Following the common types of measures of uncertainty for parameter estimation, two measures of uncertainty were proposed for model selection, see Liu, Li and Jiang (2020) <doi:10.1007/s11749-020-00737-9>. The first measure is a kind of model confidence set that relates to the variation of model selection, called Mac. The second measure focuses on error of model selection, called LogP. They are all computed via bootstrapping. This package provides functions to compute these two measures. Furthermore, a similar model confidence set adapted from Bayesian Model Averaging can also be computed using this package.

The MACP employs machine learning algorithm for automated scoring 
   of co-fractionation mass spectrometry (CF-MS) and then systematically 
   map multi-protein complexes from these high-confidence protein-protein 
   interactions (PPIs) using unsupervised learning (i.e., clustering).

Procedures for simulating biomes by equilibrium vegetation 
  models, with a special focus on paleoenvironmental applications.
  Three widely used equilibrium biome models are currently implemented in 
  the package: the Holdridge Life Zone (HLZ) system (Holdridge 1947, 
  <doi:10.1126/science.105.2727.367>), the Köppen-Geiger classification 
  (KGC) system (Köppen 1936, 
  <https://koeppen-geiger.vu-wien.ac.at/pdf/Koppen_1936.pdf>) and the 
  BIOME model (Prentice et al. 1992, <doi:10.2307/2845499>). Three 
  climatic forest-steppe models are also implemented.
  An approach for estimating monthly time series of relative sunshine 
  duration from temperature and precipitation data (Yin 1999, 
  <doi:10.1007/s007040050111>) is also adapted, allowing 
  process-based biome models to be combined with high-resolution 
  paleoclimate simulation datasets (e.g., CHELSA-TraCE21k v1.0 dataset: 
  <https://chelsa-climate.org/chelsa-trace21k/>).

This repository aims to contribute to the econometric models' production
	with Colombian data, by providing a set of web-scrapping functions 
	of some of the main macro-financial indicators. All the sources are public and
	free, but the advantage of these functions is that they directly download 
	and harmonize the information in R's environment. No need to import or download
	additional files. You only need an internet connection!