David J Reiss, Institute for Systems Biology
cMonkey integrated biclustering algorithm for learning
co-regulated gene modules
RCurl, foreach, doMC, multicore, ff, igraph, RSVGTipsDevice,
hwriter, trimcluster

RCurl, doMC, foreach, multicore, igraph, RSVGTipsDevice,
hwriter, ref, bigmemory, filehash

RCurl, doMC, foreach, multicore, igraph0, RSVGTipsDevice,
hwriter, ref, bigmemory, filehash

Wolfgang Lenhard [cre, aut] (<https://orcid.org/0000-0002-8184-6889>),
Alexandra Lenhard [aut],
Sebastian Gary [ctb]
c(person("Wolfgang", "Lenhard", role = c("cre","aut"), email="wolfgang.lenhard@uni-wuerzburg.de", comment=c(ORCID  = "0000-0002-8184-6889")), person("Alexandra", "Lenhard", role=c("aut")), person("Sebastian", "Gary", role=c("ctb")))
Conventional methods for producing standard scores in psychometrics or biometrics are often plagued
with "jumps" or "gaps" (i.e., discontinuities) in norm tables and low confidence for assessing extreme scores.
The continuous norming method introduced by A. Lenhard et al. (2016), <doi:10.1177/1073191116656437>,  generates
continuous test norm scores on the basis of the raw data from standardization samples,
without requiring assumptions about the distribution of the raw data: Norm scores are directly established
from raw data by modeling the latter ones as a function of both percentile scores and an explanatory
variable (e.g., age). The method minimizes bias arising from sampling and measurement error, while
handling marked deviations from normality, addressing bottom or ceiling effects and capturing almost
all of the variance in the original norm data sample.
knitr, rmarkdown, shiny, shinycssloaders, foreign, readxl

Conventional methods for producing standard scores in psychometrics or biometrics
are often plagued with "jumps" or "gaps" (i.e., discontinuities) in norm tables and low
confidence for assessing extreme scores. The continuous norming method introduced by A.
Lenhard et al. (2016), <doi:10.1177/1073191116656437>, generates continuous test norm
scores on the basis of the raw data from standardization samples, without requiring
assumptions about the distribution of the raw data: Norm scores are directly established
from raw data by modeling the latter ones as a function of both percentile scores and an
explanatory variable (e.g., age). The method minimizes bias arising from sampling and
measurement error, while handling marked deviations from normality, addressing bottom
or ceiling effects and capturing almost all of the variance in the original norm data
sample.
lattice (>= 0.20), leaps (>= 3.0.0), latticeExtra (>= 0.6),
rmarkdown
knitr, markdown, shiny, shinycssloaders, foreign, readxl