broom (>= 0.7.0), broom.helpers (>= 1.1.0), chemometrics,
geosphere (>= 1.5-1), ggforce, Hmisc, igraph (>= 1.0.1),
intergraph (>= 2.0-2), labelled, maps (>= 3.1.0), mapproj,
nnet, network (>= 1.17.1), scagnostics, sna (>= 2.3-2),
survival, rmarkdown, roxygen2, testthat, crosstalk, knitr,
spelling, emmeans

The GHQ function can be used to obtain the quadrature points and weights to approximate an integral in two or more dimensions. This function uses the pruning approach to eliminate that points that do not contribute to the approximation of the integral and increases computational cost. The advantage to conducting this elimination of points is the decrease in the number of times that the function of interest is evaluated. This advantage is crucial in mixed models in which we must address several integrations within an iterative process to obtain model parameters.

Graphical Horseshoe MCMC Sampler Using Data Augmented Block
Gibbs Sampler
Draw posterior samples to estimate the precision matrix for multivariate Gaussian data. Posterior means of the samples is the graphical horseshoe estimate by Li, Bhadra and Craig(2017) <arXiv:1707.06661>. The function uses matrix decomposition and variable change from the Bayesian graphical lasso by Wang(2012) <doi:10.1214/12-BA729>, and the variable augmentation for sampling under the horseshoe prior by Makalic and Schmidt(2016) <arXiv:1508.03884>. Structure of the graphical horseshoe function was inspired by the Bayesian graphical lasso function using blocked sampling, authored by Wang(2012) <doi:10.1214/12-BA729>.
2018-10-24 16:05:44 UTC; Ashutosh Srivastava

Yuri Tani Utsunomiya, Marco Milanesi, Mario Barbato
Haplotype calling from phased marker data. Given user-defined haplotype blocks (HapBlock), the package identifies the different haplotype alleles (HapAllele) present in the data and scores sample haplotype allele genotypes (HapGenotype) based on HapAllele dose (i.e. 0, 1 or 2 copies). The output is not only useful for analyses that can handle multi-allelic markers, but is also conveniently formatted for existing pipelines intended for bi-allelic markers. The package was first described in Bioinformatics by Utsunomiya et al. (2016, <doi:10.1093/bioinformatics/btw356>). Since the v2 release, the package provides functions for unsupervised and supervised detection of ancestry tracks. The methods implemented in these functions were described in an article published in Methods in Ecology and Evolution by Utsunomiya et al. (2020, <doi:10.1111/2041-210X.13467>).