Package index • beacon

All functions

ACF(): Plot Auto Correlation Function (ACF)

Autoregressive(): Fit AR model using either single or ensemble approach

BandPass(): Apply band-pass filter to `ts`

C.Burg(): AR Burg core (.Call)

CCF(): Plot Cross Correlation Function (CCF)

DQ_Dec2Bits(): Convert Decimal DQ Flag to Bit Representation

DQ_ShortNames(): List of DQ Bit Names with Their Bit Positions

DQlev(): Extract Specific DQ Level from Decimal Flag

Differencing(): Difference a time series manually or automatically

MovingAverage(): Apply MA or EoA depending on `q`

PACF(): Plot Partial Auto Correlation Function (PACF)

Significance(): Detection Significance from Probability

add_DQ(): Add DQ information to the processed data

add_P0(): Add combined significance probability P0

add_P0_DQ(): Add P0_DQ mask to processed data

add_Pexp(): Add exponential inter-cluster probability

add_Ppois(): Add Poisson anomaly-tail probability to dataframe

add_Pstats(): Add full statistical significance fields (Ppois, Pexp, P0)

add_stat(): Join cluster-level statistics into proc dataframe

adjust_proc(): Adjust anomaly detection results for current batch

amax(): Calculate maximum of absolute values

amin(): Calculate minimum of absolute values

anomalize2(): Detect anomalies in a data frame column using robust statistical methods

anomaly(): Detect anomalies in time series using robust methods

append.line(): Append a Line to an Existing File

append_NA(): Append NA placeholders to result list

apply_pca(): Perform PCA with fixed sign for interpretability

arch(): Run full architecture: denoising, anomaly detection, and clustering

ari_mean(): Arithmetic Mean Ignoring NA

as_tbt(): Convert `ts` to tibbletime object

auto_diff(): Automatically determine differencing order via KPSS

batching(): Split a time series into fixed-length batches

batching.network(): Perform batching and NA filtering for multiple ts objects

beacon-package beacon: A short title line describing what the package does

burgar(): Fit AR model using modified Burg method

ceiling_digit(): Ceiling with Decimal Digits

check_normality(): Check for normality using the Anderson-Darling test

check_stationary(): Check for stationarity using the KPSS test across segments

coincide_P0(): Coincidence Analysis Using P0 Statistics

concat_ts(): Concatenate with previous tail to form new ts

config_pipe(): Get default pipeline configuration

copy_attr(): Copy Attributes from Reference Object

correlate(): Correlate two complex vectors

create_wd(): Create Working Directory (WD) Recursively with Optional Suffix

crop_to(): Handy window function with index

cutoff_to(): Crop frequency series to a given range

cyclic(): Cyclically shift a vector

decomp_freq_trend(): Estimate frequency/trend windows from ACF with spectral fallback

deltaf(): Extract Frequency Resolution from `fs` Object

deltam_f(): Convert mismatch to fractional spacing

.iter_freqs(): Iterate over tile center frequencies for a given Q

.iter_qs(): Iterate over the Q values

download_event(): Download dataset/strain-file for a GWOSC event (v2, sampling.freq-aware)

dur(): Get Duration of the Original Time Series

ear(): Ensemble AR model fitting across multiple orders

embedParallelCpp(): C++ version of embed function

eoa(): Apply Ensemble of Averages (EoA)

evenify(): Ensure even-length `ts`

extract_pc(): Extract principal component(s) from input

floor_digit(): Floor with Decimal Digits

freqs(): Get Frequency Samples from `fs`

fs(): Create a Frequency Series Object (`fs`)

fs_df(): Convert `fs` to Data Frame

geo_mean(): Geometric Mean Ignoring NA

gesd2(): Detect outliers using robust GESD method

get_MissingValues(): Sequential ARIMA pipeline components

get_antpatt(): Compute antenna pattern for a detector

get_available_detectors(): Get available GW detectors

get_break_rngdgt(): Compute break points with rounded range

get_cutoff_indices(): Get frequency cutoff indices for overlap calculation

get_dqmask(): Reconstruct dqmask as a ts or mts object

get_gps(): Add GPS time column to a data frame

get_gwosc_param(): Get GWOSC parameters for one or more GW events

get_last_tcen(): Extract the most recent cluster center time

get_light_travel_time_among_detectors(): Compute maximum light travel time between detectors

get_limit(): Get limit range from ts by scaled maximum value

get_order(): Calculate the order of magnitude

get_wave(): Generate gravitational-wave signal from PyCBC

gps2utc(): Convert GPS time to UTC

har_mean(): Harmonic Mean Ignoring NA

init_pipe(): Initialize pipeline environment

inner(): Compute inner product between two vectors

interp2d(): 2D Interpolation for Surface Data

interp_psd(): Interpolate PSD Return a new PSD that has been interpolated to the desired delta_f

inv_spec_trunc_psd(): Inverse Spectrum Truncation

iqr2(): Identify outliers using robust IQR method

is.anomdet(): Check if anomalies were detected

legend_inside(): Theme wrapper for legend inside plot area

list.append(): Append or assign a value into a nested list

list_gwosc_event(): List GW event nicknames from GWOSC (API v2)

list_gwosc_param(): List all available GWOSC parameter names

ma(): Two-sided Centered Moving Average Smoother

mae(): Median Absolute Error for sample

matched_filter(): Matched filter (R ver.)

message_by(): Print Message Conditionally by Iteration

message_verb(): Conditionally Print Message

mmed(): Apply median filter to a `ts`

mode(): Mode

oom(): Order of Magnitude

oscillo_option(): Common ggplot Components for Oscillogram Plot

overlap(): Calculate real overlap

overlap_cplx(): Calculate complex overlap

pad(): Pad a `ts` with zeros

paste_wd(): Combine Working Directory Path with File Name

pipe(): Main anomaly detection pipeline for a single detector

pipe_net(): Run anomaly detection pipeline over detector network

plot(<fs>): Plot a Frequency Series (`fs`)

plot_aic(): Plot AIC curve for AR model

plot_anomaly(): Plot anomalies with error bands and significance

plot_background(): Plot histogram of background detection statistic

plot_coinc(): Plot coincidence significance values over time

plot_lambda(): Plot lambda values over time

plot_onsource(): Plot on-source detection statistic as discrete points

plot_oscillo(): Plot a Single Oscillogram

plot_oscillo_multi(): Plot Multiple Oscillograms in Facet Layout

plot_spectro(): Plot Q-Transform Spectrogram with Oscillogram

ppois.anom(): Compute anomaly-tail Poisson probability

print(<fs>): Custom Print Method for `fs` Class

proc2ts(): Convert processed data frame to time series

proj_network(): Project GW polarizations onto multiple detectors

proj_wave(): Project GW polarizations onto detector

psd(): Generate power spectral density (PSD) estimate using Welch’s method

psd_noise(): Simulate Gaussian noise from detector PSD

qplane(): Compute Q-plane across all Q and f0 tiles

qseries(): Perform Q-transform on a single tile

qtiling(): Generate Q-tiling scheme

qtransform(): Q-transform of time series

range_width(): Rounded Range with Specified Bin Width

read_DQ(): Read Only the DQ Mask from HDF5 File

read_H5(): Read HDF5 Time Series Data with Data Quality Mask

relpass_time(): Relative arrival times of signal at detectors

reproduce(): Reproduce the `proc` and coincidence result for a specific batch

residual(): Compute residuals using AR coefficients

resize(): Resize a `ts` to target length

run_dbscan(): Run DBSCAN clustering on detected anomalies

sample_dist(): Sample from a fitted distribution based on a population

sar(): Simple wrapper for AR fitting

savefig(): Save a ggplot Object to File

savemsg(): Save a Message to a Text File

savetab(): Save a Data Frame as a Markdown-Formatted Table

se(): Standard Error for sample

seqarima(): Apply sequential ARIMA pipeline

shift(): Shift time series by time offset

shift_cyclic(): Cyclically shift a `ts`

shift_phase(): Align phase of a `ts` to another

sigmasq(): Estimate signal loudness (sigma squared)

sinking(): Save console output to a file

sprint(): Print Formatted String Using cat and sprintf

stat_anom(): Compute anomaly cluster statistics

str(<fs>): Custom Structure Summary for `fs` Object

stream(): Run full anomaly detection stream over multiple batches

suppressALL(): Suppress all output, messages, and warnings

sys_cat(): Print Contents of a File to Console

tf(): Final timestamp of a time series

ti(): Initial timestamp of a time series

tl(): Duration of a time series

to_fs(): Convert Time Series (`ts`) to Frequency Series (`fs`)

to_ts(): Inverse Fourier transform of `fs` object

tr(): Time range of a time series

tr_overlap(): Compute temporal overlap for ARIMA-based filtering

transpose.List(): Reshape a list-of-lists by transposing elements

travel_times(): Light travel time between detectors

ts_df(): Convert `ts` to data frame with time column

tsfy(): Convert to `ts` object using a reference

tukey_window(): Generate a Tukey Window

uniqdif(): Wrapper of unique(diff(x)) with tolerance check

unit_denormalize(): Denormalize a unit-normalized `ts` object

unit_normalize(): Normalize a `ts` to unit scale

update_logic(): Update logic for statistics with optional P0-based filtering

update_stat(): Update accumulated cluster-level statistics

utc2gps(): Convert UTC to GPS time

vf(): Simple final value extractor

vi(): Simple initial value extractor

vl(): Length from first to last value

vr(): Simple range extractor

weighted_inner(): Weighted inner product

whiten(): Spectral Whitening

window_to(): Handy window function

write_H5(): Write a time series and metadata into HDF5 format