Adaptive Lasso for the Cox Regression with Interval Censored and
Possibly Left Truncated Data
Penalized variable selection tools for the Cox
proportional hazards model with interval censored and possibly
left truncated data. It performs variable selection via
penalized nonparametric maximum likelihood estimation with an
adaptive lasso penalty. The optimal thresholding parameter can be
searched by the package based on the profile Bayesian information
criterion (BIC). The asymptotic validity of the methodology is
established in Li et al. (2019 <doi:10.1177/0962280219856238>).
The unpenalized nonparametric maximum likelihood estimation for
interval censored and possibly left truncated data is also
available.

Implements Anderson-Moore algorithm for solving linear
rational expectations models.  For information about the
algorithm and its uses, please see
http://www.federalreserve.gov/pubs/oss/oss4/aimindex.html.
This version works on both Unix and Windows.  For questions
about the algorithm and its implementation/applications, please
contact gary.anderson@frb.gov.  If you are having technical
issues with the package, please contact
aneesh.raghunandan@yale.edu.
The "C" code, sparseAim.c, implementing the basic algorithm
is in the public domain and may be used freely.  However, the
authors would appreciate acknowledgement of the source by
citation of any of the following papers: Anderson, G. and
Moore, G. "A Linear Algebraic Procedure For Solving Linear
Perfect Foresight Models."  Economics Letters, 17, 1985.
Anderson, G. "Solving Linear Rational Expectations Models: A
Horse Race." Computational Economics, 2008, vol. 31, issue 2,
pp. 95-113 Anderson, G. "A Reliable and Computationally
Efficient Algorithm for Imposing the Saddle Point Property in
Dynamic Models." Journal of Economic Dynamics and Control,
2010, vol 34, issue 3, pp. 472-489. \n

Compare Gene Expressions from 2-Treatment RNA-Seq Experiments
An Approximated Most Average Powerful Test with Optimal
FDR Control with Application to RNA-seq Data