NMOF: Numerical Methods and Optimization in Finance
Functions, examples and data from the first and
the second edition of "Numerical Methods and Optimization in
Finance" by M. Gilli, D. Maringer and E. Schumann (2019,
ISBN:978-0128150658). The package provides implementations
of optimisation heuristics (Differential Evolution, Genetic
Algorithms, Particle Swarm Optimisation, Simulated Annealing
and Threshold Accepting), and other optimisation tools, such
as grid search and greedy search. There are also functions
for the valuation of financial instruments such as bonds and
options, for portfolio selection and functions that help
with stochastic simulations.
| Version: |
2.10-0 |
| Depends: |
R (≥ 3.5) |
| Imports: |
grDevices, graphics, parallel, stats, utils |
| Suggests: |
MASS, PMwR, RUnit, Rglpk, datetimeutils, openxlsx, quadprog, readxl, tinytest, zoo |
| Published: |
2024-10-20 |
| DOI: |
10.32614/CRAN.package.NMOF |
| Author: |
Enrico Schumann 
[aut, cre] |
| Maintainer: |
Enrico Schumann <es at enricoschumann.net> |
| License: |
GPL-3 |
| URL: |
https://enricoschumann.net/NMOF.htm , https://gitlab.com/NMOF ,
https://git.sr.ht/~enricoschumann/NMOF ,
https://github.com/enricoschumann/NMOF |
| NeedsCompilation: |
no |
| Classification/JEL: |
C61, C63 |
| Citation: |
NMOF citation info |
| Materials: |
README NEWS |
| In views: |
Finance, Optimization, ReproducibleResearch |
| CRAN checks: |
NMOF results |
Documentation:
| Reference manual: |
NMOF.pdf |
| Vignettes: |
An Overview of the NMOF Package (source, R code)
Fitting the Nelson–Siegel–Svensson model with Differential Evolution (source, R code)
Solving the N-Queens Problem with Local Search (source, R code)
Asset selection with Local Search (source, R code)
Robust Regression with Particle Swarm Optimisation and Differential Evolution (source, R code)
Portfolio Optimisation with Threshold Accepting (source, R code)
Functions for portfolio selection (source, R code)
Examples for the qTable function (source, R code)
Repairing solutions (source, R code)
Vectorised objective functions (source, R code)
|
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