Description: 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
Imports: lattice (>= 0.20), leaps (>= 3.0.0), latticeExtra (>= 0.6),
Suggests: knitr, markdown, shiny, shinycssloaders, foreign, readxl

Authors@R: c(person("Wolfgang", "Lenhard", role = c("cre","aut"), email="wolfgang.lenhard@uni-wuerzburg.de", comment=c("<https://orcid.org/0000-0002-8184-6889>")), person("Alexandra", "Lenhard", role=c("aut")), person("Sebastian", "Gary", role=c("ctb")))
Suggests: knitr, markdown, shiny, shinycssloaders, foreign, readxl,

Imports: lattice (>= 0.20), leaps (>= 3.0), latticeExtra (>= 0.6)

Authors@R: c(person("Wolfgang", "Lenhard", role = c("cre","aut"), email="wolfgang.lenhard@uni-wuerzburg.de", comment=c("<https://orcid.org/0000-0002-8184-6889>")), person("Alexandra", "Lenhard", role=c("aut")), person("Sebastian", "Gary", role=c("aut")))
sample. An online demonstration is available via <https://cnorm.shinyapps.io/cNORM/>.

Suggests: knitr, shiny, shinycssloaders, foreign, readxl, rmarkdown

Suggests: knitr, shiny, shinycssloaders, foreign, readxl, rmarkdown,

person("Wolfgang", "Lenhard", role = c("cre","aut"),
Description: Conventional methods for producing standard scores or percentiles
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.
2021 <doi: 10.1177/0013164420928457>) estimates percentile development
(e. g. over age) and 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. It includes procedures
for post stratification of norm samples to overcome bias in data collection
and to mitigate violations of representativeness. An online demonstration is
Author: Alexandra Lenhard [aut] (<https://orcidorg/0000-0001-8680-4381>),
Wolfgang Lenhard [cre, aut] (<https://orcid.org/0000-0002-8184-6889>),