ACF | Auto-Covariance and Correlation Functions |
acf_sum | Helper Function for ARMA to WV Approximation |
adis_wv | Wavelet variance of IMU Data from an ADIS 16405 sensor |
ar1_to_wv | AR(1) process to WV |
arma11_to_wv | ARMA(1,1) to WV |
ARMAacf_cpp | Compute Theoretical ACF for an ARMA Process |
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 |
av_ar1 | Calculate Theoretical Allan Variance for Stationary... |
av_wn | Calculate Theoretical Allan Variance for Stationary White... |
batch_modwt_wvar_cpp | Computes the MO/DWT wavelet variance for multiple processes |
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 interval for a Univariate Time Series |
compare_wvar | Comparison Between Multiple Wavelet Variances |
compare_wvar_no_split | Combined Plot Comparison Between Multiple Wavelet Variances |
compare_wvar_split | Multi-Plot Comparison Between Multiple Wavelet Variances |
create_wvar | Create a 'wvar' object |
decomp_theoretical_wv | Each Models Process Decomposed to WV |
decomp_to_theo_wv | Decomposed WV to Single WV |
dft_acf | Discrete Fourier Transformation for Autocovariance Function |
diff_cpp | Lagged Differences in Armadillo |
diff_inv | Discrete Intergral: Inverse Difference |
dot-acf | Auto-Covariance and Correlation Functions |
dr_to_wv | Drift to WV |
dwt | Discrete Wavelet Transform |
dwt_cpp | Discrete Wavelet Transform |
imar_wv | Wavelet variance of IMU Data from IMAR Gyroscopes |
kvh1750_wv | Wavelet variance of IMU Data from a KVH1750 IMU sensor |
ln200_wv | Wavelet variance of IMU Data from a LN200 sensor |
ma1_to_wv | Moving Average Order 1 (MA(1)) to WV |
mean_diff | Mean of the First Difference of the Data |
modwt | Maximum Overlap Discrete Wavelet Transform |
modwt_cpp | Maximum Overlap Discrete Wavelet Transform |
modwt_wvar_cpp | Computes the (MODWT) wavelet variance |
navchip_wv | Wavelet variance of IMU Data from a navchip sensor |
num_rep | Replicate a Vector of Elements n times |
plot.auto_corr | Auto-Covariance and Correlation Functions |
plot.dwt | Plot Discrete Wavelet Transform |
plot.imu_wvar | Plot Wavelet Variance based on IMU Data |
plot.modwt | Plot Maximum Overlap Discrete Wavelet Transform |
plot.wccv_pair | Plot Cross Covariance Pair |
plot.wvar | Plot Wavelet Variance |
print.dwt | Print Discrete Wavelet Transform |
print.modwt | Print Maximum Overlap Discrete Wavelet Transform |
print.wvar | Print Wavelet Variances |
qn_to_wv | Quantisation Noise (QN) to WV |
quantile_cpp | Find Quantiles |
rfilter | Time Series Recursive Filters |
robust_eda | Comparison between classical and robust Wavelet Variances |
rw_to_wv | Random Walk to WV |
sarma_calculate_spadding | Calculates Length of Seasonal Padding |
sarma_components | Determine parameter expansion based upon objdesc |
sarma_expand | Expand Parameters for an SARMA object |
sarma_expand_unguided | (Internal) Expand the SARMA Parameters |
sarma_objdesc | Create the ts.model obj.desc given split values |
sarma_params_construct | Efficient way to merge items together |
scales_cpp | Computes the MODWT scales |
seq_cpp | Generate a sequence of values |
seq_len_cpp | Generate a sequence of values based on supplied number |
sp_hfilter | Haar filter for a spatial case |
sp_modwt_cpp | Compute the Spatial Wavelet Coefficients |
summary.dwt | Summary Discrete Wavelet Transform |
summary.modwt | Summary Maximum Overlap Discrete Wavelet Transform |
summary.wvar | Summary of Wavelet Variances |
theoretical_wv | Model Process to WV |
unitConversion | Convert Unit of Time Series Data |
wave_variance | Generate a Wave Variance for a Univariate Time Series |
wccv | Cross Covariance of Matrix |
wccv_get_y | Mapping to log10 scale |
wccv_pair | Cross Covariance of a TS Pair |
wn_to_wv | Gaussian White Noise to WV |
wv | wv |
wvar | Wavelet Variance |
wvar_cpp | Computes the (MODWT) wavelet variance |
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