Control Charts for Conway-Maxwell-Poisson Distribution
Kimberly Sellers <kfs7@georgetown.edu> ; Luis Costa <lc436@georgetown.edu>
The main purpose of this package is to juxtapose the different control limits obtained by modelling a data set through the COM-Poisson distribution vs. the classical Poisson distribution. Accordingly, this package offers the ability to compute the COM-Poisson parameter estimates and plot associated Shewhart control charts for a given data set.

Interactive Document for Working with Confusion Matrix
An interactive document on  the topic of confusion matrix analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at <https://predanalyticssessions1.shinyapps.io/ConfusionMatrixShiny/>.

Representation Tool For Output Of Connectivity Map (CMap)
Analysis
Raphaël Bonnet [aut, cre], Jean-François Peyron[aut,ths]
Automatically displays graphical visualization for exported data table from Connectivity Map (CMap) (2006) <doi:10.1126/science.1132939>.
It allows the representation of the statistics (p-value and enrichment) according to each cell lines in the form of a bubble plot.
readxl, ggplot2, dplyr, scales, reshape2, stringr

Automatically displays graphical visualization for exported data table (permutated results) from Connectivity Map (CMap) (2006) <doi:10.1126/science.1132939>.
It allows the representation of the statistics (p-value and enrichment) according to each cell lines in the form of a bubble plot.

Matching Algorithms for Causal Inference with Clustered Data
c(person("Massimo", "Cannas", role = c("aut", "cre"),
email = "massimo.cannas@unica.it"),
person("Elena", "Colicino", role = "ctb",
email = "elena.colicino@mssm.edu"))
Massimo Cannas [aut, cre], Elena Colicino [ctb]
Provides functions to perform matching algorithms for causal inference with clustered data, as described in B. Arpino and M. Cannas (2016) <doi:10.1002/sim.6880>. Pure within-cluster and preferential-within cluster matching are implemented. Both algorithms provide causal estimates with cluster-adjusted estimates of standard errors.