time series analysis for a time series without a regular frequency

I am trying to conduct a time series analysis on historic hydrologic data,
but I cannot coerce it into class ts because it does not have regular
sampling intervals (some years have 20 samples, other have 8). Specifically
I am trying to perform a CUSUM or or other step change detection, but the
packages all seem to require data as ts.
As Gabor already pointed out: the zoo package can be used to store such 
data.

If you want to use the strucchange package for detecting changes then you 
can do two things:
   -  use a plain data.frame (without ts or some other time
      series class) which can be easily produced via
      as.data.frame(zoo_obj).
      Then the axis annotation in graphics won't be the time axis but
      the standard unit interval (= proportion of data). This is not so
      pretty but all statistical interpretations are still correct.
   - If zoo_obj is just a univariate series and you want to conduct a
     CUSUM test for a change in the mean you can do
       cus <- gefp(zoo_obj ~ 1)
       plot(cus)
       plot(cus, functional = meanL2BB)
     and so on. gefp() is the only function in strucchange that
     automatically supports "zoo".

hth,
Z
Is there a way to coerce my data into ts while maintaining all of my
samples?

Or alternatively, can someone recommend a package that does not require data
as ts?

Thanks!

-- 
Erin Hestir
Center for Spatial Technology and Remote Sensing
University of California Davis

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time series analysis for a time series without a regular frequency

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