Conditional Density Estimation with Heteroskedasticity

Usage

lnr_heteroskedastic_density(
  data,
  formula,
  mean_lnr,
  var_lnr,
  mean_lnr_args = NULL,
  var_lnr_args = NULL,
  density_args = NULL
)

data: A dataframe to train a learner / learners on.
formula: A regression formula to use inside this learner.
mean_lnr: A learner (function) passed in to be trained on the data with the given formula and then used to predict conditional means for provided newdata.
var_lnr: A learner (function) passed in to be trained on the squared error from the mean_lnr on the given data and then used to predict the expected variance for the density distribution of the outcome centered around the predicted conditional mean in the output.
mean_lnr_args: Extra arguments to be passed to the mean_lnr
var_lnr_args: Extra arguments to be passed to the var_lnr
density_args: Extra arguments to be passed to the kernel density smoother stats::density, especially things like bw for specifying the smoothing bandwidth. See ?stats::density.

a closure (function) that produces density estimates at the newdata given according to the fit model.