Benchmark Suite for Indirect Methods for RI Estimation
Tatjana Ammer [aut, cre],
Christopher Rank [aut],
Andre Schuetzenmeister [aut]
c(
person(given ="Tatjana", family ="Ammer", email ="tatjana.ammer@roche.com", role =c("aut", "cre")),
person(given ="Christopher", family = "Rank", email ="christopher.rank@roche.com", role ="aut"),
person(given ="Andre", family ="Schuetzenmeister", email ="andre.schuetzenmeister@roche.com", role ="aut"))
stats, optparse, digest, data.table, msm, graphics, grDevices,
RColorBrewer, sfsmisc
The provided benchmark suite enables the automated evaluation and comparison of any existing and novel indirect method for reference interval ('RI') estimation in a systematic way.
Indirect methods take routine measurements of diagnostic tests, containing pathological and non-pathological samples as input and use sophisticated
statistical methods to derive a model describing the distribution of the non-pathological samples, which can then be
used to derive reference intervals. The benchmark suite contains 5,760 simulated test sets with varying difficulty.
To include any indirect method, a custom wrapper function needs to be provided.
The package offers functions for generating the test sets, executing the indirect method and evaluating the results.
See ?RIbench or vignette("RIbench_package") for a more comprehensive description of the features.
A detailed description and application is described in Ammer T., Schuetzenmeister A., Prokosch H.-U., Zierk J., Rank C.M., Rauh M. "RIbench: A Proposed Benchmark for the Standardized Evaluation of Indirect Methods for Reference Interval Estimation". Clinical Chemistry (2022) <doi:10.1093/clinchem/hvac142>.

stats, optparse, digest, data.table, graphics, grDevices,
RColorBrewer

Drawing SVG Graphics to Visualize and Map Genome-Wide Data in
Idiograms
c(person("Zhaodong", "Hao", email = "haozd1992@163.com", role = c("aut", "cre")),
person("Dekang", "Lv", email = "dekanglv@126.com", role = "aut"),
person("Ying", "Ge", email = "geying.tju@gmail.com", role = "aut"),
person("Jisen", "Shi", email = "Jshi@njfu.edu.cn", role = "aut"),
person("Guangchuang", "Yu", email = "guangchuangyu@gmail.com", role = "aut"),
person("Jinhui", "Chen", email = "chenjh@njfu.edu.cn", role = "aut"))
For whole-genome analysis, idiograms are virtually the most intuitive and effective way to map and visualize the genome-wide information. RIdeogram was developed to visualize and map whole-genome data in idiograms with no restriction of species.
ggplot2, grDevices, grImport2, rsvg, scales, tools
Zhaodong Hao [aut, cre],
Dekang Lv [aut],
Ying Ge [aut],
Jisen Shi [aut],
Guangchuang Yu [aut],
Jinhui Chen [aut]