Toolbox for Model Building and Forecasting
Error measures for an estimated model
Function extracts the actual values from the function
Automatic Identification of Demand
Asymmetric Laplace Distribution
Augmented Linear Model
Functions that extracts type of error from the model
Measures of association
Box-Cox Normal Distribution
Coefficients of the model and their statistics
Bootstrap for parameters of models
Number of parameters and number of variates in the model
Calculate Cramer's V for categorical variables
DST and Leap year detector functions
Coefficients of determination
Distribution functions of the greybox package
Data Shape Replication Bootstrap
Error measures
Functions to extract scale and standard error from a model
Folded Normal Distribution
The generalized normal distribution
Linear graph construction function
Grey box
This function calculates parameters for the polynomials
Half moment of a distribution and its derivatives.
Implant the scale model in the location model
Corrected Akaike's Information Criterion and Bayesian Information Crit...
Greybox classes checkers
Forecasting using greybox functions
Laplace Distribution
Combine regressions based on information criteria
Combine regressions based on point information criteria
Logit Normal Distribution
Multiple correlation
Error measures for the provided forecasts
Outlier detection and matrix creation
Partial correlations
Pinball function
Plots of the fit and residuals
Point AIC
Point likelihood values
Rectified Normal Distribution
Objects exported from other packages
Regression for Multiple Comparison with the Best
Rolling Origin
S Distribution
Scale Model
Construct scatterplot / boxplots for the data
Stepwise selection of regressors
Construct a plot for categorical variable
Dummy variables for provided seasonality type
Three Parameter Log Normal Distribution
Exogenous variables expander
Exogenous variables cross-products
Exogenous variables transformer
Implements functions and instruments for regression model building and its application to forecasting. The main scope of the package is in variables selection and models specification for cases of time series data. This includes promotional modelling, selection between different dynamic regressions with non-standard distributions of errors, selection based on cross validation, solutions to the fat regression model problem and more. Models developed in the package are tailored specifically for forecasting purposes. So as a results there are several methods that allow producing forecasts from these models and visualising them.