Title: comparison of medical forms in CDISC ODM format
Description: Input: 2 ODM files (ODM version 1.3) Output: list of
identical, matching, similar and differing data items

person("Collin", "Erickson", , "collinberickson@gmail.com", c("aut", "cre"))
Description: Makes comparisons quickly for different functions or code
blocks performing the same task with the function mbc().
Can be used to compare model fits to the same data or
Suggests: plyr, testthat, covr, knitr, rmarkdown, microbenchmark

Description: Quickly run experiments to compare the run time and output of
code blocks. The function mbc() can make fast comparisons of code,
and will calculate statistics comparing the resulting outputs.
It can be used to compare model fits to the same data or
The function ffexp() runs a function using all possible combinations
of selected inputs. This is useful for comparing the effect of
different parameter values. It can also run in parallel and
automatically save intermediate results, which is very useful
Suggests: plyr, progress, testthat, covr, knitr, ggplot2, parallel,

snow, rmarkdown, reshape, tibble, microbenchmark

The R6 class ffexp$new() runs a function using all possible combinations
Suggests: plyr, progress, testthat (>= 2.1.0), covr, knitr, ggplot2,
GGally, parallel, snow, rmarkdown, reshape, tibble, lhs,

Title: Multivariate likelihood ratio calculation and evaluation
Description: Functions for calculating and evaluating likelihood ratios

Description: Functions for calculating and evaluating likelihood ratios from uni/multivariate continuous observations

Title: Multivariate Likelihood Ratio Calculation and Evaluation
Authors@R: c(person("David", "Lucy", role = "aut"),
person("James", "Curran", role = c("aut", "cre"),
person("Agnieszka", "Martyna", role = "aut",
Description: Functions for calculating and evaluating likelihood ratios from uni/multivariate continuous observations.
The package includes the two-level functions to calculate the LR assuming multivariate normality, and another with drops
this assumption and uses a multivariate kernel density estimate. The package also contains code to perform empirical
cross entropy (ECE) calibration of likelihood ratios. The LR functions are based primarily on
Aitken, C.G.G. and Lucy, D. (2004) <doi:10.1046/j.0035-9254.2003.05271.x>, "Evaluation of trace evidence in the form of multivariate data,"
Journal of the Royal Statistical Society: Series C (Applied Statistics), 53: 109-122. The ECE functions are based primarily
on  D. Ramos and J. Gonzalez-Rodrigues, (2008) "Cross-entropy analysis of the information in forensic speaker recognition,"
in Proc. IEEE Odyssey, Speaker Lang. Recognit. Workshop.