Auxiliary function as user interface for finetuning RDS.bootstrap.intervals algorithm, which computes interval estimates for via bootstrapping.
1 2 3  control.rds.estimates(confidence.level = 0.95, SS.infinity = 0.01,
lowprevalence = c(8, 14), discrete.cutoff = 0.8, useC = TRUE,
number.of.bootstrap.samples = NULL, seed = NULL)

confidence.level 
The confidence level for the confidence intervals. The default is 0.95 for 95%. 
SS.infinity 
The sample proportion, 
lowprevalence 
Standard confidence interval procedures can be inaccurate when the
outcome expected count is close to zero. This sets conditions where alternatives to the
standard are used for the 
discrete.cutoff 
The minimum proportion of the values of the outcome variable that need to be unique before the variable is judged to be continuous. 
useC 
Use a Clevel implementation of Gile's bootstrap (rather than the R level). The implementations should be computational equivalent (except for speed). 
number.of.bootstrap.samples 
The number of bootstrap samples to take
in estimating the uncertainty of the estimator. If 
seed 
Seed value (integer) for the random number generator. See

This function is only used within a call to the RDS.bootstrap.intervals
function.
Some of the arguments are not yet fully implemented. It will evolve slower to incorporate more arguments as the package develops.
Standard confidence interval procedures can be inaccurate when the
outcome expected count is close to zero. In these cases
the combined AgrestiCoull and the bootstrapt interval of
Mantalos and Zografos (2008) can be used.
The lowprevalence
argument is a
two vector parameter setting the conditions under which the approximation is used.
The first is the penalty term on the differential activity. If the observed number
of the rare group minus the product of the first parameter and the differential
activity is lower than the
second parameter, the low prevalence approximation is used.
A list with arguments as components.
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