Usage: 3dLocalBistat [options] dataset1 dataset2

This program computes statistics between 2 datasets,
at each voxel, based on a local neighborhood of that voxel.
 - The neighborhood is defined by the '-nbhd' option.
 - Statistics to be calculated are defined by the '-stat' option(s).
 - The 2 input datasets should have the same number of sub-bricks.
 - OR dataset1 should have 1 sub-brick and dataset2 can have more than 1:
   - In which case, the statistics of dataset2 against dataset1 are
     calculated for the #0 sub-brick of dataset1 against each sub-brick
     of dataset2.

 -nbhd 'nnn' = The string 'nnn' defines the region around each
               voxel that will be extracted for the statistics
               calculation.  The format of the 'nnn' string are:
               * 'SPHERE(r)' where 'r' is the radius in mm;
                 the neighborhood is all voxels whose center-to-
                 center distance is less than or equal to 'r'.
                 ** A negative value for 'r' means that the region
                    is calculated using voxel indexes rather than
                    voxel dimensions; that is, the neighborhood
                    region is a "sphere" in voxel indexes of
                    "radius" abs(r).
               * 'RECT(a,b,c)' is a rectangular block which
                 proceeds plus-or-minus 'a' mm in the x-direction,
                 'b' mm in the y-direction, and 'c' mm in the
                 z-direction.  The correspondence between the
                 dataset xyz axes and the actual spatial orientation
                 can be determined by using program 3dinfo.
                 ** A negative value for 'a' means that the region
                    extends plus-and-minus abs(a) voxels in the
                    x-direction, rather than plus-and-minus a mm.
                    Mutatis mutandum for negative 'b' and/or 'c'.
               * 'RHDD(r)' is a rhombic dodecahedron of 'radius' r.
               * 'TOHD(r)' is a truncated octahedron of 'radius' r.

 -stat sss   = Compute the statistic named 'sss' on the values
               extracted from the region around each voxel:
               * pearson  = Pearson correlation coefficient
               * spearman = Spearman correlation coefficient
               * quadrant = Quadrant correlation coefficient
               * mutinfo  = Mutual Information
               * normuti  = Normalized Mutual Information
               * jointent = Joint entropy
               * hellinger= Hellinger metric
               * crU      = Correlation ratio (Unsymmetric)
               * crM      = Correlation ratio (symmetrized by Multiplication)
               * crA      = Correlation ratio (symmetrized by Addition)
               * L2slope  = slope of least-squares (L2) linear regression of
                            the data from dataset1 vs. the dataset2
                            (i.e., d2 = a + b*d1 ==> this is 'b')
               * L1slope  = slope of least-absolute-sum (L1) linear regression
                            of the data from dataset1 vs. the dataset2
               * num      = number of the values in the region:
                            with the use of -mask or -automask,
                            the size of the region around any given
                            voxel will vary; this option lets you
                            map that size.
               * ALL      = all of the above, in that order
               More than one '-stat' option can be used.

 -mask mset  = Read in dataset 'mset' and use the nonzero voxels
               therein as a mask.  Voxels NOT in the mask will
               not be used in the neighborhood of any voxel. Also,
               a voxel NOT in the mask will have its statistic(s)
               computed as zero (0).
 -automask   = Compute the mask as in program 3dAutomask.
               -mask and -automask are mutually exclusive: that is,
               you can only specify one mask.
 -weight ws  = Use dataset 'ws' as a weight.  Only applies to 'pearson'.

 -prefix ppp = Use string 'ppp' as the prefix for the output dataset.
               The output dataset is always stored as floats.

 -histpow pp   = By default, the number of bins in the histogram used
                 for calculating the Hellinger, Mutual Information,
                 and Correlation Ratio statistics is n^(1/3), where n
                 is the number of data points in the -nbhd mask.  You
                 can change that exponent to 'pp' with this option.
 -histbin nn   = Or you can just set the number of bins directly to 'nn'.
 -hclip1 a b   = Clip dataset1 to lie between values 'a' and 'b'.  If 'a'
                 and 'b' end in '%', then these values are percentage
                 points on the cumulative histogram.
 -hclip2 a b   = Similar to '-hclip1' for dataset2.

Author: RWCox - October 2006.

++ Compile date = May 11 2021 {AFNI_21.1.07:linux_ubuntu_16_64}