Automated Uncertainty Analysis
Specify the Purpose of the Model Search Process
Get Options for Measuring Performance
Set Options to Exclude a Model Subset
Get Extra Options for Model Search Process
Split VARMA parameter into its Components
Extract Maximum Log-Likelihood
Adjust Indices in a List
Akaike Information Criterion
Bayesian Information Criterion
Box-Cox Transformation of Numeric Matrix
Extract Coefficients Matrix
Create Table of Coefficients
Combine a List of ldt.search Objects
Extract Endogenous Variable(s) Data
Convert a List of Equations to a Matrix
Estimate a Binary Choice Model
Get Model Name
Estimate a SUR Model
Get the Specification of an ldt.estim.varma Model
Estimate a VARMA Model
Extract Exogenous Variable(s) Data
Fan Plot Function
Extract Fitted Data
Define Combinations for Search Process
Append newX to data$data matrix.
Check if a column is discrete
Check for an intercept in a matrix
Remove Rows with Missing Observations from Data
Transform and Prepare Data for Analysis
Get Numeric Indices in a Combination
Get Options for L-BFGS Optimization
Options for Nelder-Mead Optimization
Get Options for Newton Optimization
Get Options for PCA
Get Options for ROC and AUC Calculations
Plot Diagnostics for ldt.estim Object
Plot Predictions from a VARMA model
Extract Prediction Results
Extract Prediction Results from a ldt.estim.varma Object
Prints an ldt.estim.projection object
Prints an ldt.estim object
Prints an ldt.list object
Prints an ldt.search object
Prints an ldt.varma.prediction object
Generate Random Samples from a Multivariate Normal Distribution
Extract Residuals Data
Group Variables with Hierarchical Clustering
Hierarchical Clustering
Combine Mean, Variance, Skewness, and Kurtosis This function combines ...
Get the Distances Between Variables
GLD Density-Quantile Function
Get the GLD Parameters from the moments
GLD Quantile Function
Convert a Weight to Metric
Principal Component Analysis
Get ROC Curve Data for Binary Classification
Convert a Metric to Weight
Create a Model Set for Binary Choice Models
Create a Model Set for an R Function
Step-wise estimation
Create a Model Set for SUR Models
Create Model Set for VARMA Models
Generate Random Sample from a DC Model
Generate Random Sample from an SUR Model
Generate Random Sample from a VARMA Model
Summary for an ldt.search.item object
Summary for an ldt.search object
Methods and tools for model selection and multi-model inference (Burnham and Anderson (2002) <doi:10.1007/b97636>, among others). 'SUR' (for parameter estimation), 'logit'/'probit' (for binary classification), and 'VARMA' (for time-series forecasting) are implemented. Evaluations are both in-sample and out-of-sample. It is designed to be efficient in terms of CPU usage and memory consumption.