# Changing sensitivity (cval) in tsoutliers resulting in unexpected results

I am using the excellent tsoutliers R package to detect outliers (additive outliers, temporary changes etc.), but the cval parameter in the tso function is providing me with inconsistent, or at least counter-intuitive results. I was under the impression that a lower value for cval would include more outliers (but possibly also irrelevant ones), but this doesn't always seem the case. For example, using the following data and logic, I get the following output, which is nearly exactly what I was after:

data <- c(121.54, 119.79, 119.18, 118.56, 104, 65.52, 66, 119.18,
123.42, 119.18, 118.56, 99, 61.74, 67.98, 119.18, 123.42, 120.36,
115.14, 98, 62.37, 67.98, 122.72, 121, 116.82, 117.42, 98, 83.538,
103.096, 165.332, 185.6955, 145.848, 129.162, 101, 62.37, 64.68,
115.64, 124.63, 115.64, 118.56, 102, 62.37, 67.32, 115.64, 122.21,
121.54, 114, 103, 62.37, 65.34, 118, 122.21, 119.18, 114, 99, 65.52,
65.34, 118, 122.21, 115.64, 117.42, 73.5, 40.131, 41.184, 79.4376,
95.832, 105.138, 117.42, 100, 63, 66.66, 122.72, 123.42, 116.82, 114,
98, 61.74, 64.68, 116.82, 121, 188.152, 114, 99, 61.74, 66, 122.72,
118.58, 115.64, 112.86, 101, 63.63, 66.66)

# simple tso function
volume <- ts(data, start = c(2016,1,1), frequency = 7)
data.ts.outliers <- tso(volume, types = c("AO", "LS", "TC"), cval = 3.0)
data.ts.outliers
plot(data.ts.outliers)


However, using cval = 2.9, as well as most other values for cval above or below 3.0, I get the following results, which is missing some key outliers:

As I want to use this package without manual review for each timeseries, Ideally I'd be able to use a slightly lower cval value to ensure I am capturing most outliers, but the inconsistency that I am seeing is not allowing for this. Anything I am missing?