Carries out Bland Altman analyses (also known as a Tukey
mean-difference plot) as described by JM Bland and DG Altman in
1986 <doi:10.1016/S0140-6736(86)90837-8>. This package was created in
2015 as existing Bland-Altman analysis functions did not calculate
confidence intervals. This package was created to rectify this,
and create reproducible plots. It further provides the structures for
creating a Jamovi (<https://www.jamovi.org/>) module to allows use of
the package functions via an easy to use GUI system.
ggplot2, knitr, stringr, jmvcore (>= 0.5.5), R6, rmarkdown
'blandr.data.preparation.r' 'blandr.dataset.fibre.r'
'blandr.dataset.sbp.r' 'blandr.dataset.o2sats.r'
'blandr.dataset.pefr.r' 'blandr.dataset.load.r'
'blandr.plot.ggplot.r' 'blandr.plot.rplot.r'
'blandr.plot.limits.r' 'blandr.statistics.r' 'blandr.draw.r'
'blandr.output.text.r' 'blandr.display.and.draw.r'
'blandr.display.and.plot.r' 'blandr.method.comparison.r'
'blandr.output.report.r' 'blandr.plot.normality.r'
'blandr.plot.qq.r' 'blandr.plot.r' 'jamovibaanalysis.b.R'
'jamovibaanalysis.h.R' 'jamovibaplothistogram.b.R'
'jamovibaplothistogram.h.R' 'jamovibaplotqq.b.R'
'jamovibaplotqq.h.R' 'jamovibastats.b.R' 'jamovibastats.h.R'

Carries out Bland Altman analyses (also known as a Tukey
mean-difference plot) as described by JM Bland and DG Altman in
1986 <doi:10.1016/S0140-6736(86)90837-8>. This package was created in
2015 as existing Bland-Altman analysis functions did not calculate
confidence intervals. This package was created to rectify this,
and create reproducible plots. This package is also available as a module
for the 'jamovi' statistical spreadsheet (see <https://www.jamovi.org>
for more information).
ggplot2, knitr, stringr, jmvcore (>= 0.8.5), R6, rmarkdown

Bayesian Inference with Laplace Approximations and P-Splines
c(person("Oswaldo","Gressani", email="oswaldo_gressani@hotmail.fr",
role=c("aut","cre"), comment = "Author"),
person("Philippe", "Lambert", email="p.lambert@uliege.be",
role=c("aut", "ths"), comment = "Co-author and thesis advisor"))
Laplace approximations and penalized B-splines are combined
for fast Bayesian inference in latent Gaussian models. The routines can be
used to fit survival models, especially proportional hazards and promotion
time cure models (Gressani, O. and Lambert, P. (2018)
<doi:10.1016/j.csda.2018.02.007>). The Laplace-P-spline methodology can also
be implemented for inference in additive and generalized additive models
(Gressani, O. and Lambert, P. (2020) <arXiv:2003.07214>). See the associated
website for more information and examples.
Oswaldo Gressani [aut, cre] (Author),
Philippe Lambert [aut, ths] (Co-author and thesis advisor)