Perform meta-analysis of single-case experiments, including calculating various effect size measures (SMD, PND and PEM) and probability combining (additive and multiplicative method).

Perform meta-analysis of single-case experiments, including calculating various effect size measures (SMD, PND, PEM and NAP) and probability combining (additive and multiplicative method), as discussed in Bulte and Onghena (2013) <doi:10.22237/jmasm/1383280020>.

Isis Bulte, Tamal Kumar De, Patrick Onghena

Score Projection Between in 'Vivo' and in 'Vitro' Datasets
Assigns a score projection from 0 to 1 between a given in 'vivo' stage and each single cluster from an in 'vitro' dataset. The score is assigned based on the the fraction of specific markers of the in 'vivo' stage that are conserved in the in 'vitro' clusters <https://github.com/ScialdoneLab>.
CIARA, rmarkdown, testthat (>= 3.0.0), knitr

c(
person("Debsurya","De", ,"debsurya001@gmail.com", role =c("cre","aut")),
person("Priyam","Das", ,"pdas@ncsu.edu" , role = "aut")
)
'imports.R' 'biomarker.R' 'SHUM.R' 'EHUM.R' 'ULBA.R' 'SCOR.R'
'SCOR-package.R' 'optimized_HUM.R' 'youden_points.R'
'YoudenBoxPlot.R'
A non-convex optimization package that optimizes any function under the criterion, combination of
variables are on the surface of a unit sphere, as described in the paper : Das et al. (2019) <arXiv:1909.04024> .
Debsurya De [cre, aut],
Priyam Das [aut]

A non convex optimization package that optimizes any function under the criterion, combination of variables are on the surface of a unit sphere, as described in the paper : Das et al. (2019) <arXiv:1909.04024> .

SCORER 2.0: an algorithm for distinguishing parallel dimeric and
trimeric coiled-coil sequences
Craig T. Armstrong, Thomas L. Vincent <tlfvincent@gmail.com>, Peter J. Green and Derek N. Woolfson <D.N.Woolfson@bristol.ac.uk>
This package contains the functions necessary to run the SCORER 2.0 algorithm. SCORER 2.0 can be used to differentiate between parallel dimeric and trimeric coiled-coil sequence, which are the two most more frequent coiled-coil structures observed naturally. As such, SCORER 2.0 is particularly useful for researchers looking to characterize novel coiled-coil sequences. It may also be used to assist in the structural characterization of synthetic coiled-coil sequences. Also included in this package are functions that allows the user to retrain the SCORER 2.0 algorithm using user-defined training data.