autovarCore: Automated Vector Autoregression Models and Networks

Automatically find the best vector autoregression models and networks for a given time series data set. 'AutovarCore' evaluates eight kinds of models: models with and without log transforming the data, lag 1 and lag 2 models, and models with and without day dummy variables. For each of these 8 model configurations, 'AutovarCore' evaluates all possible combinations for including outlier dummies (at 2.5x the standard deviation of the residuals) and retains the best model. Model evaluation includes the Eigenvalue stability test and a configurable set of residual tests. These eight models are further reduced to four models because 'AutovarCore' determines whether adding day dummies improves the model fit.

AuthorAndo Emerencia [aut, cre]
Date of publication2015-07-01 14:41:42
MaintainerAndo Emerencia <ando.emerencia@gmail.com>
LicenseMIT + file LICENSE
Version1.0-0

View on CRAN

Man pages

apply_ln_transformation: Applies the natural logarithm to the data set

assess_joint_sktest: Tests the skewness and kurtosis of a VAR model

assess_kurtosis: Tests the kurtosis of a VAR model

assess_portmanteau: Tests the white noise assumption for a VAR model using a...

assess_portmanteau_squared: Tests the homeskedasticity assumption for a VAR model using a...

assess_skewness: Tests the skewness of a VAR model

autovar: Return the best VAR models found for a time series data set

autovarCore-package: Automated Vector Autoregression Networks

compete: Returns the winning model

day_dummies: Calculate day dummy variables

daypart_dummies: Calculate day-part dummy variables

explode_dummies: Explode dummies columns into separate dummy variables

impute_datamatrix: Imputes the missing values in the input data

invalid_mask: Calculate a bit mask to identify invalid outlier dummies

model_is_stable: Eigenvalue stability condition checking

model_score: Return the model fit for the given varest model

needs_trend: Determines if a trend is required for the specified VAR model

rcpp_hello_world: Simple function using Rcpp

residual_outliers: Calculate dummy variables to mask residual outliers

run_tests: Execute a series of model validity assumptions

run_var: Calculate the VAR model and apply restrictions

selected_columns: Convert an outlier_mask to a vector of column indices

select_valid_masks: Select and return valid dummy outlier masks

trend_columns: Construct linear and quadratic trend columns

validate_params: Validates the params given to the autovar function

validate_raw_dataframe: Validates the dataframe given to the autovar function

Files in this package

autovarCore
autovarCore/inst
autovarCore/inst/bash
autovarCore/inst/bash/install-package-dependencies.sh
autovarCore/inst/help_files
autovarCore/inst/help_files/index.html
autovarCore/inst/help_files/assess_portmanteau_squared.html
autovarCore/inst/help_files/needs_trend.html
autovarCore/inst/help_files/residual_outliers.html
autovarCore/inst/help_files/select_valid_masks.html
autovarCore/inst/help_files/autovarCore-package.html
autovarCore/inst/help_files/img
autovarCore/inst/help_files/img/glyphicons-halflings-white.png
autovarCore/inst/help_files/img/glyphicons-halflings.png
autovarCore/inst/help_files/daypart_dummies.html
autovarCore/inst/help_files/explode_dummies.html
autovarCore/inst/help_files/impute_datamatrix.html
autovarCore/inst/help_files/run_tests.html
autovarCore/inst/help_files/model_is_stable.html
autovarCore/inst/help_files/assess_joint_sktest.html
autovarCore/inst/help_files/validate_params.html
autovarCore/inst/help_files/css
autovarCore/inst/help_files/css/bootstrap.css
autovarCore/inst/help_files/css/bootstrap.min.css
autovarCore/inst/help_files/css/staticdocs.css
autovarCore/inst/help_files/css/bootstrap-responsive.css
autovarCore/inst/help_files/css/bootstrap-responsive.min.css
autovarCore/inst/help_files/css/highlight.css
autovarCore/inst/help_files/validate_raw_dataframe.html
autovarCore/inst/help_files/assess_kurtosis.html
autovarCore/inst/help_files/trend_columns.html
autovarCore/inst/help_files/run_var.html
autovarCore/inst/help_files/rcpp_hello_world.html
autovarCore/inst/help_files/assess_portmanteau.html
autovarCore/inst/help_files/autovar.html
autovarCore/inst/help_files/invalid_mask.html
autovarCore/inst/help_files/compete.html
autovarCore/inst/help_files/assess_skewness.html
autovarCore/inst/help_files/js
autovarCore/inst/help_files/js/bootstrap.js
autovarCore/inst/help_files/js/bootstrap.min.js
autovarCore/inst/help_files/apply_ln_transformation.html
autovarCore/inst/help_files/day_dummies.html
autovarCore/inst/help_files/selected_columns.html
autovarCore/inst/help_files/model_score.html
autovarCore/tests
autovarCore/tests/testthat.R
autovarCore/tests/testthat
autovarCore/tests/testthat/test_model_score.r
autovarCore/tests/testthat/test_model_is_stable.r
autovarCore/tests/testthat/test_needs_trend.r
autovarCore/tests/testthat/test_apply_ln_transformation.r
autovarCore/tests/testthat/test_autovar.r
autovarCore/tests/testthat/test_assess_kurtosis.r
autovarCore/tests/testthat/test_validate_raw_dataframe.r
autovarCore/tests/testthat/test_assertions.r
autovarCore/tests/testthat/test_explode_dummies.r
autovarCore/tests/testthat/test_impute_datamatrix.r
autovarCore/tests/testthat/test_daypart_dummies.r
autovarCore/tests/testthat/test_config.r
autovarCore/tests/testthat/test_assess_skewness.r
autovarCore/tests/testthat/test_validate_params.r
autovarCore/tests/testthat/test_assess_portmanteau_squared.r
autovarCore/tests/testthat/test_run_var.r
autovarCore/tests/testthat/test_assess_portmanteau.r
autovarCore/tests/testthat/test_select_valid_masks.r
autovarCore/tests/testthat/test_selected_columns.r
autovarCore/tests/testthat/test_residual_outliers.r
autovarCore/tests/testthat/test_assess_joint_sktest.r
autovarCore/tests/testthat/test_day_dummies.r
autovarCore/tests/testthat/test_compete.r
autovarCore/tests/testthat/test_run_tests.r
autovarCore/tests/testthat/test_trend_columns.r
autovarCore/tests/testthat/test_invalid_mask.r
autovarCore/src
autovarCore/src/rcpp_hello_world.cpp
autovarCore/src/RcppExports.cpp
autovarCore/NAMESPACE
autovarCore/R
autovarCore/R/autovar.r
autovarCore/R/daypart_dummies.r
autovarCore/R/on_unload.r
autovarCore/R/trend_columns.r
autovarCore/R/apply_ln_transformation.r
autovarCore/R/model_is_stable.r
autovarCore/R/selected_columns.r
autovarCore/R/assess_portmanteau_squared.r
autovarCore/R/run_tests.r
autovarCore/R/assess_kurtosis.r
autovarCore/R/validate_raw_dataframe.r
autovarCore/R/invalid_mask.r
autovarCore/R/assess_portmanteau.r
autovarCore/R/explode_dummies.r
autovarCore/R/select_valid_masks.r
autovarCore/R/compete.r
autovarCore/R/RcppExports.R
autovarCore/R/assess_joint_sktest.r
autovarCore/R/model_score.r
autovarCore/R/run_var.r
autovarCore/R/impute_datamatrix.r
autovarCore/R/assess_skewness.r
autovarCore/R/needs_trend.r
autovarCore/R/assertions.r
autovarCore/R/config.r
autovarCore/R/day_dummies.r
autovarCore/R/validate_params.r
autovarCore/R/residual_outliers.r
autovarCore/README.md
autovarCore/MD5
autovarCore/DESCRIPTION
autovarCore/man
autovarCore/man/residual_outliers.Rd autovarCore/man/model_score.Rd autovarCore/man/trend_columns.Rd autovarCore/man/run_var.Rd autovarCore/man/model_is_stable.Rd autovarCore/man/select_valid_masks.Rd autovarCore/man/validate_raw_dataframe.Rd autovarCore/man/compete.Rd autovarCore/man/impute_datamatrix.Rd autovarCore/man/run_tests.Rd autovarCore/man/assess_portmanteau.Rd autovarCore/man/invalid_mask.Rd autovarCore/man/selected_columns.Rd autovarCore/man/apply_ln_transformation.Rd autovarCore/man/assess_portmanteau_squared.Rd autovarCore/man/assess_kurtosis.Rd autovarCore/man/explode_dummies.Rd autovarCore/man/needs_trend.Rd autovarCore/man/autovar.Rd autovarCore/man/validate_params.Rd autovarCore/man/assess_joint_sktest.Rd autovarCore/man/assess_skewness.Rd autovarCore/man/daypart_dummies.Rd autovarCore/man/day_dummies.Rd autovarCore/man/autovarCore-package.Rd autovarCore/man/rcpp_hello_world.Rd
autovarCore/LICENSE

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