This is a function built for doing data generation and variable selection using functional lars with different settings and data with different correlation structures.

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`seed` |
Set the seed for random numbers. |

`nsamples` |
Sample size of the data to generate. |

`nTrain` |
Sample size of the training data. |

`var_type` |
Two choices of the variable types. See details for more information. |

`cor_type` |
Correlation structures. See details for more information. |

`VarThreshold0` |
Threshold for removing variables based on variation explained. See |

`SignThreshold0` |
Same as |

`lasso` |
Use lasso modification or not. In other words, can variables selected in the former iterations be removed in the later iterations. |

`check` |
Type of lasso check. 1 means variance check, 2 means sign check. |

`uncorr` |
If the variables are uncorrelated or not. See details for more information. |

`nVar` |
Number of variables to generate. |

`Discrete_Norm_ID` |
Which discrete method and which norm to use. 1 to 12. |

`NoRaw_max` |
Number of variables to select when not using RDP discretising method. |

`raw_max` |
Number of variables to select when using RDP discretising method. |

`hyper` |
Hyper parameters used in the Gaussian process. GP is used for building the covariance structure of the functional variables. |

`RealX` |
Real data input X. |

`RealY` |
Real data input Y. |

`dataL` |
Real input data list rather than generate in the function. It should has the same structure as that generated. |

`nCor` |
Number of cores to use. |

`control` |
List of control items. See |

A list of results using different normalization methods and different representation methods for the functional coefficients.

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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