cor_dist: Compute pairwise correlation-based distance
In nlhuong/buds: Bayesian Unidimensional Scaling (BUDS)

Pearson correlation distance for continuous data: d = (1- cor(x_i, x_j))/2 where cor(x_i, x_j) is the correlation between vector x_i and vector x_j.

1 2	cor_dist(X, method = "pearson", scale = TRUE, base = 1e+06, log_trans = TRUE, log_base = 10)

`X`	A data matrix, e.g. gene expression
`method`	a character string indicating which correlation coefficient is to be computed. One of "pearson" (default), "kendall", or "spearman": can be abbreviated.
`scale`	A boolean indicating whether to normalize the columns (samples) of the data to the even sum.
`base`	A numeric value for the shared column sum, if scale is TRUE.
`log_trans`	A boolean indicating whether to log transform the data prio to distance computation (log(X + 1)). Default is FALSE.
`log_base`	A number indicating base for log transformation. Default is 10.