socmod/_pkgdown.yml at main · mt-digital/socmod · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
url: https://SocSci-for-Sustainability.github.io/socmod/
template:
  bootstrap: 5
reference:
- title: The `Trial` class and run helpers
  desc: |
    The `run_trial()` and `run_trials()` functions use the specified `partner_selection`, `interaction`,  and `iterate_model` functions to specify these dynamics and iterate the `model` (an instance of `socmod::AgentBasedModel`). `fixated` helps us tell when all agents are performing the same behavior, i.e., the population has fixated.
  contents:
  - starts_with("run_trial")
  - Trial
  - fixated
  - observe_behavior
- title:
  desc: |
    Summarise a collection of trial outcomes or prevalence dynamics over model parameters
  contents:
  - summarise_prevalence
  - summarise_outcomes
- title: Visualizations
  desc: Tools to plot networks, outcomes, and time series
  contents:
  - plot_prevalence
  - plot_network_adoption
  - SOCMOD_PALETTE
  - SOCMOD_PALETTE_CVD
- title: Examples with real-world data
  desc: Simple examples of real-world data in socmod.
  contents:
  - get_feld_1991_network
- title: AgentBasedModel and ModelParameters
  desc: |
    Create agent-based models and specify model configuration parameters
  contents:
  - make_abm
  - initialize_agents
  - make_model_parameters
  - AgentBasedModel
  - ModelParameters
  - DEFAULT_PARAMETERS
- title: Model dynamics class, make_model_dynamics, and other tools
  desc: |
    Functions to pass to run for model iteration in a learning model,  and for either success-biased or frequency-biased adaptive learning.
  contents:
  - iterate_learning_model
  - ModelDynamics
  - starts_with("success_bias")
  - starts_with("frequency_bias")
  - starts_with("contagion_")
  - ends_with("_learning_model")
  - learning_model_step
  - dummy_model_dynamics
  - make_model_dynamics
- title: Agents and their neighbors
  desc: |
    Agents in the model are represented by a number of attributes, with select class methods to track lists of their neighbors.
  contents:
  - Agent
  - Neighbors
- title: Networks
  desc: |
    Create networks, including a growing suite of network construction routines.
  contents:
  - make_homophily_network
  - make_small_world
  - make_preferential_attachment
  - G_NM
  - make_regular_lattice
  - add_unique_edge
  - get_all_possible_edges
  - not_adjacent
  - compare_friendship_paradox
  - load_igraph_from_csv