parLply

vector of inputs over which to parallel compute

this option is set to "FORK", use "PSOCK" on windows

type

the number of cores to use. Defaults at 1

no_cores

<code>parLply</code> uses <code>parlapply</code> from the <code>parallel</code> package with 
a function as input

Implements Bayesian Distribution Regression methods. This package contains functions for three estimators (non-asymptotic, semi-asymptotic and asymptotic) and related routines for Bayesian Distribution Regression in Huang and Tsyawo (2018) <doi:10.2139/ssrn.3048658> which is also the recommended reference to cite for this package. The functions can be grouped into three (3) categories. The first computes the logit likelihood function and posterior densities under uniform and normal priors. The second contains Independence and Random Walk Metropolis-Hastings Markov Chain Monte Carlo (MCMC) algorithms as functions and the third category of functions are useful for semi-asymptotic and asymptotic Bayesian distribution regression inference.

parLply: Parallel compute

Description

Usage

Arguments

Value