Prediction & Confidence interval: the pima and
cima functions
Back-transformartion for logarithmic scale outcomes (print &
plot function; thanks to Dr. Morio Aihara).
CITATION was updated.
Vignette was updated.
Version 1.1.2 (2019-03-11)
Prediction interval: the pima function
Parallel computing for the parametric bootstrap method (see a
Vignette file).
Forest plot (see a Vignette file).
Kenward-Roger’s approach.
Confidence interval: the cima function
A Wald-type t-distribution confidence interval. Variance estimator
of the average effect: an approximate estimator. Heterogeneity variance:
Dersimonian-Laird estimator.
A Wald-type t-distribution confidence interval. Variance estimator
of the average effect: an approximate, Hartung-Knapp, Sidik-Jonkman,
Kenward-Roger estimators. Heterogeneity variance: REML estimator.
Profile likelihood confidence interval.
Profile likelihood confidence interval with a Bartlett type
correction.
Forest plot.
Heterogeneity variance estimators: the tau2h function
DerSimonian-Laird estimator.
Variance component type estimator.
Paule–Mandel estimator.
Hartung-Makambi estimator.
Hunter–Schmidt estimator.
Maximum likelihood estimator.
Restricted maximum likelihood estimator.
Approximate restricted maximum likelihood estimator.
Sidik–Jonkman estimator.
Sidik–Jonkman improved estimator.
Empirical Bayes estimator.
Bayes modal estimator.
ML and REML confidence intervals.
Converting binary data: the convert_bin function
Converting binary data to logarithmic odds ratio (see a Vignette
file).
Converting binary data to logarithmic relative risk.
Converting binary data to risk difference.
The distribution of a positive linear combination of chiqaure random
variables: the pwchisq function
Version 1.1.1 (2018-09-15)
Fixed documents.
Version 1.1.0 (2018-09-14)
Refined the package structure.
New function pima is available (see a Vignette
file).