"iRefR" allows the user to load any version of the consolidated protein interaction database "iRefIndex" and perform tasks such as: selecting databases, pmids, experimental methods, searching for specific proteins, separate binary interactions from complexes and polymers, generate complexes according to an algorithm that looks after possible binary-represented complexes, make general database statistics and create network graphs, among others.
Antonio Mora <a.m.ortiz@biotek.uio.no>, Ian Donaldson <i.m.donaldson@biotek.uio.no>

Antonio Mora <a.c.m.ortiz@biotek.uio.no>, Ian Donaldson
<i.m.donaldson@biotek.uio.no>

Antonio Mora <antoniocmora@gmail.com>, Ian Donaldson
<i.m.donaldson@biotek.uio.no>

Antonio Mora <antoniocmora@gmail.com>, Ian Donaldson
<ian.oslo@gmail.com>

Antonio Mora <antoniocmora@gmail.com>, Ian Donaldson <ian.oslo@gmail.com>

Eufrasio de A. Lima Neto / Claudio A. Vasconcelos
Eufrasio de A. Lima Neto <eufrasio@de.ufpb.br> ; Claudio A.
Vasconcelos <cldsza@gmail.com>
Use the centre method, centre and range method and MinMax
method to fit a linear regression model for symbolic
interval-valued data.

Regression methods for interval-valued variables
This package contains some important regression methods
for interval-valued variables. For each method, it is available
the fitted values, residuals and some goodness-of-fit
measures.Depends: mgcvImports: pcls

Regression Methods for Interval-Valued Variables
Eufrasio de A. Lima Neto / Claudio A. V. de Souza Filho / Pedro R. D. Marinho
Eufrasio de A. Lima Neto <eufrasio@de.ufpb.br>
Contains some important regression methods for interval-valued variables. For each method, it is available the fitted values, residuals and some goodness-of-fit measures.

Eufrasio de A. Lima Neto with contribution from Claudio A.
Vasconcelos
This package contains some important regression methods
for interval-valued variables. For each method, it is available
the fitted values, residuals and some goodness-of-fit measures.

This package calculates intraclass correlation coefficient (ICC) for assessing reproducibility of interval-censored data with two repeated measurements. ICC is estimated by maximum likelihood from model with one fixed and one random effect (both intercepts). Help in model checking (normality of subjects' means and residuals) is provided.