bivariate.findCNVs
finds CNVs using read count information from both strands.
1 2 3 4 5  bivariate.findCNVs(binned.data, ID = NULL, eps = 0.1, init = "standard",
max.time = 1, max.iter = 1, num.trials = 1, eps.try = NULL,
num.threads = 1, count.cutoff.quantile = 0.999,
states = c("zeroinflation", paste0(0:10, "somy")),
most.frequent.state = "1somy", algorithm = "EM", initial.params = NULL)

binned.data 
A GRanges object with binned read counts. 
ID 
An identifier that will be used to identify this sample in various downstream functions. Could be the file name of the 
eps 
Convergence threshold for the BaumWelch algorithm. 
init 
One of the following initialization procedures:

max.time 
The maximum running time in seconds for the BaumWelch algorithm. If this time is reached, the BaumWelch will terminate after the current iteration finishes. Set 
max.iter 
The maximum number of iterations for the BaumWelch algorithm. Set 
num.trials 
The number of trials to find a fit where state 
eps.try 
If code num.trials is set to greater than 1, 
num.threads 
Number of threads to use. Setting this to >1 may give increased performance. 
count.cutoff.quantile 
A quantile between 0 and 1. Should be near 1. Read counts above this quantile will be set to the read count specified by this quantile. Filtering very high read counts increases the performance of the BaumWelch fitting procedure. However, if your data contains very few peaks they might be filtered out. Set 
states 
A subset or all of 
most.frequent.state 
One of the states that were given in 
algorithm 
One of 
initial.params 
A 
An aneuBiHMM
object.
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