalization. The responses of interest may include a mix of discrete and continuous variables. The responses may share the same set of predictors, however, the models and parameters differ across different platforms. Integrating information from different data sets can enhance the power of model selection. Package is based on Xin Gao, Raymond J. Carroll (2017) <arXiv:1610.00667v1>.