npCBPS.fit

1 | ```
npCBPS.fit(treat, X, corprior, print.level, ...)
``` |

`treat` |
A vector of treatment assignments. Binary or multi-valued treatments should be factors. Continuous treatments should be numeric. |

`X` |
A covariate matrix. |

`corprior` |
Prior hyperparameter controlling the expected amount of correlation between each covariate and the treatment. Specifically, the amount of correlation between the k-dimensional covariates, X, and the treatment T after weighting is assumed to have prior distribution MVN(0,sigma^2 I_k). We conceptualize sigma^2 as a tuning parameter to be used pragmatically. It's default of 0.1 ensures that the balance constraints are not too harsh, and that a solution is likely to exist. Once the algorithm works at such a high value of sigma^2, the user may wish to attempt values closer to 0 to get finer balance. |

`print.level` |
Controls verbosity of output to the screen while npCBPS runs. At the default of print.level=0, little output is produced. It print.level>0, it outputs diagnostics including the log posterior (log_post), the log empirical likelihood associated with the weights (log_el), and the log prior probability of the (weighted) correlation of treatment with the covariates. |

`...` |
Other parameters to be passed. |

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