ea_iid - Run IID tests for entropy assesment

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ea_iid 1.1.5                                       April 2023                                          EA_IID(1)

       Run IID tests for entropy assesment.

       <file_name>: Must be relative path to a binary file with at least 1 million entries (samples).

       [bits_per_symbol]: Must be between 1-8, inclusive. By default this value is inferred from the data.

       [-i|-c]: '-i' for initial entropy estimate, '-c' for conditioned sequential dataset entropy estimate. The
              initial entropy estimate is the default.

       [-a|-t]:  '-a'  produces  the  'H_bitstring' assessment using all read bits, '-t' truncates the bitstring
              used to produce the `H_bitstring` assessment to 1000000 bits. Test all data by default.

              Note: When testing binary data, no `H_bitstring` assessment is produced,  so  the  `-a`  and  `-t`
              options produce the same results for the initial assessment of binary data.

       -v: Optional verbosity flag for more output. Can be used multiple times.

       -q: Quiet mode, less output to screen. This will override any verbose flags.

       -l <index>,<samples>   Read the <index> substring of length <samples>.

              Samples  are assumed to be packed into 8-bit values, where the least significant 'bits_per_symbol'
              bits constitute the symbol.

       -i: Initial Entropy Estimate (Section 3.1.3)

              Computes the initial entropy estimate H_I as  described  in  Section  3.1.3  (not  accounting  for
              H_submitter)  using  the  entropy  estimators  specified  in Section 6.3.  If 'bits_per_symbol' is
              greater than 1, the samples are also converted to bitstrings and assessed to  create  H_bitstring;
              for  multi-bit  symbols,  two entropy estimates are computed: H_original and H_bitstring.  Returns
              min(H_original,  bits_per_symbol  X   H_bitstring).   The   initial   entropy   estimate   H_I   =
              min(H_submitter, H_original, bits_per_symbol X H_bitstring).

       -c: Conditioned Sequential Dataset Entropy Estimate (Section 3.1.5.2)

              Computes  the  entropy  estimate  per  bit  h'  for  the  conditioned  sequential  dataset  if the
              conditioning function is non-vetted. The samples are converted  to  a  bitstring.   Returns  h'  =
              min(H_bitstring).

       -o: Set Output Type to JSON

              Changes the output format to JSON and sets the file location for the output file.

       --version: Prints tool version information

       ea_iid - Run IID tests for entropy assesment

ea_iid [-i|-c] [-a|-t] [-v] [-q] [-l <index>,<samples> ] <file_name> [bits_per_symbol]