`Duff.SingleStats`

— Type```
SingleStats(d::Int)
SingleStats(s::Vector{Float64}, q::Vector{Float64}, n::Vector{Int})
Initiates statistics for a DAF with a tracking only single variable
`s` holds a the sum of contributions of each sample
`q` holds a the sum of squares of contributions of each sample
`n` holds the number of times each sample is updated
```

`HypothesisTests.UnequalVarianceTTest`

— Type```
meanscore(d::Daf)
return the basic DAf score --- difference of means when features is present and absent
```

`Duff.getmask`

— Method```
getmask(d::Daf,p)
create mask with `p` fraction of samples present
```

`Duff.meanscore`

— Method```
meanscore(d::Daf)
return the basic DAf score --- difference of means when features is present and absent
```

`Duff.onlinedaf!`

— Method```
onlinedaf!(daf, onestep, p, n, warmup::Int = 0)
opdate daf `n`-times with mask containing `p` fraction of features
`onestep` is a function `(mask) -> f` which accepts mask as a feature
and return value of the criterion used in daf
```

`Duff.update!`

— Method```
update!(s::SingleStats,f,mask,negate::Bool)
update!(s::SingleStats,f,idx,negate::Bool)
updates the stats with a value `f` contributing to samples in from `idxs` or `mask`. If `negate` is true, `mask`
is negated / `idxs` are set to complement of `idxs`
```