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.

Install the latest version of this package by entering the following in R:
install.packages("autovarCore")
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

Functions

apply_ln_transformation Man page
assess_joint_sktest Man page
assess_kurtosis Man page
assess_portmanteau Man page
assess_portmanteau_squared Man page
assess_skewness Man page
autovar Man page
autovarCore-package Man page
compete Man page
day_dummies Man page
daypart_dummies Man page
explode_dummies Man page
impute_datamatrix Man page
invalid_mask Man page
model_is_stable Man page
model_score Man page
needs_trend Man page
rcpp_hello_world Man page
residual_outliers Man page
run_tests Man page
run_var Man page
selected_columns Man page
select_valid_masks Man page
trend_columns Man page
validate_params Man page
validate_raw_dataframe Man page

Files

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

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