The 'dfmirroR' package allows users to input a data frame, simulate some number of observations based on specified columns of that data frame, and then outputs a string that contains the code to re-create the simulation. The goal is to both provide workable test data sets and provide users with the information they need to set up reproducible examples with team members. This package was created out of a need to share examples in cases where data are private and where a full data frame is not needed for testing or coordinating.

Efficient estimation of Dynamic Factor Models using the Expectation Maximization (EM) algorithm 
  or Two-Step (2S) estimation, supporting datasets with missing data. Factors are assumed to follow a stationary VAR 
  process of order p. The estimation options follow advances in the econometric literature: either running the Kalman 
  Filter and Smoother once with initial values from PCA - 2S estimation as in Doz, Giannone and Reichlin (2011) 
  <doi:10.1016/j.jeconom.2011.02.012> - or via iterated Kalman Filtering and Smoothing until EM convergence - following 
  Doz, Giannone and Reichlin (2012) <doi:10.1162/REST_a_00225> - or using the adapted EM algorithm of Banbura and 
  Modugno (2014) <doi:10.1002/jae.2306>, allowing arbitrary patterns of missing data. The implementation makes heavy 
  use of the 'Armadillo' 'C++' library and the 'collapse' package, providing for particularly speedy estimation. 
  A comprehensive set of methods supports interpretation and visualization of the model as well as forecasting. 
  Information criteria to choose the number of factors are also provided - following Bai and Ng (2002) 
  <doi:10.1111/1468-0262.00273>.

Phase I/II adaptive dose-finding design for single-agent
   Molecularly Targeted Agent (MTA), according to the paper "Phase
   I/II Dose-Finding Design for Molecularly Targeted Agent: Plateau
   Determination using Adaptive Randomization", Riviere Marie-Karelle et
   al. (2016) <doi:10.1177/0962280216631763>.