Title: General-to-Specific (GETS) Modelling and Indicator Saturation
Author: Genaro Sucarrat [aut, cre], Felix Pretis [aut], James Reade [aut]
Description: Automated General-to-Specific (GETS) modelling of the mean and variance of a regression, and indicator saturation methods for detecting and testing for structural breaks in the mean.

Author: Felix Pretis, James Reade, Genaro Sucarrat
Description: Automated multi-path General-to-Specific (GETS) modelling of the mean and variance of a regression, and indicator saturation methods for detecting structural breaks in the mean. The mean can be specified as an autoregressive model with covariates (an 'AR-X' model), and the variance can be specified as a log-variance model with covariates (a 'log-ARCH-X' model). The four main functions of the package are arx, getsm, getsv and isat. The first function, arx, estimates an AR-X model with log-ARCH-X errors. The second function, getsm, undertakes GETS model selection of the mean specification of an arx object. The third function, getsv, undertakes GETS model selection of the log-variance specification of an arx object. The fourth function, isat, undertakes GETS model selection of an indicator saturated mean specification.

Author: Genaro Sucarrat [aut, cre], Felix Pretis [aut], James Reade [aut], Jonas Kurle [ctb], Moritz Schwarz [ctb]
Description: Automated General-to-Specific (GETS) modelling of the mean and variance of a regression, and indicator saturation methods for detecting and testing for structural breaks in the mean, see Pretis, Reade and Sucarrat (2018) <doi:10.18637/jss.v086.i03>.

Suggests: lgarch, xtable, Matrix, microbenchmark

Description: Automated multi-path General-to-Specific (GETS) modelling of the mean and variance of a regression, and indicator saturation methods for detecting and testing for structural breaks in the mean.

Title: SPRE Statistics for Exploring Heterogeneity in Meta-Analysis
Description: An implementation of SPRE (standardised predicted random-effects)
statistics in R to explore heterogeneity in genetic association meta-
analyses, as described by Magosi et al. (2019)
<doi:10.1093/bioinformatics/btz590>. SPRE statistics are precision
weighted residuals that indicate the direction and extent with which
individual study-effects in a meta-analysis deviate from the average
genetic effect. Overly influential positive outliers have the potential
to inflate average genetic effects in a meta-analysis whilst negative
outliers might lower or change the direction of effect. See the 'getspres'
colorspace (>= 1.2-6), RColorBrewer (>= 1.1-2), colorRamps (>=