rank_models

data

A <code>vector<vector<string>></vector<string></code> that gives a list of models to test.

model_str

A <code>vector<string></string></code> that contains the largest / full model.

full_model

A <code>double</code> that indicates the alpha level for CIs.

alpha

A <code>string</code> indicating the type of V matrix to generate

compute_v

A <code>string</code> that describes the model generation / transformation: 'ssm' or 'imu'

model_type

A <code>int</code> that controls how many times the GMWM is run.

A <code>int</code> that controls how many bootstraps occur.

A <code>int</code> that controls how many guesses occur.

A <code>bool</code> that indicates whether to use classical or robust wavelet variance.

robust

A <code>double</code> that indicates the efficiency to use.

A <code>bool</code> that indicates whether the model selection score should be calculated with bootstrap or asymptotics.

bs_optimism


Provides the core material to create an S3 object for rank.models


internal

Generalized Method of Wavelet Moments (GMWM) is an estimation
technique for the parameters of time series models. It uses the wavelet variance
in a moment matching approach that makes it particularly suitable for the
estimation of certain state-space models. Furthermore, there exists a robust
implementation of GMWM, which allows the robust estimation of some state-space
models and ARIMA models. Lastly, the package provides the ability to quickly
generate time series data, perform different wavelet decompositions, and
visualizations.

rank_models: Find the Rank Models result

Description

Usage

Arguments

Value