Estimation, testing and regression modeling of
 subdistribution functions in competing risks, as described in Gray
 (1988), A class of K-sample tests for comparing the cumulative
 incidence of a competing risk, Ann. Stat. 16:1141-1154
 <DOI:10.1214/aos/1176350951>, and Fine JP and
 Gray RJ (1999), A proportional hazards model for the subdistribution
 of a competing risk, JASA, 94:496-509, <DOI:10.1080/01621459.1999.10474144>.

Uses inverse probability weighting methods to estimate treatment effect under marginal structure model for the cause-specific hazard of competing risk events. Estimates also the cumulative incidence function (i.e. risk) of the potential outcomes, and provides inference on risk difference and risk ratio. Reference: Kalbfleisch & Prentice (2002)<doi:10.1002/9781118032985>; Hernan et al (2001)<doi:10.1198/016214501753168154>.

Estimation, testing and regression modeling of
 subdistribution functions in competing risks using quantile regressions,
 as described in Peng and Fine (2009) <DOI:10.1198/jasa.2009.tm08228>.

This is an open-source implementation of the Congruent Matching Profile 
    Segments (CMPS) method (Chen et al. 2019)<doi:10.1016/j.forsciint.2019.109964>. 
    In general, it can be used for objective comparison of striated tool marks, and in our 
    examples, we specifically use it for bullet signatures comparisons. The CMPS score is 
    expected to be large if two signatures are similar. So it can also be considered as a 
    feature that measures the similarity of two bullet signatures.

Computes maximum response from Cardiac Magnetic Resonance Images using spatial and voxel wise spline based Bayesian model. This is an implementation of the methods described in Schmid (2011) <doi:10.1109/TMI.2011.2109733> "Voxel-Based Adaptive Spatio-Temporal Modelling of Perfusion Cardiovascular MRI". IEEE TMI 30(7) p. 1305 - 1313.

A collection of useful helper routines developed by
  students of the Center for Mathematical Research, Stankin,
  Moscow.