The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
The goal of BINtools is to implement a BIN model, a Bayesian approach to decomposing forecasting accuracy into three components: bias, partial information, and noise.
You can install the released version of BINtools from CRAN with:
install.packages("BINtools")This is a basic example which shows you how to solve a common problem:
library(BINtools)
# An example with two forecasting groups
# a) Simulate synthetic data:
synthetic_data = simulate_data(list(mu_star = -0.8,mu_0 = -0.5,mu_1 = 0.2,gamma_0 = 0.1,
gamma_1 = 0.3, rho_0 = 0.05,delta_0 = 0.1, rho_1 = 0.2, delta_1 = 0.3,rho_01 = 0.05), 300,100,100)
# b) Estimate the BIN-model on the synthetic data:
full_bayesian_fit = estimate_BIN(synthetic_data$Outcomes,synthetic_data$Control,synthetic_data$Treatment,warmup = 1000, iter = 2000)
# c) Analyze the results:
complete_summary(full_bayesian_fit)These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.