Package index
Wind data
Download hourly wind, and represent it as a time series (wind_series) or a single snapshot (wind_field).
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download_wind_data() - Download hourly wind data from NCAR
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download_land_mask() - Download a land-water layer from NCAR
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wind_series() - Create a wind_series
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wind_times() - Get the time of each step in a wind_series
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subset_series() - Select time steps from a wind_series
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wind_field() - Create a wind_field
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mean(<wind_series>) - Mean wind of a wind_series
Wind roses
Summarize a long time series of wind conditions as the conductance from each cell to its eight neighbors—the basis of both connectivity models.
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wind_rose() - Build or load a wind rose
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download_wind_rose() - Download a pre-built wind rose
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wind_rose_catalog() - Catalog of pre-built wind roses
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wind_rose_cache() - Manage the wind rose download cache
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combine_roses() - Combine wind roses built from different time periods
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weight_conductance() - Weight a wind rose's conductances
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net_flow() - Net flow of a wind rose, as a wind field
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aggregate(<wind_rose>)disagg(<wind_rose>)resample(<wind_rose>,<ANY>)project(<wind_rose>) - Changing the grid of a wind rose
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least_cost() - Least-cost travel time surface
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least_cost_paths() - Least-cost paths
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pairwise_least_cost() - Pairwise least-cost travel times among sites
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wind_graph() - Build a wind connectivity graph
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random_walk() - Simulate wind dispersal by random walk
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random_walk_paths() - Paths of material through a random walk
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pairwise_random_walk() - Pairwise random walk connectivity among sites
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rw_exit_prob() - Probability of exiting a random walk's domain
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rw_self_retention() - Self-contribution of each source cell in a stream-mode random walk
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rw_max_step() - Maximum iteration duration for a random walk
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iter_length() - Iteration step length of a random walk
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check_cell_distance() - Check how grid cells distort distances among sites
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downscale() - Downscale a wind rose to higher spatial resolution
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cell_distance() - Pairwise distances between cell centroids
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ws_summarize() - Summary statistics for a windshed
Hypothesis testing
Test hypotheses about wind connectivity using pairwise matrices of wind, gene flow, or other relationships among sites.
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mantel_test() - Mantel test
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pairwise_ratios() - Convert asymmetric pairwise matrix to reciprocally symmetrical matrix
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pairwise_means() - Convert asymmetric pairwise matrix to symmetrical pairwise means
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point_distance() - Pairwise distances between points
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geom_wind_rose()stat_wind_rose() - Wind rose glyphs: a map of local wind roses
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geom_wind_arrow()stat_wind_arrow() - Wind field arrows
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geom_wind_trail()stat_wind_trail() - Wind trails and streamlines
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geom_wind_path() - Draw existing wind trails or paths
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scale_fill_bearing()scale_colour_bearing()scale_color_bearing() - Color scales for wind direction
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fortify(<wind_rose>)fortify(<wind_field>)fortify(<wind_series>)fortify(<random_walk>) - Convert windscape objects to data frames for ggplot2
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wind_trails() - Trace particle trails through a wind field
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generate_particles() - Generate initial particle locations
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windscape_example() - Example wind data sets
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birch - Silver birch landscape genetic data from Tsuda et al. (2017)
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wind_series-class - An S4 object class representing a wind field time series
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wind_field-class - An S4 object class representing a wind field
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wind_rose-class - An S4 object class representing a wind rose
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wind_graph-class - An S4 object class representing a wind graph
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`[[`(<wind_series>,<ANY>,<ANY>)`[[`(<wind_field>,<ANY>,<ANY>)`[[`(<wind_rose>,<ANY>,<ANY>)subset(<wind_series>)subset(<wind_field>)subset(<wind_rose>)c(<wind_series>)c(<wind_field>)c(<wind_rose>) - Selecting and combining layers of windscape objects
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geoCorrection(<wind_graph>,<ANY>) - geoCorrection is not applicable to wind graphs