Computes a confidence interval for a specified linear combination of 
    the regression parameters in a linear regression model with iid normal 
    errors with unknown variance when there is uncertain prior information 
    that a distinct specified linear combination of the regression 
    parameters takes a specified number. This confidence interval, found by 
    numerical nonlinear constrained optimization, has the required minimum coverage 
    and utilizes this uncertain prior information through desirable 
    expected length properties. This confidence interval is proposed by 
    Kabaila, P. and Giri, K. (2009) <doi:10.1016/j.jspi.2009.03.018>.

Implementation of the categorical instrumental variable (CIV) 
    estimator proposed by Wiemann (2023) <arXiv:2311.17021>. CIV allows for optimal instrumental 
    variable estimation in settings with relatively few observations per 
    category. To obtain valid inference in these challenging settings, CIV
    leverages a regularization assumption that implies existence of a latent 
    categorical variable with fixed finite support achieving the same first 
    stage fit as the observed instrument.

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.