Tools to run system processes in the background.  It can
    check if a background process is running; wait on a background process
    to finish; get the exit status of finished processes; kill background
    processes. It can read the standard output and error of the processes,
    using non-blocking connections. 'processx' can poll a process for
    standard output or error, with a timeout. It can also poll several
    processes at once.

Portable '/proc/self/maps' as a data frame.
    Determine which library or other region is mapped to a specific
    address of a process. --
    R packages can contain native code, compiled to shared libraries at build or
    installation time.
    When loaded, each shared library occupies a portion of the address space of
    the main process.
    When only a machine instruction pointer is available (e.g. from a backtrace
    during error inspection or profiling), the address space map determines
    which library this instruction pointer corresponds to.

Estimates corrected Procrustean correlation between matrices for removing overfitting effect. Coissac Eric and Gonindard-Melodelima Christelle (2019) <doi:10.1101/842070>.

Contains functions to simulate the most commonly used 
    'SAS®' procedures.  Specifically, the package aims to 
    simulate the functionality of 'proc freq', 'proc means', 'proc ttest',
    'proc reg', 'proc transpose', 'proc sort', and 'proc print'. 
    The simulation will include recreating 
    all statistics with the highest fidelity possible.

A direct and flexible method for estimating an ICA model. This approach estimates the densities for each component directly via a tilted Gaussian. The tilt functions are estimated via a GAM Poisson model. Details can be found in "Elements of Statistical Learning (2nd Edition)" in Section 14.7.4.

Implements the methods proposed by Olley, G.S. and Pakes, A. (1996) <doi:10.2307/2171831>, Levinsohn, J. and Petrin, A. (2003) <doi:10.1111/1467-937X.00246>, Ackerberg, D.A. and Caves, K. and Frazer, G. (2015) <doi:10.3982/ECTA13408> and Wooldridge, J.M. (2009) <doi:10.1016/j.econlet.2009.04.026> for structural productivity estimation .