Implementation of Taylor Regression Estimator (TRE), 
   Tulip Extreme Finding Estimator (TEFE), Bell Extreme Finding Estimator (BEFE),
   Integration Extreme Finding Estimator (IEFE) and 
   Integration Root Finding Estimator (IRFE) for roots, extrema and inflections of a curve .     
   Christopoulos, DT (2019) <doi:10.13140/RG.2.2.17158.32324> .
   Christopoulos, DT (2016) <doi:10.2139/ssrn.3043076> .
   Christopoulos, DT (2016) <https://demovtu.veltech.edu.in/wp-content/uploads/2016/04/Paper-04-2016.pdf> .
   Christopoulos, DT (2014) <doi:10.48550/arXiv.1206.5478> .

Routines to find the root of nonlinear functions, and to perform steady-state and equilibrium analysis of ordinary differential equations (ODE). 
  Includes routines that: (1) generate gradient and jacobian matrices (full and banded),
  (2) find roots of non-linear equations by the 'Newton-Raphson' method, 
  (3) estimate steady-state conditions of a system of (differential) equations in full, banded or sparse form, using the 'Newton-Raphson' method, or by dynamically running,
  (4) solve the steady-state conditions for uni-and multicomponent 1-D, 2-D, and 3-D partial differential equations, that have been converted to ordinary differential equations
    by numerical differencing (using the method-of-lines approach).
  Includes fortran code.

Selects one model with variable selection FDR controlled at a
    specified level. A q-value for each potential variable is also returned. The
    input, variable selection counts over many bootstraps for several levels of
    penalization, is modeled as coming from a beta-binomial mixture
    distribution.

The 'ropenblas' package (<https://prdm0.github.io/ropenblas/>) is useful for users of any 'GNU/Linux' distribution. It will be possible to download, compile and link the 'OpenBLAS' library (<https://www.openblas.net/>) with the R language, always by the same procedure, regardless of the 'GNU/Linux' distribution used. With the 'ropenblas' package it is possible to download, compile and link the latest version of the 'OpenBLAS' library even the repositories of the 'GNU/Linux' distribution used do not include the latest versions of 'OpenBLAS'. If of interest, older versions of the 'OpenBLAS' library may be considered. Linking R with an optimized version of 'BLAS' (<https://netlib.org/blas/>) may improve the computational performance of R code. The 'OpenBLAS' library is an optimized implementation of 'BLAS' that can be easily linked to R with the 'ropenblas' package.