Implements the fast iterative shrinkage-thresholding algorithm
             (FISTA) algorithm to fit a Gamma distribution with an elastic net 
             penalty as described in Chen, Arakvin and Martin (2018) 
             <arxiv:1804.07780>. An implementation for the case of the 
             exponential distribution is also available, with details available 
             in Chen and Martin (2018) <https://papers.ssrn.com/abstract_id=3085672>.

Computes the influence functions time series of the returns for the risk and 
             performance measures as mentioned in Chen and Martin (2018) 
             <https://www.ssrn.com/abstract=3085672>, as well as in Zhang et al. (2019)
             <https://www.ssrn.com/abstract=3415903>. Also evaluates estimators influence
             functions at a set of parameter values and plots them to display the shapes of 
             the influence functions.

Implements the methodology of "Cannings, T. I. and Samworth, R. J. (2017) Random-projection ensemble classification, J. Roy. Statist. Soc., Ser. B. (with discussion), 79, 959--1035". The random projection ensemble classifier is a general method for classification of high-dimensional data, based on careful combination of the results of applying an arbitrary base classifier to random projections of the feature vectors into a lower-dimensional space. The random projections are divided into non-overlapping blocks, and within each block the projection yielding the smallest estimate of the test error is selected. The random projection ensemble classifier then aggregates the results of applying the base classifier on the selected projections, with a data-driven voting threshold to determine the final assignment.

Estimates of standard errors of popular risk and performance 
             measures for asset or portfolio returns using methods as described in 
             Chen and Martin (2021) <doi:10.21314/JOR.2020.446>.

This reduced piecewise exponential survival software implements the likelihood ratio test and backward elimination procedure in Han, Schell, and Kim (2012 <doi:10.1080/19466315.2012.698945>, 2014 <doi:10.1002/sim.5915>), and Han et al. (2016 <doi:10.1111/biom.12590>). Inputs to the program can be either times when events/censoring occur or the vectors of total time on test and the number of events. Outputs of the programs are times and the corresponding p-values in the backward elimination. Details about the model and implementation are given in Han et al. 2014. This program can run in R version 3.2.2 and above.