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This function obtains a Random Number Generator (RNG) or collection of RNGs that replicate the required parameter(s) of a distribution for a time series of data. Consider the case of reproducing a time series data set of size 20 that uses an autoregressive (AR) model with phi = 0.8 and standard deviation equal to 1. When one checks the arima.sin() function's estimated parameters, it's possible that after a single trial or a few more, one won't find the precise parameters. This enables one to look for the ideal RNG setting for a simulation that will accurately duplicate the desired parameters.
| Version: | 0.1.0 |
| Depends: | R (≥ 4.2.0) |
| Imports: | forecast, foreach, parallel, doParallel, future, stats, tibble |
| Suggests: | knitr, testthat (≥ 3.0.0) |
| Published: | 2023-01-23 |
| Author: | Daniel James [cre, aut], Ayinde Kayode [aut] |
| Maintainer: | Daniel James <futathesis at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | paramsim results |
| Reference manual: | paramsim.pdf |
| Vignettes: |
paramsim |
| Package source: | paramsim_0.1.0.tar.gz |
| Windows binaries: | r-devel: paramsim_0.1.0.zip, r-release: paramsim_0.1.0.zip, r-oldrel: paramsim_0.1.0.zip |
| macOS binaries: | r-release (arm64): paramsim_0.1.0.tgz, r-oldrel (arm64): paramsim_0.1.0.tgz, r-release (x86_64): paramsim_0.1.0.tgz, r-oldrel (x86_64): paramsim_0.1.0.tgz |
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These binaries (installable software) and packages are in development.
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