Package index • 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_maxPhases(): Estimate the maximum number of Phases

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

irn_crossClustering(): Cross CLustering Coefficient

irn_crossDegree(): Cross Degree

irn_crossTriples(): Cross Triples

irn_plot(): Inter system recurrence networks

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_nodeDegree(): Matrix node degree

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_phaseProjection(): Plot Phase Space Projection

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() experimental: 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