The goal of ctbi is to clean, decompose, impute and aggregate univariate time series. Ctbi stands for *Cyclic/trend decomposition using bin interpolation* : the time series is divided into a sequence of non-overlapping bins. The long-term trend is a linear interpolation of the mean values between successive bins and the cyclic component is the mean stack of detrended data within all bins. Outliers present in the residuals are flagged using an enhanced Boxplot rule called LogBox, which replaces the original 1.5 constant with *k* × log (*n*) + 1 (*n* being the sample size, and *k* = 0.6 a default value). The strength of the cyclic pattern within each bin is quantified by a new metric, the Stacked Cycles Index (SCI), with SCI <= 0 associated with no cyclicity and SCI = 1 a perfectly cyclic signal.

You can install the development version of ctbi from GitHub with:

```
library(ctbi)
# example of contaminated sunspot data
example1 <- data.frame(year = 1700:1988,sunspot = as.numeric(sunspot.year))
example1[sample(1:289,30),'sunspot'] <- NA
example1[c(5,30,50),'sunspot'] <- c(-50,300,400)
example1 <- example1[-(70:100),]
bin.period <- 11 # aggregation performed every 11 years (the year is numeric here)
bin.side <- 1989 # to capture the last year, 1988, in a complete bin
bin.FUN <- 'mean'
bin.max.f.NA <- 0.2 # maximum of 20% of missing data per bin
ylim <- c(0,Inf) # negative values are impossible
list.main <- ctbi(example1,bin.period=bin.period,
bin.side=bin.side,bin.FUN=bin.FUN,
ylim=ylim,bin.max.f.NA=bin.max.f.NA)
data0.example1 <- list.main$data0 # cleaned raw dataset
data1.example1 <- list.main$data1 # aggregated dataset.
SCI.example1 <- list.main$SCI # this data set shows a moderate seasonality
mean.cycle.example1 <- list.main$mean.cycle # this data set shows a moderate seasonality
bin.size.example1 <- list.main$bin.size # 12 data points per bin on average (12 months per year)
```