eqm_bias_correction: Calibrating the bias of observed data by empirical quantile...
In jieyu97/QCwind: Quality control procedures for citizen science wind observations

Performing empirical quantile distribution mapping to correct bias in the observation data, the "truth" quantiles are given as reference data.

eqm_bias_correction(
  train.obs,
  train.datetime,
  test.obs,
  test.datetime,
  true.quantiles
)

`train.obs`	a numeric vector of training observed data to build the bias correction model.
`train.datetime`	a sequence of timestamps in `train.obs`.
`test.obs`	a numeric vector of test observed data to be calibrated, could be the same as `train.obs`.
`test.datetime`	a sequence of timestamps in `test.obs`.
`true.quantiles`	a list of six numeric vectors of the "truth" quantiles to build the bias correction model, the corresponding cumulative probability stamps must be evenly spreaded between 0 and 1. If not satisfied, a pseudo "truth" observation numeric vector satisfying the quantiles should be provided. We suggest using quantiles with cumulative probability from 0.01 to 1 with interval length 0.01 (100 quantiles in total). We use Weibull distribution with maximum likelihood estimation to fit these quantiles (in R package fitdistrplus).