Description Usage Arguments Value References Examples

Adapted maximum entropy bootstrap routine from `meboot`

https://cran.r-project.org/package=meboot.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 |

`x` |
vector of data. |

`reps` |
numeric; number of replicates to generate. |

`rho` |
numeric [0,1]; The default setting |

`type` |
options("spearman", "pearson", "NNScor", "NNSdep"); |

`drift` |
logical; |

`trim` |
numeric [0,1]; The mean trimming proportion, defaults to |

`xmin` |
numeric; the lower limit for the left tail. |

`xmax` |
numeric; the upper limit for the right tail. |

`reachbnd` |
logical; If |

`expand.sd` |
logical; If |

`force.clt` |
logical; If |

`scl.adjustment` |
logical; If |

`sym` |
logical; If |

`elaps` |
logical; If |

`colsubj` |
numeric; the column in |

`coldata` |
numeric; the column in |

`coltimes` |
numeric; an optional argument indicating the column that contains the times at which the observations for each individual are observed. It is ignored if the input data |

`...` |
possible argument |

x original data provided as input.

replicates maximum entropy bootstrap replicates.

ensemble average observation over all replicates.

xx sorted order stats (xx[1] is minimum value).

z class intervals limits.

dv deviations of consecutive data values.

dvtrim trimmed mean of dv.

xmin data minimum for ensemble=xx[1]-dvtrim.

xmax data x maximum for ensemble=xx[n]+dvtrim.

desintxb desired interval means.

ordxx ordered x values.

kappa scale adjustment to the variance of ME density.

elaps elapsed time.

Vinod, H.D. and Viole, F. (2020) Arbitrary Spearman's Rank Correlations in Maximum Entropy Bootstrap and Improved Monte Carlo Simulations https://www.ssrn.com/abstract=3621614

Vinod, H.D. (2013), Maximum Entropy Bootstrap Algorithm Enhancements. https://www.ssrn.com/abstract=2285041.

Vinod, H.D. (2006), Maximum Entropy Ensembles for Time Series Inference in Economics,

*Journal of Asian Economics*,**17**(6), pp. 955-978.Vinod, H.D. (2004), Ranking mutual funds using unconventional utility theory and stochastic dominance,

*Journal of Empirical Finance*,**11**(3), pp. 353-377.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
## Not run:
# To generate an orthogonal rank correlated time-series to AirPassengers
boots <- NNS.meboot(AirPassengers, reps=100, rho = 0, xmin = 0)
# Verify correlation of replicates ensemble to original
cor(boots$ensemble, AirPassengers, method = "spearman")
# Plot all replicates
matplot(boots$replicates, type = 'l')
# Plot ensemble
lines(boots$ensemble, lwd = 3)
## End(Not run)
``` |

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