pdqr | summarize custom random variables with distribution | Development Tools library

‘pdqr’ is not yet on CRAN. You can install the development version from GitHub with:.
Sample input is converted into data frame of appropriate structure that defines distribution (see next list item). It is done based on type. For “discrete” type it gets tabulated with frequency of unique values serving as their probability. For “continuous” type distribution density is estimated using density() function if input has at least 2 elements. For 1 element special “dirac-like” pdqr-function is created: an approximation of single number as triangular distribution with very narrow support (1e-8 order of magnitude).
Data frame input should completely define distribution. For “discrete” type it should have “x” and “prob” columns for output values and their probabilities. For “continuous” type - “x” and “y” columns for points, which define piecewise-linear continuous density function. Columns “prob” and “y” will be automatically normalized to represent proper distribution: sum of “prob” will be 1 and total square under graph of piecewise-linear function will be 1.
P-function is a cumulative distribution function. Created with new_p().
D-function is a probability mass function for “discrete” type and density function for “continuous”. Created with new_d(). Generally speaking, it is a derivative of distribution’s p-function.
Q-function is a quantile function. Created with new_q(). Inverse of distribution’s p-function.
R-function is a random generation function. Created with new_r(). Generates a random sample from distribution.