Create, transform, and summarize custom random variables with distribution functions (analogues of 'p*()', 'd*()', 'q*()', and 'r*()' functions from base R). Two types of distributions are supported: "discrete" (random variable has finite number of output values) and "continuous" (infinite number of values in the form of continuous random variable). Functions for distribution transformations and summaries are available. Implemented approaches often emphasize approximate and numerical solutions: all distributions assume finite support and finite values of density function; some methods implemented with simulation techniques.
Excerpt of important documentation:
README and vignettes provide overview of package functionality.
Documentation of meta_*() functions describes implementation details of pdqr-functions.
Documentation of new_*() functions describes the process of creating pdqr-functions.
Documentation of as_*() functions describes the process of updating class of pdqr-functions.
summ_*() functions describes how different summary
functions work. A good place to start is
region_*() functions describes functionality
to work with regions: data frames defining subset of one dimensional real
This package has the following options (should be set by options()):
"pdqr.group_gen.args_new", "pdqr.group_gen.n_sample", "pdqr.group_gen.repair_supp_method". They may be used to customize behavior of methods for S3 group generic functions. See their help page for more information.
"pdqr.assert_args". This boolean option (default to
TRUE) may be used
to turn off sanity checks of function arguments (set it to
will somewhat increase general execution speed. Use this option at your own
risk in case you are confident that input arguments have correct type and