Function to incorporate information on the low-r behaviour of G(r) into the Bayesian model.

1 2 |

`data` |
an object of type |

`r1,r2` |
numeric vectors, specify grids on which the G(r) behaviour is controlled. |

`rho.0` |
numeric, atomic number density of the material: a number of atoms per unit cell divided by a volume of the unit cell. |

`type1, type2` |
characters, specify the way to control the behavior of G(r). See details. |

`sigma.f, l` |
numerics or numeric vectors, specify parameters for a squared-exponential covariance function. |

`type1`

can be either "gaussianNoise" or "correlatedNoise". G(r) is restricted to the *-4πρ.0r1* line plus independent Gaussian noise or correlated Gaussian noise, respectively.

`type2`

can be either "secondDeriv" or "gaussianProcess" to impose smootheness conditions over the interval `r2`

. If `type2`

is "secondDeriv", a minimum of the second derivative is sought. If `type2`

is "gaussianProcess", the smoothness is controlled via the Gaussian process using parameters sigma.f and l.

According to our experience, the most efficient way is to impose `type1="gaussianNoise"`

and `type2=NA`

conditions.

An object of type `data`

.

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

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