Efficient estimation of Dynamic Factor Models using the Expectation Maximization (EM) algorithm 
  or Two-Step (2S) estimation, supporting datasets with missing data and mixed-frequency nowcasting applications. 
  Factors follow a stationary VAR process of order p. Estimation options include: running the Kalman Filter and 
  Smoother once with PCA initial values (2S) as in Doz, Giannone and Reichlin (2011) <doi:10.1016/j.jeconom.2011.02.012>;
  iterated Kalman Filtering and Smoothing until EM convergence as in Doz, Giannone and Reichlin (2012) 
  <doi:10.1162/REST_a_00225>; or the adapted EM algorithm of Banbura and Modugno (2014) <doi:10.1002/jae.2306>, 
  allowing arbitrary missing-data patterns and monthly-quarterly mixed-frequency datasets. The implementation uses 
  the 'Armadillo' 'C++' library and the 'collapse' package for fast estimation. A comprehensive set of methods supports 
  interpretation and visualization, forecasting, and decomposition of the 'news' content of macroeconomic data releases 
  following Banbura and Modugno (2014). 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>.

Tools to identify, quantify, analyze, and visualize growth
    suppression events in tree rings that are often produced by insect
    defoliation. Described in Guiterman et al. (2020) <doi:10.1016/j.dendro.2020.125750>.

Derivative-Free optimization algorithms. These algorithms do not require gradient information. More importantly, they can be used to solve non-smooth optimization problems.

Provides R-implementation of Decision forest algorithm, which combines the predictions of
    multiple independent decision tree models for a consensus decision. In particular, Decision Forest is a novel 
    pattern-recognition method which can be used to analyze: (1) DNA microarray data; 
    (2) Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry  (SELDI-TOF-MS) data; and 
    (3) Structure-Activity Relation (SAR) data.
    In this package, three fundamental functions are provided, as (1)DF_train, (2)DF_pred, and (3)DF_CV.  
    run Dforest() to see more instructions.
    Weida Tong (2003) <doi:10.1021/ci020058s>.