Package index

Theming

Functions for tailoring graphs with themes, scales, and palettes.

stocnet_theme() set_stocnet_theme()

Setting a consistent theme for all plots

stocnet_medium() set_stocnet_medium() ag_size()

Setting the medium a plot is made for

ag_base() ag_ink() ag_missing() ag_highlight() ag_positive() ag_negative() ag_qualitative() ag_sequential() ag_divergent() ag_font()

Consistent palette calls

list_fonts()

Listing the fonts available to R

simulate_colorblind()

Simulating how colours look to a colour-blind viewer

match_color() is_dark()

Matching colors across palettes

check_separation() check_contrast()

Checking colours for colour blindness and legibility

scale_x_mdate() scale_y_mdate()

Scales for messy dates

Graphing

Functions for graphing networks. graphr() graphs any manynet-compatible class object automagically. graphs() and grapht() do the same for multiple networks and dynamic networks, respectively.

graphr()

Easily graph networks with sensible defaults

graphs()

Easily graph a set of networks with sensible defaults

grapht() print(<grapht>)

Easily animate dynamic networks with sensible defaults

stocnet_completion() set_completion()

Completing argument values as you type

Plotting

plot() methods exist for results and other objects for stocnet packages.

plot(<node_measure>) plot(<tie_measure>) plot(<network_measures>)

Plotting logical marks Plotting numeric measures

plot(<node_member>) plot(<matrix>)

Plotting categorical memberships

plot(<node_motif>) plot(<network_motif>)

Plotting tabular motifs

plot(<netlm>) plot(<netlogit>)

Plotting methods for MRQAP models

plot(<diff_model>) plot(<diffs_model>) plot(<learn_model>)

Plotting diffusion models

plot(<network_test>)

Plotting methods for CUG and QAP tests

plot(<diagnose_outliers>) plot(<outliers.goldfish>) plot(<diagnose_changepoints>) plot(<changepoints.goldfish>) plot(<goldfishOutliers>) plot(<goldfishChangepoints>) plot(<goldfishMargins>) plot(<goldfishGOF>) plot(<goldfishTimeTest>) plot(<goldfishOnset>)

Plotting adequacy diagnostics

plot(<ag_conv>) plot(<traces.monan>) plot(<ergm>) load_ergm_res()

Plotting convergence diagnostics

plot(<ag_gof>) plot(<gof.stats.monan>) plot(<sienaGOF>) plot(<gof.ergm>)

Plotting goodness-of-fit results

plot(<result.goldfish>) plot(<goldfishFit>)

Plotting a goldfish model fit at a glance

graphr()

Easily graph networks with sensible defaults

graphs()

Easily graph a set of networks with sensible defaults

grapht() print(<grapht>)

Easily animate dynamic networks with sensible defaults

plot(<selectionTable>) plot(<influenceTable>)

Plotting effects interpretation

count_pages()

How many pages a paged diagnostic figure has

Layouts

Functions for laying out the nodes in a graph. Included here are some improved or additional layouts to those offered in igraph and ggraph by default.

layout_concentric() layout_tbl_graph_concentric()

Concentric layout

layout_configuration() layout_tbl_graph_configuration() layout_dyad() layout_triad() layout_tetrad() layout_pentad() layout_hexad()

Layout algorithms based on configurational positions

layout_correspondence() layout_tbl_graph_correspondence()

Correspondence layout

layout_layered() layout_tbl_graph_layered() layout_lineage() layout_tbl_graph_lineage() layout_railway() layout_tbl_graph_railway() layout_ladder() layout_tbl_graph_ladder()

Layered layouts

layout_levels() layout_tbl_graph_levels()

Levels layout

layout_matching() layout_tbl_graph_matching()

Matching layout

layout_scaling() layout_tbl_graph_scaling()

Scaling layout

layout_valence() layout_tbl_graph_valence()

Valence layout

check_span() check_offset() check_stress() check_crossings() check_slopes() check_lengths() check_angles() check_drawing()

Checking how well a layout draws a network

Results objects from other packages

res_migraph_reg res_migraph_test res_migraph_diff res_manynet_diff siena_gof siena_influence siena_selection monan_conv monan_gof ergm_gof goldfish_outliers goldfish_changepoints goldfish_margins goldfish_gof goldfish_time goldfish_onset goldfish_fit

Precooked results for demonstrating plotting