NEWS.md
In pimeta: Prediction Intervals for Random-Effects Meta-Analysis

The 'pimeta' package

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.

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