* BLUE for Combining Location and Scale Information in a Meta-Analysis* (

**metaBLUE**)

The sample mean and standard deviation are two commonly used statistics in meta-analyses, but some trials use other summary statistics such as the median and quartiles to report the results. Therefore, researchers need to transform those information back to the sample mean and standard deviation. This package implemented sample mean estimators by Luo et al. (2016) <arXiv:1505.05687>, sample standard deviation estimators by Wan et al. (2014) <arXiv:1407.8038>, and the best linear unbiased estimators (BLUEs) of location and scale parameters by Yang et al. (2018, submitted) based on sample quantiles derived summaries in a meta-analysis.

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**Efficient Estimation of Grouped Survival Models Using the Exact Likelihood Function****groupedSurv**)

The core of this ‘Rcpp’-based package is a set of functions to compute the efficient score statistics for grouped survival models. The functions are designed to analyze grouped time-to-event data with the optional inclusion of either baseline covariates or family structure of related individuals (e.g., trios). Functions for estimating the baseline hazards, frailty variance, nuisance parameters, and fixed effects are also provided. The functions encompass two processes for discrete-time shared frailty model data with random effects: (1) evaluation of the multiple variable integration to compute the exact proportional-hazards-model-based likelihood and (2) estimation of the desired parameters using maximum likelihood. For data without family structure, only the latter step is performed. The integration is evaluated by the ‘Cuhre’ algorithm from the ‘Cuba’ library (Hahn, T. (2005). Cuba-a library for multidimensional numerical integration, Comput. Phys. Commun. 168, 78-95 <doi:10.1016/j.cpc.2005.01.010>), and the source files of the ‘Cuhre’ function are included in this package. The maximization process is carried out using Brent’s algorithm, with the ‘C++’ code file from John Burkardt and John Denker (Brent, R., Algorithms for Minimization without Derivatives, Dover, 2002, ISBN 0-486-41998-3).

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**Colour Palettes Based on the Scientific Colour-Maps****scico**)

Colour choice in information visualisation is important in order to avoid being mislead by inherent bias in the used colour palette. The ‘scico’ package provides access to the perceptually uniform and colour-blindness friendly palettes developed by Fabio Crameri and released under the ‘Scientific Colour-Maps’ moniker. The package contains 17 different palettes and includes both diverging and sequential types.