# Automated Uncertainty Analysis

## Documentation for package ‘ldt’ version 0.5.3

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS

## Help Pages

adjust_indices_after_remove | Adjust Indices in a List
AIC.ldt.estim | Akaike Information Criterion
BIC.ldt.estim | Bayesian Information Criterion
boxCoxTransform | Box-Cox Transformation of Numeric Matrix
coef.ldt.estim | Extract Coefficients Matrix
coefs.table | Create Table of Coefficients
combine.search | Combine a List of 'ldt.search' Objects
data.berka | Berka and Sochorova (1993) Dataset for Loan Default
data.pcp | IMF's Primary Commodity Prices
data.wdi | Long-run Growth from World Development Indicator Dataset
endogenous | Extract Endogenous Variable(s) Data
eqList2Matrix | Convert a List of Equations to a Matrix
estim.bin | Estimate a Binary Choice Model
estim.binary.model.string | Get Model Name
estim.sur | Estimate a SUR Model
estim.varma | Estimate a VARMA Model
estim.varma.model.string | Get the Specification of an 'ldt.estim.varma' Model
exogenous | Extract Exogenous Variable(s) Data
fan.plot | Fan Plot Function
fitted.ldt.estim | Extract Fitted Data
get.combinations | Define Combinations for Search Process
get.data | Transform and Prepare Data for Analysis
get.data.append.newX | Append 'newX' to 'data$data' matrix.
get.data.check.discrete | Check if a column is discrete
get.data.check.intercept | Check for an intercept in a matrix
get.data.keep.complete | Remove Rows with Missing Observations from Data
get.indexation | Get Numeric Indices in a Combination
get.options.lbfgs | Get Options for L-BFGS Optimization
get.options.neldermead | Options for Nelder-Mead Optimization
get.options.newton | Get Options for Newton Optimization
get.options.pca | Get Options for PCA
get.options.roc | Get Options for ROC and AUC Calculations
get.search.items | Specify the Purpose of the Model Search Process
get.search.metrics | Get Options for Measuring Performance
get.search.modelchecks | Set Options to Exclude a Model Subset
get.search.options | Get Extra Options for Model Search Process
get.varma.params | Split VARMA parameter into its Components
logLik.ldt.estim | Extract Maximum Log-Likelihood
plot.ldt.estim | Plot Diagnostics for 'ldt.estim' Object
plot.ldt.varma.prediction | Plot Predictions from a VARMA model
predict.ldt.estim | Extract Prediction Results
predict.ldt.estim.varma | Extract Prediction Results from a 'ldt.estim.varma' Object
print.ldt.estim | Prints an 'ldt.estim' object
print.ldt.estim.projection | Prints an 'ldt.estim.projection' object
print.ldt.list | Prints an 'ldt.list' object
print.ldt.search | Prints an 'ldt.search' object
print.ldt.varma.prediction | Prints an 'ldt.varma.prediction' object
rand.mnormal | Generate Random Samples from a Multivariate Normal Distribution
residuals.ldt.estim | Extract Residuals Data
s.cluster.h | Hierarchical Clustering
s.cluster.h.group | Group Variables with Hierarchical Clustering
s.combine.stats4 | Combine Mean, Variance, Skewness, and Kurtosis This function combines two sets of mean, variance, skewness, and kurtosis and generates the combined statistics.
s.distance | Get the Distances Between Variables
s.gld.density.quantile | GLD Density-Quantile Function
s.gld.from.moments | Get the GLD Parameters from the moments
s.gld.quantile | GLD Quantile Function
s.metric.from.weight | Convert a Weight to Metric
s.pca | Principal Component Analysis
s.roc | Get ROC Curve Data for Binary Classification
s.weight.from.metric | Convert a Metric to Weight
search.bin | Create a Model Set for Binary Choice Models
search.rfunc | Create a Model Set for an R Function
search.steps | Step-wise estimation
search.sur | Create a Model Set for SUR Models
search.varma | Create Model Set for VARMA Models
sim.bin | Generate Random Sample from a DC Model
sim.sur | Generate Random Sample from an SUR Model
sim.varma | Generate Random Sample from a VARMA Model
summary.ldt.search | Summary for an 'ldt.search' object
summary.ldt.search.item | Summary for an 'ldt.search.item' object