Function reference • casnet

All functions
`ColouredNoise`	Coloured noise data
`RNG`	Random Number Sequences
`SWtestE()`	Small World test
`ac_win()`	Windowed autocorrelation function
`add_alpha()`	Add transparency to a colour
`as.numeric_character()`	Character vector to named numeric vector
`as.numeric_discrete()`	Discrete (factor or character) to numeric vector
`as.numeric_factor()`	Numeric factor to numeric vector
`bandReplace()`	Replace matrix diagonals
`checkPkg()`	Check package
`createCorridor()`	Corridor analysis
`dc_ccp()`	Cumulative Complexity Peaks (CCP)
`dc_d()`	Distribution Uniformity
`dc_f()`	Fluctuation Intensity
`dc_win()`	Dynamic Complexity
`eig_win()`	Windowed Eigenvalue (PCA)
`elascer()`	Elastic Scaler - A Flexible Rescale Function
`est_emDim()`	Estimate number of embedding dimensions
`est_emLag()`	Estimate embedding lag (tau)
`est_parameters()`	Estimate RQA parameters
`est_radius()`	Estimate Radius.
`est_radius_rqa()`	Estimate Radius without building a recurrence matrix
`fd_RR()`	Relative Roughness
`fd_allan()`	Allan Variance Analysis
`fd_boxcount2D()`	2D Boxcount for 1D signal
`fd_dfa()`	Detrended Fluctuation Analysis (DFA)
`fd_mfdfa()`	Multi-fractal Detrended Fluctuation Analysis
`fd_psd()`	Power Spectral Density Slope (PSD).
`fd_sda()`	Standardised Dispersion Analysis (SDA).
`fd_sev()`	Calculate FD using Sevcik's method
`flight_Cauchy()`	Create Cauchy Flight
`flight_LevyPareto()`	Create a Levy-Pareto flight
`flight_Rayleigh()`	Create Rayleigh Flight (Brownian Motion)
`fnn()`	False Nearest Neighbours
`getColours()`	Get some nice colours
`getPairs()`	Get all combinations
`get_os()`	Which OS is running?
`growth_ac()`	Examples of dynamical growth models (maps)
`growth_ac_cond()`	Examples of conditional dynamical growth models (maps)
`inf_MSE()`	Multi-Scale Entropy
`inf_SampEn()`	Sample Entropy
`is.date()`	It's a Date!
`layout_as_spiral()`	Layout a graph on a spiral
`lv_Ndim()`	Lotka-Volterra model for N species
`make_spiral_graph()`	Make Spiral Graph
`manyAnalystsESM`	Data from the Many Analysts study.
`mat_coursegrain()`	Course grain a matrix for plotting
`mat_di2bi()`	Distance to binary matrix
`mat_di2ch()`	Distance to chromatic matrix
`mat_di2we()`	Distance to weighted matrix
`mat_hamming()`	Calculate Hamming distance
`mat_ind()`	Get indices of matrix diagonals, rows, or columns
`mat_mat2ind()`	Matrix to indexed data frame
`mat_we2bi()`	Weighted to Binary matrix
`mi_interlayer()`	Inter-layer mutual information
`mi_mat()`	Mutual Information variations
`mif()`	Mutual Information Function
`mrn()`	Multiplex Recurrence Network
`mrn_plot()`	Multiplex Recurrence Network Plot
`noise_fBm()`	Generate fractional Brownian motion
`noise_fGn()`	Generate fractional Gaussian noise
`noise_powerlaw()`	Generate noise series with power law scaling exponent
`plotDC_ccp()`	Plot Cumulative Complexity Peaks
`plotDC_lvl()`	Plot Peaks versus Levels
`plotDC_res()`	Plot Complexity Resonance Diagram
`plotFD_loglog()`	Plot output from fluctuation analyses based on log-log regression
`plotMRN_win()`	Plot windowed Multiplex Recurrence Network measures
`plotNET_BA()`	Example of Barabasi scale-free network
`plotNET_SW()`	Example of Strogatz-Watts small-world network
`plotNET_groupColour()`	Vertex and Edge Group Colours
`plotNET_groupWeight()`	Set Edge weights by group
`plotNET_prep()`	Plot Network Based on RQA
`plotRED_acf()`	Plot ACF and PACF
`plotRED_mif()`	Plot various MI functions
`plotRN_phaseDensity()`	Phase Density for each dimension
`plotRN_phaseProfile()`	Profile Plot
`plotRN_phaseSeries()`	Phase Series plot
`plotSUR_hist()`	Surrogate Test
`plotTS_multi()`	Plot Multivariate Time Series Data
`repmat()`	Repeat Copies of a Matrix
`rn()`	Create a Recurrence Network Matrix
`rn_measures()`	Recurrence Network Measures
`rn_phases()`	Extract Phases from weighted RN
`rn_plot()`	Plot (thresholded) distance matrix as a network
`rn_recSpec()`	Recurrence Time Spectrum
`rn_strengthDist()`	Strength versus Degree scaling relation
`rn_transition()`	Create transition network
`rp()`	Create a Distance Matrix
`rp_cl()`	Fast (C)RQA (command line crp)
`rp_copy_attributes()`	Copy Matrix Attributes
`rp_diagProfile()`	Diagonal Recurrence Profile
`rp_lineDist()`	Line length distributions
`rp_measures()`	Get (C)RQA measures based on a binary matrix
`rp_nzdiags()`	rp_nzdiags
`rp_plot()`	Plot (thresholded) distance matrix as a recurrence plot
`rp_size()`	rp_size
`rqa_fast()`	Fast rqa
`rqa_lineDist()`	Fast line dist
`rqa_measures()`	Fast RQA
`rqa_par()`	Massively Parallel RQA analysis
`rqa_stitchRows()`	Stitch rows
`sa2fd_dfa()`	Informed Dimension estimate from DFA slope (H)
`sa2fd_psd()`	Informed Dimension estimate from Spectral Slope (aplha)
`sa2fd_sda()`	Informed Dimension estimate from SDA slope.
`setTheiler()`	Set theiler window on a distance matrix or recurrence matrix.
`set_command_line_rp()`	Set command line RQA executable
`ts_center()`	Center a vector
`ts_changeindex()`	Find change indices
`ts_checkfix()`	Check and/or Fix a vector
`ts_coarsegrain()`	Course grain a time series
`ts_cp()`	Change Profile
`ts_detrend()`	Detrend a time series
`ts_diff()`	Derivative of time series
`ts_discrete()`	Discrete representation
`ts_duration()`	Time series to Duration series
`ts_embed()`	Delay embedding of a time series
`ts_integrate()`	Create a timeseries profile
`ts_levels()`	Detect levels in time series
`ts_peaks()`	Find Peaks or Wells
`ts_permtest_block()`	Permutation Test: Block Randomisation
`ts_permtest_transmat()`	Permutation Test: Transition Matrix
`ts_rasterize()`	Turn a 1D time series vector into a 2D curve
`ts_sd()`	Standard Deviation estimates
`ts_slice()`	Slice a Matrix
`ts_slope()`	Calculate Kendall's tau in sliding window or around change point.
`ts_slopes()`	Detect slopes in time series
`ts_standardise()`	Standardise a vector
`ts_sumorder()`	Adjust time series by summation order
`ts_symbolic()`	Symbolic representation
`ts_transmat()`	Transition matrix
`ts_trimfill()`	Trim or Fill Vectors
`ts_windower()`	Get sliding window indices
`var_win()`	Windowed variance

Reference

All functions