Helps users in quickly visualizing risk-of-bias 
    assessments performed as part of a systematic review. It allows users to 
    create weighted bar-plots of the distribution of risk-of-bias judgments 
    within each bias domain, in addition to traffic-light plots of the 
    specific domain-level judgments for each study. The resulting figures are 
    of publication quality and are formatted according the risk-of-bias 
    assessment tool use to perform the assessments. Currently, the supported 
    tools are ROB2.0 (for randomized controlled trials; Sterne et al (2019)  
    <doi:10.1136/bmj.l4898>), ROBINS-I (for non-randomised studies of 
    interventions; Sterne et al (2016) <doi:10.1136/bmj.i4919>), and QUADAS-2 
    (for diagnostic accuracy studies; Whiting et al (2011) 
    <doi:10.7326/0003-4819-155-8-201110180-00009>).

Semi-Automated Marketing Mix Modeling (MMM) aiming to reduce human bias by means of ridge regression and evolutionary algorithms, enables actionable decision making providing a budget allocation and diminishing returns curves and allows ground-truth calibration to account for causation.

Aggregates multiple Receiver Operating Characteristic (ROC) curves 
    obtained from different sources into one global ROC. Additionally, it’s 
    also possible to calculate the aggregated precision-recall (PR) curve.

Generation of Box-Cox based ROC curves and several aspects of inferences and hypothesis testing. Can be used when inferences for one biomarker (Bantis LE, Nakas CT, Reiser B. (2018)<doi:10.1002/bimj.201700107>) are of interest or when comparisons of two correlated biomarkers (Bantis LE, Nakas CT, Reiser B. (2021)<doi:10.1002/bimj.202000128>) are of interest. Provides inferences and comparisons around the AUC, the Youden index, the sensitivity at a given specificity level (and vice versa), the optimal operating point of the ROC curve (in the Youden sense), and the Youden based cutoff.

Functions for a classification method based on receiver operating characteristics (ROC). Briefly, features are selected according to their ranked AUC value in the training set. The selected features are merged by the mean value to form a meta-gene. The samples are ranked by their meta-gene value and the meta-gene threshold that has the highest accuracy in splitting the training samples is determined. A new sample is classified by its meta-gene value relative to the threshold. In the first place, the package is aimed at two class problems in gene expression data, but might also apply to other problems.