A toolkit for Partially Observed Markov Decision Processes (POMDP). Provides
    bindings to C++ libraries implementing the algorithm SARSOP (Successive Approximations
    of the Reachable Space under Optimal Policies) and described in Kurniawati et al (2008),
    <doi:10.15607/RSS.2008.IV.009>.  This package also provides a high-level interface
    for generating, solving and simulating POMDP problems and their solutions.

Documentation and prototypes for the earliest (circa 2010) open-source effort to reverse engineer the sas7bdat file format. The package includes a prototype reader for sas7bdat files. However, newer packages may contain more robust readers for sas7bdat files.

Using any importation code designed for 'SAS' users to read ASCII files into 'sas7bdat' files, this package parses through the INPUT block of a '.sas' syntax file to design the parameters needed for a 'read.fwf()' function call.  This allows the user to specify the location of the ASCII (often a '.dat') file and the location of the 'SAS' syntax file, and then load the data frame directly into R in just one step.

Converts the dates to different 'SAS' date formats. In 'SAS' dates are a special case of 
             numeric values. Each day is assigned a specific numeric value, starting from January 1, 1960.
             This date is assigned the date value 0, and the next date has a date value of 1 and so on.
             The previous days to this date are represented by -1 , -2 and so on. With this approach, 
             'SAS' can represent any date in the future or any date in the past. There are many date 
             formats used in 'SAS' to represent date-time. Here, we try to develop functions which will
             convert the date to different 'SAS' date formats.

Provides 'SAS'-style IF/ELSE chains, independent IF rules,
    and DELETE logic for 'data.table', enabling clinical programmers to
    express Study Data Tabulation Model (SDTM) and Analysis Data Model
    (ADaM)-style derivations in familiar SAS-like syntax.
    Methods are informed by clinical data standards described in CDISC
    SDTM and ADaM implementation guides. See
    <https://www.cdisc.org/standards/foundational/sdtm> and
    <https://www.cdisc.org/standards/foundational/adam>.