using WeightedOnlineStats

values = rand(100)
weights = rand(100)

# fit using arrays:
o1 = fit!(WeightedMean(), values, weights)

# fit using an iterator that returns a tuple (value, weight):
o2 = fit!(WeightedMean(), zip(values, weights))

# fit single values at a time:
o3 = WeightedMean()
for i in 1:length(values)
    fit!(o3, values[i], weights[i])
end

mean(o1)
mean(o2)
mean(o3)