CFtime 1.4.1

• Method slab() has an additional argument rightmost.closed to indicate if the upper extreme value should be included in the result.
• Fixed bounds information on CFtime instances returned from methods indexOf() and slab().
• Several minor code improvements.

CFtime 1.4.0

• Bounds that define intervals around offsets can be associated with a CFtime instance and retrieved as raw offset values or as formatted timestamps.
• Methods that subset a CF time series (e.g. CFfactor(), cut(), slab()) now have an attribute “CFtime” (among possible others) that describes the “time” dimension of the analysis result applying the subset. In other words, if CFtime instance ‘Acf’ describes the temporal dimension of data set ‘A’ and a factor ‘Af’ is generated from ‘Acf’, then Bcf <- attr(Af, "CFtime") describes the temporal dimension of the result of, say, B <- apply(A, 1:2, tapply, Af, FUN).
• New indexOf() method added that returns the indices of supplied timestamps in a CFtime instance, optionally with a fractional part. This can be used to extract specific time steps, or to interpolate between time steps using the fractional part, from the time dimension of the data set associated with the CFtime instance. A vector of indices (e.g. referring to slices of the data set) can also be supplied, in which case valid indices are returned, with the new CFtime instance.
• New cut() method added to generate a factor, similar to cut.POSIXt() but with some differences in the arguments.
• CFfactor() now supports a period “quarter”, for calendar quarters.
• format() method added that generates a character vector of timestamps for the offsets in a CFtime instance. The format is specified using the flags used in strptime(), with some limitations. In particular, locale-specific formatting is limited to month names and no weekday information can be generated. The range() method has a new “format” parameter to support the same functionality and timestamps can also be generated for the extremes of the bounds, if set.
• as_character() and length() methods added that return a vector of timestamps or the number of offsets in a CFtime instance, respectively.
• Several functions have been renamed (most notably CFtimestamp() to as_timestamp(), CFcomplete() to is_complete(), CFrange() to the standard generic method range(), and CFsubset() to slab()) to be more consistent with the R universe. The original functions are now flagged as being deprecated. Some datum functions (deep down where regular mortals do not dwell) have been deleted.
• Time zone designator “UTC” accepted when parsing timestamps to offsets.
• Minor code fixes, see GitHub commits.
• Documentation updated, with description of new functions.

CFtime 1.3.0

• Two CFtime instances can be added if they have compatible calendars and units. The earlier origin is preserved in the result and offsets from the later instance are incremented by the appropriate amount. As before, in the result offsets are in the order of the specified CFtime instances.
• A new function CFsubset() can be used to retrieve a logical vector that indicates which time steps in a CFtime instance fall between two timestamps. This is useful to slice data during reading from file or analysis.
• Time zone information is managed at the level of the datum. If a vector of character timestamps is parsed and found to have different time zones, a warning is generated.
• Much reduced memory footprint.
• Minor code fixes, see GitHub commits.
• Codecov.io test coverage monitoring added.
• Documentation updated, with description of new functions.

CFtime 1.2.0

• Datum units “years” and “months” added. While these units are discouraged by the CF Metadata Conventions due to their problematic definition, there are quite a few data sets out there that use these units nonetheless. For this reason, reading existing files with such datum units is supported (converting offsets to time elements is easy) but parsing timestamps is not (calculating offsets from time elements is possible but tedious and slow). Should there be a definite need, open an issue on GitHub and make a very good case why this functionality is required.
• CFresolution() returns the average separation between elements in a time series, in units of the datum.
• CFcomplete() indicates if the time series is complete, meaning that there are no gaps in the time series. This also works for time series with a somewhat variable length such as monthly data with a “days” datum unit. This works for all but the most exotic time dimension constructions.
• CFtimestamp() produces a timestamp for all midnight values if the datum unit is “hours”, “minutes” or “seconds”. The “time” format has been removed. For “standard”, “gregorian” and “proleptic_gregorian” calendars output can be generated as POSIXct by specifying the new argument asPOSIX = TRUE – defaults to FALSE, the previous behaviour so the API is not broken.
• Minor documentation updates.
• Assorted minor code fixes, see GitHub commits.

CFtime 1.1.0

• CFtime() can now also be invoked with a vector of character timestamps as offsets, or with a single timestamp to create a complete time series from the datum to the indicated timestamp.
• CFtimestamp() can now automatically select the best format for the time series.
• New CFfactor_units() and CFfactor_coverage() functions. CFfactor_units() will tell you how many time units compose every level of a factor. CFfactor_coverage() computes the actual or relative number of time units in the factor levels from the time series in a CFtime instance with which the factor was created. This will enable you to assess the completeness of your time series (and perhaps filter out factor levels below a certain coverage threshold) and it can be useful in computing absolute values from average values, as is often useful when computing anomalies.
• Global constants are now defined in a package environment, CFt.
• Documentation expanded, updated and fixed.
• Assorted minor code fixes, see GitHub commits.

CFtime 1.0.0

• This version supports all CF Metadata Conventions calendars for use with climate projection data.
• You can create timestamps from the offsets in the files and create factors that greatly simplify working with climate change data.