calculate.bandwidths.by.resolution: Calculate temporal bandwidths by spatial resolution
In kernelPhil: Kernel Smoothing Tools for Philology and Historical Dialectology

This function calculates relationships between temporal bandwidth and possible spatial resolution for a given power and suggests minimum possible temporal bandwidth for a given resolution

calculate.bandwidths.by.resolution(
  dataset,
  dependent.variable = "dependent.variable",
  time = "year",
  weight = "weight",
  alpha = 0.05,
  margin = 0.1,
  measure.times,
  temporal.bandwidth.limits,
  temporal.bandwidth.n.levels = 200,
  minimum.spatial.resolution = 5,
  summary.plots = FALSE,
  kernel.function = gaussian.kernel
)

`dataset`	The dataset to be smoothed as a data.frame.
`dependent.variable`	String name of the column in dataset with the dependent variable (defaults to "dependent.variable"); this column should be numeric or factor.
`time`	String name of the column in dataset with the time variable (defaults to "year").
`weight`	String name of column in the dataset with numeric weights (defaults to "weight").
`alpha`	Numeric alpha for calculating error margins (defaults to 0.05).
`margin`	Numeric desired error margin for calculating spatial bandwidths (defaults to 0.1).
`measure.times`	A numeric vector of specific times at which to make estimates; if not given, will default to seq(from=min(time),to=max(time),length.out=5).
`temporal.bandwidth.limits`	Numeric vector of length 2 specifying minimum and maximum temporal bandwidth to be tested (defaults to the range of time0.01 to the range of time2).
`temporal.bandwidth.n.levels`	Number of distinct levels of temporal bandwidth to be tested (defaults to 200).
`minimum.spatial.resolution`	Numeric minimum spatial resolution.
`summary.plots`	If TRUE, plots of smoothed sample density, the dependent variable, and variance are returned along with the plot of resolution by bandwidth.
`kernel.function`	The kernel function, one of gaussian.kernel, gaussian.square.kernel, triangular.kernel, square.kernel, or a custom function (defaults to gaussian.kernel).