Package index • distantia

Naming Conventions

The package follows these naming conventions:

distantia() and distantia_...(): functions to assess time series dissimilarity via dynamic time warping or lock-step methods.
psi_...(): step-by-step demonstration of how psi dissimilarity scores are computed.
tsl_...(): functions to generate, process, and explore time series lists (TSLs).
f_...(): transformation functions used as input arguments for other functions.
zoo_...(): functions to manage zoo time series.
utils_...(): internal functions and helpers.
..._cpp(): C++ core functions built via Rcpp for efficient dissimilarity analysis.

Example Time Series

Time series datasets from different disciplines.

albatross: Flight Path Time Series of Albatrosses in The Pacific

cities_coordinates: Coordinates of 100 Major Cities

cities_temperature: Long Term Monthly Temperature in 20 Major Cities

covid_coordinates: County Coordinates of the Covid Prevalence Dataset

covid_prevalence: Time Series of Covid Prevalence in California Counties

eemian_coordinates: Site Coordinates of Nine Interglacial Sites in Central Europe

eemian_pollen: Pollen Counts of Nine Interglacial Sites in Central Europe

fagus_coordinates: Site Coordinates of Fagus sylvatica Stands

fagus_dynamics: Time Series Data from Three Fagus sylvatica Stands

Time Series Simulation

Functions to generate simulated time series.

tsl_simulate(): Simulate a Time Series List

zoo_simulate(): Simulate a Zoo Time Series

Core Functions for Time Series Dissimilarity Analysis

Functions to compare time series and assess the partial contribution of individual variables to dissimilarity.

distantia(): Dissimilarity Analysis of Time Series Lists

distantia_importance(): Contribution of Individual Variables to Dissimilarity in Time Series Lists

Support Functions for Dissimilarity Analysis

These functions use the output of distantia() or distantia_importance() to enhance dissimilarity analysis by adding plotting, clustering, and mapping capabilities.

distantia_aggregate(): Aggregate Dissimilarity Analysis Data Frames Across Parameter Combinations

distantia_boxplot(): Boxplot of Dissimilarity Analysis Data Frames

distantia_cluster_hclust(): Hierarchical Clustering of Dissimilarity Analysis Data Frames

distantia_cluster_kmeans(): K-Means Clustering of Dissimilarity Analysis Data Frames

distantia_matrix(): Convert Dissimilarity Analysis Data Frame to Distance Matrix

distantia_plot(): Two-Way Dissimilarity Plots of Time Series Lists

distantia_to_sf(): Convert Dissimilarity Analysis Data Frames to Spatial Dissimilarity Networks

utils_block_size(): Default Block Size for Restricted Permutation in Dissimilarity Analyses

utils_cluster_hclust_optimizer(): Optimize the Silhouette Width of Hierarchical Clustering Solutions

utils_cluster_kmeans_optimizer(): Optimize the Silhouette Width of K-Means Clustering Solutions

utils_cluster_silhouette(): Compute Silhouette Width of a Clustering Solution

utils_importance_df_to_wide(): Data Frame with Contribution of Individual Variables to Dissimilarity to Wide Format

C++ Backend for Dissimilarity Analysis

These functions, designed to boost the computational efficiency of the package, implement all dissimilarity methods available in distantia() and distantia_importance().

null_psi_dynamic_time_warping_cpp(): (C++) Null Distribution of Dissimilarity Scores of Two Time Series

null_psi_lock_step_cpp(): (C++) Null Distribution of the Dissimilarity Scores of Two Aligned Time Series

psi_dynamic_time_warping_cpp(): (C++) Psi Dissimilarity Score of Two Time-Series

psi_equation_cpp(): (C++) Equation of the Psi Dissimilarity Score

psi_lock_step_cpp(): (C++) Psi Dissimilarity Score of Two Aligned Time Series

importance_dynamic_time_warping_legacy_cpp(): (C++) Contribution of Individual Variables to the Dissimilarity Between Two Time Series (Legacy Version)

importance_dynamic_time_warping_robust_cpp(): (C++) Contribution of Individual Variables to the Dissimilarity Between Two Time Series (Robust Version)

importance_lock_step_cpp(): (C++) Contribution of Individual Variables to the Dissimilarity Between Two Aligned Time Series

permute_free_by_row_cpp(): (C++) Unrestricted Permutation of Complete Rows

permute_free_cpp(): (C++) Unrestricted Permutation of Cases

permute_restricted_by_row_cpp(): (C++) Restricted Permutation of Complete Rows Within Blocks

permute_restricted_cpp(): (C++) Restricted Permutation of Cases Within Blocks

cost_matrix_diagonal_cpp(): (C++) Compute Orthogonal and Diagonal Least Cost Matrix from a Distance Matrix

cost_matrix_diagonal_weighted_cpp(): (C++) Compute Orthogonal and Weighted Diagonal Least Cost Matrix from a Distance Matrix

cost_matrix_orthogonal_cpp(): (C++) Compute Orthogonal Least Cost Matrix from a Distance Matrix

distance_lock_step_cpp(): (C++) Sum of Pairwise Distances Between Cases in Two Aligned Time Series

distance_matrix_cpp(): (C++) Distance Matrix of Two Time Series

auto_distance_cpp(): (C++) Sum Distances Between Consecutive Samples in a Time Series

auto_sum_cpp(): (C++) Sum Distances Between Consecutive Samples in Two Time Series

auto_sum_full_cpp(): (C++) Sum Distances Between All Consecutive Samples in Two Time Series

auto_sum_path_cpp(): (C++) Sum Distances Between All Consecutive Samples in the Least Cost Path Between Two Time Series

subset_matrix_by_rows_cpp(): (C++) Subset Matrix by Rows

cost_path_cpp(): Find Least Cost Path within a Least Cost Matrix

cost_path_diagonal_cpp(): (C++) Find Orthogonal And Diagonal Least Cost Path within a Least Cost Matrix

cost_path_orthogonal_cpp(): (C++) Find Orthogonal Least Cost Path within a Least Cost Matrix

cost_path_slotting_cpp(): (C++) Least Cost Path for Sequence Slotting

cost_path_sum_cpp(): (C++) Sum Distances in a Least Cost Path

cost_path_trim_cpp(): (C++) Remove Blocks from a Least Cost Path

distance_canberra_cpp(): (C++) Canberra Distance Between Two Binary Vectors

distance_chebyshev_cpp(): (C++) Chebyshev Distance Between Two Vectors

distance_chi_cpp(): (C++) Normalized Chi Distance Between Two Vectors

distance_cosine_cpp(): (C++) Cosine Dissimilarity Between Two Vectors

distance_euclidean_cpp(): (C++) Euclidean Distance Between Two Vectors

distance_hamming_cpp(): (C++) Hamming Distance Between Two Binary Vectors

distance_hellinger_cpp(): (C++) Hellinger Distance Between Two Vectors

distance_jaccard_cpp(): (C++) Jaccard Distance Between Two Binary Vectors

distance_manhattan_cpp(): (C++) Manhattan Distance Between Two Vectors

distance_russelrao_cpp(): (C++) Russell-Rao Distance Between Two Binary Vectors

Psi Demo Functions

Step-by-step demonstration of Psi dissimilarity scores. These functions are R wrappers of several key C++ functions above.

distance(): Distance Between Numeric Vectors

distances: Distance Methods

psi_auto_distance(): Cumulative Sum of Distances Between Consecutive Cases in a Time Series

psi_auto_sum(): Auto Sum

psi_cost_matrix(): Cost Matrix

psi_cost_path(): Least Cost Path

psi_cost_path_ignore_blocks(): Trims Blocks from a Least Cost Path

psi_cost_path_sum(): Sum of Distances in Least Cost Path

psi_distance_lock_step(): Lock-Step Distance

psi_distance_matrix(): Distance Matrix

psi_equation(): Normalized Dissimilarity Score

Create Time Series Lists

Transform raw time series data into time series lists (TSLs hereafter). TSLs are lists of zoo time series objects used within the package for data management and analysis.

tsl_initialize() tsl_init(): Transform Raw Time Series Data to Time Series List

Manage Time Series Lists

Functions manage, diagnose, repair TSLs.

tsl_colnames_clean(): Clean Column Names in Time Series Lists

tsl_colnames_get(): Get Column Names from a Time Series Lists

tsl_colnames_set(): Set Column Names in Time Series Lists

tsl_count_NA(): Count NA Cases in Time Series Lists

tsl_diagnose(): Diagnose Issues in Time Series Lists

tsl_handle_NA() tsl_Inf_to_NA() tsl_NaN_to_NA(): Handle NA Cases in Time Series Lists

tsl_names_clean(): Clean Time Series Names in a Time Series List

tsl_names_get(): Get Time Series Names from a Time Series Lists

tsl_names_set(): Set Time Series Names in a Time Series List

tsl_names_test(): Tests Naming Issues in Time Series Lists

tsl_ncol(): Get Number of Columns in Time Series Lists

tsl_nrow(): Get Number of Rows in Time Series Lists

tsl_repair(): Repair Issues in Time Series Lists

tsl_split(): Splits Multivariate Time Series Lists to Univariate TSLs

tsl_subset(): Subset Time Series Lists by Time Series Names, Time, and/or Column Names

tsl_time() tsl_time_summary(): Time Features of Time Series Lists

tsl_time_class_set(): Coerce Elements of Time Series List to same Time Class

tsl_to_df(): Transform Time Series List to Data Frame

Plot Time Series Lists

Main function to plot TSLs and its helpers.

tsl_plot(): Plot Time Series List

Processing Time Series Lists

Functions to transform, rescale, and aggregate TSLs.

tsl_aggregate(): Aggregate Cases in Time Series Lists

tsl_resample(): Resample Time Series Lists to a New Time

tsl_stats(): Summary Statistics of Time Series Lists

tsl_transform(): Transform Values in Time Series Lists

f_center(): Data Transformation: Data Centering by Column

f_detrend_difference(): Data Transformation: Differencing Detrending of Zoo Time Series

f_detrend_linear(): Data Transformation: Linear Detrending of Zoo Time Series

f_hellinger(): Data Transformation: Hellinger Transformation by Rows

f_list(): Lists Available Transformation Functions

f_pca(): Data Transformation: Principal Components of Zoo Time Series

f_percentage(): Data Transformation: Convert Values to Percentages by Row

f_proportion(): Data Transformation: Convert Values to Proportions by Row

f_rescale(): Data Transformation: Rescaling Values of Zoo Time Series to a New Range

f_scale(): Data Transformation: Data Centering and Scaling by Column

f_slope(): Data Transformation: Slope of Linear Regression with Time

f_smooth_window(): Data Transformation: Moving Window Smoothing of Zoo Time Series

f_trend_linear(): Data Transformation: Linear Trend of Zoo Time Series

utils_drop_geometry(): Removes Geometry Column from SF Data Frames

utils_global_scaling_params(): Global Centering and Scaling Parameters of Time Series Lists

utils_optimize_loess(): Optimize Loess Models for Time Series Resampling

utils_optimize_spline(): Optimize Spline Models for Time Series Resampling

utils_rescale_vector(): Rescale Numeric Vector to a New Data Range

Zoo Functions

Functions to Manage, Plot, and Transform Zoo Time Series

zoo_aggregate(): Aggregate Cases in Zoo Time Series

zoo_name_clean(): Clean Name of a Zoo Time Series

zoo_name_get(): Get Name of a Zoo Time Series

zoo_name_set(): Set Name of a Zoo Time Series

zoo_permute(): Random or Restricted Permutation of Zoo Time Series

zoo_plot(): Plot Zoo Time Series

zoo_resample(): Resample Zoo Objects to a New Time

zoo_time(): Get Time Features from Zoo Objects

zoo_to_tsl(): Convert Individual Zoo Objects to Time Series List

zoo_vector_to_matrix(): Coerce Coredata of Univariate Zoo Time Series to Matrix

Plotting Helpers

Internal functions to help with plotting tasks.

utils_color_breaks(): Auto Breaks for Matrix Plotting Functions

utils_color_continuous_default(): Default Continuous Color Palette

utils_color_discrete_default(): Default Discrete Color Palettes

utils_line_color(): Handles Line Colors for Sequence Plots

utils_line_guide(): Guide for Time Series Plots

utils_matrix_guide(): Color Guide for Matrix Plot

utils_matrix_plot(): Plot Distance or Cost Matrix and Least Cost Path

Time Handling Helpers

Internal functions to handle time.

utils_as_time(): Ensures Correct Class for Time Arguments

utils_coerce_time_class(): Coerces Vector to a Given Time Class

utils_is_time(): Title

utils_new_time() utils_new_time_type(): New Time for Time Series Aggregation

utils_time_keywords(): Valid Aggregation Keywords

utils_time_keywords_dictionary(): Dictionary of Time Keywords

utils_time_keywords_translate(): Translates The User's Time Keywords Into Valid Ones

utils_time_units(): Data Frame with Supported Time Units

Other Internal Functions

Internal functions to check arguments and other miscellany tasks.

utils_check_args_distantia(): Check Input Arguments to distantia()

utils_check_args_matrix(): Checks Input Matrix

utils_check_args_path(): Checks Least Cost Path

utils_check_args_tsl(): Structural Check for Time Series Lists

utils_check_args_zoo(): Checks Argument x

utils_check_distance_args(): Check Distance Argument

utils_check_list_class(): Checks Classes of List Elements Against Expectation

utils_clean_names(): Clean Character Vector of Names

utils_digits(): Number of Decimal Places

utils_distantia_df_split(): Split Dissimilarity Analysis Data Frames by Combinations of Arguments

utils_distantia_df_to_matrix(): Data Frame to Matrix

utils_prepare_df(): Convert Data Frame to a List of Data Frames

utils_prepare_matrix(): Convert Matrix to Data Frame

utils_prepare_matrix_list(): Convert List of Matrices to List of Data Frames

utils_prepare_time(): Handles Time Column in a List of Data Frames

utils_prepare_vector_list(): Convert List of Vectors to List of Data Frames

utils_prepare_zoo_list(): Convert List of Data Frames to List of Zoo Objects

utils_tsl_pairs(): Data Frame with Pairs of Time Series in Time Series Lists