generate.boot: Generate and calculate bootstrap means for all clusters
In hdng/clonevol: Clonal ordering and visualization

Generate and calculate bootstrap means for all clusters

generate.boot(variants, cluster.col.name = "cluster",
  cluster.center.method = "mean", vaf.col.names = NULL,
  depth.col.names = NULL, vaf.in.percent = TRUE, num.boots = 1000,
  bootstrap.model = "non-parametric", num.bernoulli.trials = "mean",
  weighted = FALSE, zero.sample = NULL, random.seed = NULL)

`variants:`	data frame of variants with cluster assignments and VAF of samples. Columns are c('cluster', 'sample1vaf', 'sample2vaf', ..., 'sample1depth', 'sample2depth', ....). If possible, reduce input file to only necessary columns. For unweighted resampling, columns will be assumed to be VAF columns if not explicitly specified.
`cluster.col.name:`	name of column containing cluster information. Must be specified. Default: 'cluster'.
`vaf.col.names:`	names of columns containing VAFs for each sample. Default NULL. If weighted=FALSE and no VAF columns are specified, every column except the cluster column will be treated as VAF columns.
`depth.col.names:`	names of columns containing depth for each sample. Default NULL. If weighted=TRUE, depth column names must all be specified.
`vaf.in.percent:`	If TRUE, VAFs will be converted to proportions between 0 and 1. Default TRUE.
`num.boots:`	Number of times to resample. Default 1000.
`bootstrap.model:`	specifies the statistical model used in bootstrap resampling. Model can be normal, normal-truncated, beta, binomial, beta-binomial, or non-parametric. Default: 'non-parametric'.
`num.bernoulli.trials:`	the number of Bernoulli trials to perform when using the beta-binomial model. For weighted analysis, cluster 'mean' depth is the default. Otherwise, the user should specify what depth to use as a numerical scalar or vector of depths for each cluster. If a scalar, that value will be used for all clusters in all samples. If a vector, the length must match the number of clusters times the number of samples.
`weighted:`	If TRUE, weights variants proportionally to read count. If TRUE, VAF and depth cluster columns must be specified. Default: FALSE.
`zero.sample:`	The sample of zero vaf (to use to compare with other clusters to determine if the cluster should be considered zero VAF, and not included in the models)