Requires the OpenMP library for parallel computing.
If the OpenMP library is not available, the code is executed
sequentially and a warning is printed.

Perform Dynamic Model Averaging with grid search as in Dangl and Halling (2012) <doi:10.1016/j.jfineco.2012.04.003> using parallel computing.

Leopoldo Catania [aut,cre], Nima Nonejad  [aut]
Perform dynamic model averaging with grid search as in Dangl and Halling (2012) <doi:10.1016/j.jfineco.2012.04.003> using parallel computing.

Fetch Data from the Federal Reserve Economic Database
Interact with the FRED API, <https://fred.stlouisfed.org/docs/api/fred/>,
to fetch observations across economic series;
find information about different economic sources, releases, series, etc.;
conduct searches by series name, attributes, or tags; and determine the
latest updates. Includes functions for creating panels of related variables
with minimal effort and datasets containing data sources, releases, and
popular FRED tags.

Leveraging eQTLs to Identify Individual-Level Tissue of Interest
for a Complex Trait
c(person("Arunabha", "Majumdar", email = "statgen.arunabha@gmail.com", role = c("aut", "cre")), person("Tanushree", "Haldar", email = "tanushree.haldar@gmail.com", role = "aut"), person("Bogdan", "Pasaniuc", email = "pasaniuc@ucla.edu", role = "aut"))
Genetic predisposition for complex traits is often manifested through multiple tissues of interest at different time points in the development. As an example, the genetic predisposition for obesity could be manifested through inherited variants that control metabolism through regulation of genes expressed in the brain and/or through the control of fat storage in the adipose tissue by dysregulation of genes expressed in adipose tissue. We present a method eGST (eQTL-based genetic subtyper) that integrates tissue-specific eQTLs with GWAS data for a complex trait to probabilistically assign a tissue of interest to the phenotype of each individual in the study. eGST estimates the posterior probability that an individual's phenotype can be assigned to a tissue based on individual-level genotype data of tissue-specific eQTLs and marginal phenotype data in a genome-wide association study (GWAS) cohort. Under a Bayesian framework of mixture model, eGST employs a maximum a posteriori (MAP) expectation-maximization (EM) algorithm to estimate the tissue-specific posterior probability across individuals. Methodology is available from: A Majumdar, C Giambartolomei, N Cai, MK Freund, T Haldar, T Schwarz, J Flint, B Pasaniuc (2019) <doi:10.1101/674226>.
purrr, mvtnorm, MASS, utils, stats, matrixStats
Arunabha Majumdar [aut, cre],
Tanushree Haldar [aut],
Bogdan Pasaniuc [aut]