knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )

Package ConfZIC provides methods to narrow down the number of models to look at in model selection based on Generalized Information Criteria for regression and time-series data.

Installation can be done for "ConfZIC" R package in three ways.

From the Comprehensive R Archive Network (CRAN): Use install.packages() function in R. Then, import ConfZIC package into working session using library() function. That is,

```
library(ConfZIC)
```

The primary functions in this package are \textbf{RankReg} and \textbf{RankTS}. these functions help us to narrow down the number of models to look at in model selection, uses the minimum ZIC (Generalized Information Criteria)

More Details: Jayaweera I.M.L.N, Trindade A.A., ``How Certain are You in Your Minimum AIC and BIC Values?", Sankhya A (2023+)

Rank the regression models which lie in the given confidence envelope:

library("ConfZIC") data(Concrete) x=Concrete Y=x[,9] #dependent variable #independent variables X1=x[,1];X2=x[,2];X3=x[,3];X4=x[,4]; X5=x[,5];X6=x[,6];X7=x[,7];X8=x[,8]; mydata=cbind(Y,X1,X2,X3,X4,X5,X6,X7,X8) #data matrix RankReg(mydata,0.95,"BIC")

Testing two ZIC values in Regression

x=Concrete Y=x[,9] #dependent variable model1=lm(Y~X1) model2=lm(Y~X1+X2) regZIC.test(model1,model2,model_ZIC="BIC",data=mydata,alpha=0.05)

Rank the time series models which lie in the given confidence envelope based on minimum ZIC:

library("ConfZIC") data(Sunspots) x=Sunspots RankTS(x,max.p=13,max.q=13,0.95,"AICc")

Testing two ZIC values:

model1=try(arima(x,order=c(1,0,1),method="ML",include.mean=FALSE),silent = TRUE) model2=try(arima(x,order=c(1,0,0),method="ML",include.mean=FALSE),silent = TRUE) tsZIC.test(x,model1,model2,model_ZIC="AIC",alpha=0.05)

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