A convenient interface for making
  requests directly to the 'Civis Platform API' <https://www.civisanalytics.com/platform>.
  Full documentation available 'here' <https://civisanalytics.github.io/civis-r/>.

Directory reads and summaries are provided for one or more of the subdirectories
 of the <https://cran.r-project.org/incoming/> directory, and a compact summary object is returned.
 The package name is a contraption of 'CRAN Incoming Watcher'.

We provide a computationally efficient and robust implementation of the recently proposed C-JAMP (Copula-based Joint Analysis of Multiple Phenotypes) method (Konigorski et al., 2019, submitted). C-JAMP allows estimating and testing the association of one or multiple predictors on multiple outcomes in a joint model, and is implemented here with a focus on large-scale genome-wide association studies with two phenotypes. The use of copula functions allows modeling a wide range of multivariate dependencies between the phenotypes, and previous results are supporting that C-JAMP can increase the power of association studies to identify associated genetic variants in comparison to existing methods (Konigorski, Yilmaz, Pischon, 2016, <DOI:10.1186/s12919-016-0045-6>; Konigorski, Yilmaz, Bull, 2014, <DOI:10.1186/1753-6561-8-S1-S72>). In addition to the C-JAMP functions, functions are available to generate genetic and phenotypic data, to compute the minor allele frequency (MAF) of genetic markers, and to estimate the phenotypic variance explained by genetic markers.

Connect and pull data from the 'CJA' API, which powers 'CJA Workspace' <https://github.com/AdobeDocs/cja-apis>. 
             The package was developed with the analyst in mind and will 
             continue to be developed with the guiding principles of iterative, 
             repeatable, timely analysis. New features are actively being 
             developed and we value your feedback and contribution to the process.

A tool for analyzing conjoint experiments using Bayesian Additive Regression Trees ('BART'), a machine learning method developed by Chipman, George and McCulloch (2010) <doi:10.1214/09-AOAS285>. This tool focuses specifically on estimating, identifying, and visualizing the heterogeneity within marginal component effects, at the observation- and individual-level. It uses a variable importance measure ('VIMP') with delete-d jackknife variance estimation, following Ishwaran and Lu (2019) <doi:10.1002/sim.7803>, to obtain bias-corrected estimates of which variables drive heterogeneity in the predicted individual-level effects.