Man pages for gmwm
Generalized Method of Wavelet Moments
| acf_sum | Helper Function for ARMA to WV Approximation |
| all_bootstrapper | Bootstrap for Everything! |
| AR | Create an Autoregressive P [AR(P)] Process |
| AR1 | Create an Autoregressive 1 [AR(1)] Process |
| ar1_draw | Randomly guess starting parameters for AR1 |
| ar1_to_gm | Transform AR1 to GM |
| ar1_to_wv | AR1 process to WV |
| ARMA | Create an Autoregressive Moving Average (ARMA) Process |
| ARMAacf_cpp | Compute Theoretical ACF for an ARMA Process |
| arma_adapter | ARMA Adapter to ARMA to WV Process function |
| arma_draws | Randomly guess starting parameters for ARMA |
| ARMAtoMA_cpp | Converting an ARMA Process to an Infinite MA Process |
| arma_to_wv | ARMA process to WV |
| arma_to_wv_app | ARMA process to WV approximation |
| auto_imu | Find the auto imu result |
| auto.imu | Automatically select appropriate model for IMU |
| autoplot.auto.imu | Automatic Model Selection Results of IMU Object |
| autoplot.gmwm | Graph Solution of the Generalized Method of Wavelet Moments |
| autoplot.gmwm1 | Graph Solution of the Generalized Method of Wavelet Moments... |
| autoplot.gmwm2 | Graph Solution of the Generalized Method of Wavelet Moments... |
| autoplot.gmwmComp | Compare GMWM Model Fits with ggplot2 (Internal) |
| autoplot.gts | Plot Time Series Data |
| autoplot.imu2 | Plot the Wavelet Variances of IMU Object in Combined Type |
| autoplot.imu6 | Plot the Wavelet Variances of IMU Object in Split Type |
| autoplot.lts | Plot the Latent Time Series Graph |
| autoplot.wvar | Graph Wavelet Variances |
| autoplot.wvarComp | Detail Implementation to Compare Wavelet Variances |
| autoplot.wvar.imu | Plot the Wavelet Variances of IMU Object |
| avar | Calculate the Allan Variance |
| avar_mo_cpp | Compute Maximal-Overlap Allan Variance using Means |
| avar_to_cpp | Compute Tau-Overlap Allan Variance |
| batch_modwt_wvar_cpp | Computes the MO/DWT wavelet variance for multiple processes |
| B_matrix | B Matrix |
| boot_pval_gof | Generate the Confidence Interval for GOF Bootstrapped |
| bootstrap_gof_test | Compute the Bootstrapped GoF Test |
| brickwall | Brickwall functionality for MO/DWT |
| brick_wall | Removal of Boundary Wavelet Coefficients |
| build_model_set | Build List of Unique Models |
| calculate_psi_matrix | Calculate the Psi matrix |
| cfilter | Time Series Convolution Filters |
| ci_eta3 | Generate eta3 confidence interval |
| ci_eta3_robust | Generate eta3 robust confidence interval |
| ci_wave_variance | Generate a Confidence intervval for a Univariate Time Series |
| code_zero | Optim loses NaN |
| comb.mat | Create Combination Matrix |
| compare.gmwm | Graphically Compare GMWM Model Fit |
| compare.models | Graphically Compare GMWM Models Constructed by the Same Data |
| compare.wvar | Compare Wavelet Variances |
| compute_cov_cpp | Computes the (MODWT) wavelet covariance matrix |
| count_models | Count Models |
| cov_bootstrapper | Bootstrap for Matrix V |
| create_imu | Internal IMU Object Construction |
| create_wvar | Create a Wvar object |
| cust.model.score | Formats the model score matrix |
| decomp_theoretical_wv | Each Models Process Decomposed to WV |
| decomp_to_theo_wv | Decomposed WV to Single WV |
| demo.lts | Generate a Demo about the Latent Time Series |
| deriv_2nd_ar1 | Analytic second derivative matrix for AR(1) process |
| deriv_2nd_dr | Analytic second derivative matrix for drift process |
| deriv_ar1 | Analytic D matrix for AR(1) process |
| derivative_first_matrix | Analytic D matrix of Processes |
| deriv_dr | Analytic D matrix for drift process |
| deriv_qn | Analytic D matrix quantisation noise process |
| deriv_rw | Analytic D matrix random walk process |
| deriv_wn | Analytic D matrix white noise process |
| desc.to.ts.model | Create a ts.model from desc string |
| dft_acf | Discrete Fourier Transformation for Autocovariance Function |
| diff_cpp | Lagged Differences in Armadillo |
| D_matrix | Analytic D matrix of Processes |
| do_polyroot_arma | Root Finding C++ |
| do_polyroot_cpp | Root Finding C++ |
| DR | Create an Drift (DR) Process |
| dr_to_wv | Drift to WV |
| dwt | Discrete Wavelet Transform |
| dwt_cpp | Discrete Wavelet Transform |
| e_drift | Expected value DR |
| fast_cov_cpp | Computes the (MODWT) wavelet covariance matrix using... |
| field_to_matrix | Transform an Armadillo field<vec> to a matrix |
| find_full_model | Find the Common Denominator of the Models |
| format_ci | Format the Confidence Interval for Estimates |
| gen_ar1 | Generate an AR(1) sequence |
| gen_arma | Generate ARMA |
| gen_dr | Generate a drift |
| gen.gts | Create a GMWM TS Object based on model |
| gen_lts | Generate Latent Time Series based on Model (Internal) |
| gen.lts | Generate Latent Time Series Object Based on Model |
| gen_model | Generate Time Series based on Model (Internal) |
| gen_qn | Generate a Quantisation Noise (QN) sequence |
| gen_rw | Generate a random walk without drift |
| gen_wn | Generate a white noise process |
| getModel.gmwm | Get the model in a 'gmwm' object |
| getObjFun | Retrieve GMWM starting value from Yannick's objective... |
| getObjFunStarting | Retrieve GMWM starting value from Yannick's objective... |
| get_summary | Routing function for summary info |
| ggColor | Emulate ggplot2 default color palette |
| GM | Create a Gauss-Markov (GM) Process |
| gm_conv | GM Conversion |
| gm_to_ar1 | Transform GM to AR1 |
| gmwm | GMWM for Sensors, ARMA, SSM, and Robust |
| gmwm_engine | Engine for obtaining the GMWM Estimator |
| gmwm.imu | GMWM for (Robust) Sensor |
| gmwm_master_cpp | Master Wrapper for the GMWM Estimator |
| gmwm-package | Generalized Method of Wavelet Moments (GMWM) Package |
| gmwm_param_bootstrapper | Bootstrap for Estimating Both Theta and Theta SD |
| gmwm_sd_bootstrapper | Bootstrap for Standard Deviations of Theta Estimates |
| gmwm_update_cpp | Update Wrapper for the GMWM Estimator |
| gof_test | Compute the GOF Test |
| gts | Create a GMWM TS Object based on data |
| guess_initial | Randomly guess a starting parameter |
| guess_initial_old | Randomly guess a starting parameter |
| haar_filter | Haar filter construction |
| has | Obtain the value of an object's properties |
| idf_arma | Indirect Inference for ARMA |
| idf_arma_total | Indirect Inference for ARMA |
| imu | Create an IMU Object |
| install_imudata | Install IMU Data Package |
| invert_check | Check Invertibility Conditions |
| is_func | Is GMWM Object |
| is.whole | Integer Check |
| jacobian_arma | Calculates the Jacobian for the ARMA process |
| logit | Logit Function |
| logit2 | Logit Function |
| logit2_inv | Logit2 Inverse Function |
| logit_inv | Logit Inverse Function |
| lts | Generate Latent Time Series Object Based on Data |
| m2_drift | Second moment DR |
| MA | Create an Moving Average Q [MA(Q)] Process |
| mean_diff | Mean of the First Difference of the Data |
| minroot | Obtain the smallest polynomial root |
| Mod_cpp | Absolute Value or Modulus of a Complex Number. |
| model_objdesc | Generate the ts model object description |
| model_process_desc | Generate the ts model object's process desc |
| model_score | Model Score |
| model_theta | Generate the ts model object's theta vector |
| Mod_squared_cpp | Absolute Value or Modulus of a Complex Number Squared. |
| modwt | Maximum Overlap Discrete Wavelet Transform |
| modwt_cpp | Maximum Overlap Discrete Wavelet Transform |
| modwt_wvar_cpp | Computes the (MODWT) wavelet variance |
| obj_extract | Extract Object |
| optimism_bootstrapper | Bootstrap for Optimism |
| opt_n_gof_bootstrapper | Bootstrap for Optimism and GoF |
| order_AR1s | Order AR1s by size of phi. |
| orderModel | Order the Model |
| output.format | Formats the rank.models (auto.imu) object |
| packageVersionCRAN | Latest Version of Package on CRAN |
| paperSetting | Frequent Graph Setting for Paper |
| placeLegend | Place Legend |
| plot.auto.imu | Wrapper to Automatic Model Selection Results of IMU Object |
| plot.avar | Plot Allan Variance |
| plot.gmwm | Wrapper to Graph Solution of the Generalized Method of... |
| plot.gts | Plot Time Series Data |
| plot.lts | Wrapper Function to Plot the Graph of Latent Time Series |
| plot.wvar | Wrapper to ggplot Wavelet Variances Graph |
| plot.wvar.imu | Wrapper Function to Plot the Wavelet Variances of IMU Object |
| plus-.ts.model | Add ts.model objects together |
| predict.gmwm | Predict future points in the time series using the solution... |
| print.auto.imu | Print function for auto.imu object |
| print.avar | Prints Allan Variance |
| print_data | Print GMWM Data Object |
| print.dwt | Print Discrete Wavelet Transform |
| print.gmwm | Print gmwm object |
| print.modwt | Print Maximum Overlap Discrete Wavelet Transform |
| print.rank.models | Print function for rank.models object |
| print.summary.gmwm | Print summary.gmwm object |
| print.ts.model | Multiple a ts.model by constant |
| print.wvar | Print Wavelet Variances |
| print.wvcov | Print Asymptotic Covariance Matrix |
| pseudo_logit | Pseudo Logit Function |
| pseudo_logit_inv | Pseudo Logit Inverse Function |
| qmf | Quadrature Mirror Filter |
| QN | Create an Quantisation Noise (QN) Process |
| qn_to_wv | Quantisation Noise to WV |
| quantile_cpp | Find Quantiles |
| rank_models | Find the Rank Models result |
| rank.models | Automatically select appropriate model for a set of models |
| Rcpp_ARIMA | Hook into R's ARIMA function |
| read_imu | Read an IMU Binary File into R |
| read.imu | Read an IMU Binary File into R |
| rev_col_subset | Reverse Subset Column |
| reverse_vec | Reverse Armadillo Vector |
| rev_row_subset | Reverse Subset Row |
| rfilter | Time Series Recursive Filters |
| RW | Create an Random Walk (RW) Process |
| rw_to_wv | Random Walk to WV |
| scales_cpp | Computes the MODWT scales |
| select.desc.check | TS Model Checks |
| select_filter | Select the Wavelet Filter |
| seq_cpp | Generate a sequence of values |
| seq_len_cpp | Generate a sequence of values based on supplied number |
| sort_mat | Sort Matrix by Column |
| sub-.imu | Subset an IMU Object |
| sum_field_vec | Accumulation of Armadillo field<vec> |
| summary.auto.imu | Summary function for auto.imu object |
| summary.avar | Summary Allan Variance |
| summary.dwt | Summary Discrete Wavelet Transform |
| summary.gmwm | Summary of GMWM object |
| summary.modwt | Summary Maximum Overlap Discrete Wavelet Transform |
| summary.rank.models | Summary function for rank.models object |
| summary.wvar | Summary of Wavelet Variances |
| summary.wvcov | Summary Wavelet Covariance Matrix |
| theoretical_wv | Model Process to WV |
| theta_ci | Generate the Confidence Interval for Theta Estimates |
| times-.ts.model | Multiple a ts.model by constant |
| transform_values | Transform Values for Optimization |
| unitConversion | Convert Unit of Time Series Data |
| untransform_values | Revert Transform Values for Display |
| update.gmwm | Update GMWM object for sensor, ARMA, SSM, and Robust |
| update.lts | Update Object Attribute |
| update_obj | Update the Attributes of Objects |
| value | Obtain the value of an object's properties |
| var_drift | Variance DR |
| vector_to_set | Conversion function of Vector to Set |
| wave_variance | Generate a Wave Variance for a Univariate Time Series |
| WN | Create an White Noise (WN) Process |
| wn_to_wv | White Noise to WV |
| wvar | Wavelet Variance |
| wvar_cpp | Computes the (MODWT) wavelet variance |
| wvcov | Calculate the Asymptotic Covariance Matrix |
