Predicting the functional roles of proteins based on various genome-wide data, such as protein-protein association networks, has become a canonical problem in computational biology. Approaching this ...

In this section, we estimate the monetary authorities’ reaction function using the data from August 1971 to March 2018. Most papers analyzing interventions before March 1991 have used “Change in ...

A probit model is a type of regression used in statistics to model binary outcome variables. It estimates the probability that an observation with certain characteristics will fall into one of two ...

We use the libraries: Numpy, Scipy, Sympy, Math, statsmodels.api, and Python 3.5 with Anaconda. To down statsmodels, you should visit: http://statsmodels.sourceforge ...

These R codes implement the Bayesian methodology of Castelletti & Consonni (2021, Bayesian Analysis) for structure learning and causal inference in probit graphical models. Specifically: ...

Abstract: Non-uniform random number generators are key components in Monte Carlo simulations. The inverse cumulative distribution function (ICDF) technique provides a viable solution for generating ...