|
simplex_project_mat()
|
Create linear map to move simplex with p vertices to p-1 dimensional space |
|
to_lower_simplex()
|
move df to simplex representation |
|
distance_depth_function()
|
Geenens & Nieto-Reyes functional distance-based depth |
|
as.matrix(<tidy_dist_mat>) as.array(<tidy_dist_mat>)
|
convert tidy_dist_mat to matrix |
|
check_dist_params()
|
checks the provided dist_params to make sure they are what is expected |
|
check_tidy_dist_mat_dimensions()
|
check_tidy_dist_mat_dimensions |
|
check_tidy_dist_names_distinct()
|
check_tidy_dist_names_distinct |
|
`colnames<-`(<tidy_dist_mat>)
|
assign colnames |
|
compare_new_to_rest_via_distance()
|
Title |
|
`dimnames<-`(<tidy_dist_mat>)
|
assign dimnames for tidy_dist_mat |
|
dimnames(<tidy_dist_mat>)
|
return dimnames for tidy_dist_mat |
|
dist_along_path_angle()
|
Distance/angle between points along path relative to eculidean distance (2d
path) |
|
dist_along_path_direction()
|
Distance/direction between points along path relative to eculidean distance |
|
dist_between_paths()
|
Distance between points |
|
dist_matrix_innersq_2d() dist_matrix_innersq_angle()
|
Calculates the distance matrix between a set of paths (Euclidean based). This
is actually d^2 (2d) |
|
dist_matrix_innersq_direction()
|
Calculates the distance matrix between a set of paths (Euclidean based). This
is actually d^2 |
|
distance_psuedo_density_function()
|
psuedo-density for objects with a distance between them |
|
distanglepath_df()
|
Distance/direction between points along path relative to eculidean distance |
|
distinct_time()
|
select distinct rows (relative to time) |
|
equa_dist_points_angle()
|
point compression (2d) |
|
equa_dist_points_direction()
|
point compression |
|
equa_dist_points_listable_angle()
|
List of compressions (2d) |
|
equa_dist_points_listable_direction()
|
List of compressions |
|
filament_distance_depth()
|
Calculate filament depth (relative to distance depth) |
|
format(<tidy_dist_mat>)
|
Format tidy_dist_mat for printing |
|
hausdorff_dist()
|
Calculates the Hausdorff distance between 2 sets |
|
head(<tidy_dist_mat>)
|
Return the Last Parts of a tidy_dist_mat (symmetric grab) |
|
is.tidy_dist_mat()
|
checks if object is a tidy_dist_mat |
|
l2filamentdist_df()
|
inner l2 distance between filaments |
|
local_distance_depth_function()
|
Localized version of Geenens & Nieto-Reyes functional distance-based depth |
|
print(<tidy_dist_mat>)
|
print tidy_dist_mat objects |
|
`rownames<-`(<tidy_dist_mat>)
|
assign rownames for tidy_dist_mat |
|
`[`(<tidy_dist_mat>)
|
Extract a part of a tidy_dist_mat object |
|
tail(<tidy_dist_mat>)
|
Return the Last Parts of a tidy_dist_mat (symmetric grab) |
|
tidy_dist_mat()
|
tidy_dist_mat objects |
|
which_index() which_not_index()
|
find the index of a tidy_dist_mat relative to a data.frame |
|
step_along_angle()
|
step along 2d path with angle |
|
step_along_direction()
|
step along path with direction |
|
steps_along_2d_line()
|
Create n equidistance points |
|
project_onto_simplex()
|
Project onto a simplex where observations in the unit simplex x |
|
project_to_simplex()
|
project onto a standard 3d simplex. |
|
EpiCompare
|
EpiCompare: package overview |
|
EpiModel_agg_bd
|
EpiModel_agg_bd Example output from the EpiModel package for a individual model with birth and death rates. |
|
EpiModel_det
|
EpiModel_det Example output from the EpiModel package for a deterministic model |
|
EpiModel_icm
|
EpiModel_icm Example output from the EpiModel package for a stochastic ICM |
|
GeomPredBand
|
GeomPredBand |
|
GeomSirAggregate
|
GeomSirAggregate |
|
SEIR_to_SIR_E()
|
Convert SEIR to XYZ coordinates fixed in a tetrahedron |
|
SEIR_to_XYZ()
|
Convert SEIR to XYZ coordinates fixed in a tetrahedron |
|
StatSirAggregate
|
StatSirAggregate |
|
StatPredBandConvexHull
|
stat object for use in convex hull based stat_prediction_band and
geom_prediction_band |
|
StatPredBandDeltaBall
|
stat object for use in delta_ball based stat_prediction_band and
geom_prediction_band |
|
StatPredBandKDE
|
stat object for use in kde based stat_prediction_band and
geom_prediction_band |
|
StatPredBandSpherical
|
stat object for use in spherical ball based stat_prediction_band and
geom_prediction_band |
|
agent_array_to_df()
|
Put agent_array into a data frame format |
|
agent_array_to_mat()
|
Put agent_array into a matrix format |
|
agents_sims
|
agents_sims Example output from SIR simulations |
|
agents_to_aggregate()
|
generalized method to convert raw agent based data to aggregate data |
|
agents_to_aggregate(<data.frame>)
|
generalized function to convert raw agent based data to aggregate data |
|
agents_to_aggregate(<grouped_df>)
|
generalized function to convert raw agent based data to aggregate data
for grouped_data (preforms per group) |
|
arrangeGrob() grid.arrange()
|
Arrange multiple grobs on a page (ggtern and ggplot2 compatible) |
|
as.matrix(<tidy_dist_mat>) as.array(<tidy_dist_mat>)
|
convert tidy_dist_mat to matrix |
|
cases_to_SIR()
|
Impute Recovered counts for the SIR model |
|
cases_to_SIR(<data.frame>)
|
Impute Recovered counts for the SIR model |
|
cases_to_SIR(<grouped_df>)
|
Impute Recovered counts for the SIR model |
|
check_character_percent()
|
check if a character is a desirable percentage value |
|
check_dist_params()
|
checks the provided dist_params to make sure they are what is expected |
|
check_inside_elipsoid()
|
assert if observation is inside elipsoid |
|
check_inside_elipsoid_func()
|
create a function assert if observations are inside elipsoid |
|
check_min_max_time()
|
min_max_time vector check |
|
check_ordered()
|
checks if states within data frame are ordered as inputted (<=) |
|
check_tidy_dist_mat_dimensions()
|
check_tidy_dist_mat_dimensions |
|
check_tidy_dist_names_distinct()
|
check_tidy_dist_names_distinct |
|
`colnames<-`(<tidy_dist_mat>)
|
assign colnames |
|
compare_new_to_rest_via_distance()
|
Title |
|
contained()
|
Method to check if a filament is completely contained in a set (relative to
discrete representation) |
|
convex_hull_compute_group_paths_to_points()
|
convex hull function for compute_group ggplot object (for top paths' points) |
|
create_convex_hull_structure()
|
create the convex hull associated with points |
|
create_delta_ball_structure()
|
create the delta ball associated with points |
|
delta_ball_compute_group_paths_to_points()
|
delta ball function for compute_group ggplot object (for top paths' points) |
|
delta_structure()
|
Performs delta ball approach (2d approach) |
|
`dimnames<-`(<tidy_dist_mat>)
|
assign dimnames for tidy_dist_mat |
|
dimnames(<tidy_dist_mat>)
|
return dimnames for tidy_dist_mat |
|
dist_along_path_angle()
|
Distance/angle between points along path relative to eculidean distance (2d
path) |
|
dist_along_path_direction()
|
Distance/direction between points along path relative to eculidean distance |
|
dist_between_paths()
|
Distance between points |
|
dist_matrix_innersq_2d() dist_matrix_innersq_angle()
|
Calculates the distance matrix between a set of paths (Euclidean based). This
is actually d^2 (2d) |
|
dist_matrix_innersq_direction()
|
Calculates the distance matrix between a set of paths (Euclidean based). This
is actually d^2 |
|
distance_depth_function()
|
Geenens & Nieto-Reyes functional distance-based depth |
|
distance_psuedo_density_function()
|
psuedo-density for objects with a distance between them |
|
distanglepath_df()
|
Distance/direction between points along path relative to eculidean distance |
|
distinct_time()
|
select distinct rows (relative to time) |
|
draws_to_states()
|
Draw the transfer matrix |
|
equa_dist_points_angle()
|
point compression (2d) |
|
equa_dist_points_direction()
|
point compression |
|
equa_dist_points_listable_angle()
|
List of compressions (2d) |
|
equa_dist_points_listable_direction()
|
List of compressions |
|
expanding_info()
|
expanding aggregate data to desirable format |
|
extract_contour()
|
Selection of specific contour level from KDE object |
|
extract_icm_cols()
|
Extract the icm class totals |
|
filament_compression()
|
Compress filaments to filaments with the same number of points (equally
linearly space compared to original filament definition) |
|
filament_distance_depth()
|
Calculate filament depth (relative to distance depth) |
|
fit_kde_object()
|
Fit kernel density estimator (KDE) object |
|
format(<tidy_dist_mat>)
|
Format tidy_dist_mat for printing |
|
fortify_aggregate()
|
Take external aggregate data and put it in a format used in this package |
|
fortify_aggregate(<data.frame>)
|
Takes in data from the R pomp package where the output is a data frame and
puts it in SIR format for EpiCompare |
|
fortify_aggregate(<dcm>)
|
Take external aggregate data and put it in a format used in this package |
|
fortify_aggregate(<epimodel_inner>)
|
Take EpiModel Data and format it |
|
fortify_aggregate(<icm>)
|
Take external aggregate data and put it in a format used in this package |
|
fortify_aggregate(<list>)
|
Takes in data from the R pomp package where the output is a data frame and
puts it in SIR format for EpiCompare |
|
fortify_aggregate(<netsim>)
|
Take external aggregate data and put it in a format used in this package |
|
fortify_aggregate(<pomp>)
|
Takes in data from the R pomp package where the output is a data frame and
puts it in SIR format for EpiCompare |
|
fortify_aggregate(<pompList>)
|
Takes in data from the R pomp package where the output is a data frame and
puts it in SIR format for EpiCompare |
|
fortify_aggregate(<pomp_df>)
|
Takes in data from the R pomp package where the output is a data frame and
puts it in SIR format for EpiCompare |
|
fortify_aggregate(<pomp_df_inner>)
|
Takes in data from the R pomp package where the output is a data frame and
puts it in SIR format for EpiCompare |
|
fortify_aggregate(<pomp_list>)
|
Takes in data from the R pomp package where the output is a data frame and
puts it in SIR format for EpiCompare. |
|
stat_prediction_band() geom_prediction_band()
|
The prediction_band geom/stat |
|
get_closest()
|
create a grid of points indicating near border or not (and inside or outside) |
|
get_closest_nn()
|
create a grid of points indicating near border or not (and inside or outside)
using RANN::nn2. |
|
get_delta()
|
Find delta for covering |
|
get_delta_nn()
|
calculate maxmin distance between points |
|
get_epimodel_icm_states()
|
Get the viable names for the states from an icm object |
|
get_grid_elipsoid_containment()
|
create a grid of points indicating whether in a set of 2d elipsoids |
|
get_lines()
|
Get edges within union of balls |
|
get_previous_counts()
|
Count number in each state |
|
get_state_names()
|
Get the state names fro the transition matrix |
|
get_tri_matrix()
|
Make triangle matrix |
|
get_xy_coord()
|
get xy ternary coordinates from xyz based data frame |
|
grab_top_depth_filaments()
|
Select a proportion of the top / most deep filaments |
|
hagelloch_agents
|
Measles in Hagelloch, Germany, 1861 (agent format) |
|
hagelloch_raw hagelloch_raw2 hagelloch_aug_births
|
Measles in Hagelloch, Germany, 1861 |
|
hagelloch_sir
|
Measles in Hagelloch, Germany, 1861 (SIR format) |
|
hausdorff_dist()
|
Calculates the Hausdorff distance between 2 sets |
|
head(<tidy_dist_mat>)
|
Return the Last Parts of a tidy_dist_mat (symmetric grab) |
|
imports_hidden_from()
|
import hidden function / variable from other package |
|
initialize_agent_array()
|
Initialize array for agent simulations |
|
inner_compute_group_paths_to_points()
|
inner function to compute points of top paths |
|
inner_delta_ball_wrapper()
|
Run delta ball analysis |
|
is.tidy_dist_mat()
|
checks if object is a tidy_dist_mat |
|
is.wholenumber()
|
Check for whole number values |
|
kde_from_list()
|
Contour of data points using Kernel Density Estimate. |
|
l2filamentdist_df()
|
inner l2 distance between filaments |
|
local_distance_depth_function()
|
Localized version of Geenens & Nieto-Reyes functional distance-based depth |
|
multinomial_updater()
|
Multinomial draws to update states depending on past count |
|
not() is.not_df()
|
characterizes that we will subset by the indices *not* in said data.frame |
|
pomp_arr
|
Example SIR simulation output of class 'array' from the pomp package |
|
pomp_df
|
Example SIR simulation output of class ' data.frame' from the pomp package |
|
pomp_pomp
|
Example SIR simulation output of class ' pomp' from the pomp package |
|
pomp_sir
|
pomp_sir Example output from the pomp package. |
|
print(<ggplot>)
|
Explicitly draw plot (ggtern and ggplot2 compatible) |
|
print(<tidy_dist_mat>)
|
print tidy_dist_mat objects |
|
process_df_index()
|
internal function to get the indices of the tidy_dist_mat related to a data frame. |
|
project_onto_simplex()
|
Project onto a simplex where observations in the unit simplex x |
|
project_to_simplex()
|
project onto a standard 3d simplex. |
|
quantile_curves_to_points()
|
Pull top (1-alpha)% quantile percent of curves points. |
|
r_new_interface()
|
logic to check if R is >= 4 |
|
remove_delta_off_line()
|
Shorten line by delta on both sides |
|
remove_incomplete_tri()
|
Remove triangles with edges that need removal |
|
reverse_not_df()
|
removes the not() operation (that makes a df a not_df) |
|
`rownames<-`(<tidy_dist_mat>)
|
assign rownames for tidy_dist_mat |
|
sim_arr_to_df()
|
Convert simulation output from simulate_SIR_agents to data frame format |
|
simplex_project_mat()
|
Create linear map to move simplex with p vertices to p-1 dimensional space |
|
simulate_SIR_agents()
|
Simulate SIR data according to a chain Binomial |
|
simulate_agents()
|
Simulate an agent-based model for given state-to-state transfers |
|
sirs_data
|
Example of SIRS model generated from the pomp package. |
|
sis_data
|
Example SIS model of class `dcm` from the EpiModel package. |
|
sis_data_f
|
Example SIS model converted to aggregate format |
|
geom_aggregate() stat_aggregate()
|
aggregate SIR path visuals from agent data |
|
state_change_inds()
|
Determine which agents changed states |
|
step_along_angle()
|
step along 2d path with angle |
|
step_along_direction()
|
step along path with direction |
|
steps_along_2d_line()
|
Create n equidistance points |
|
`[`(<tidy_dist_mat>)
|
Extract a part of a tidy_dist_mat object |
|
tail(<tidy_dist_mat>)
|
Return the Last Parts of a tidy_dist_mat (symmetric grab) |
|
theme_sir()
|
Add a portable theme for SIR ternary plots |
|
tidy_dist_mat()
|
tidy_dist_mat objects |
|
to_lower_simplex()
|
move df to simplex representation |
|
top_curves_to_points()
|
Pull the points from the top (1-alpha)% percent of curves. |
|
top_curves_to_points(<grouped_df>)
|
Pull the points from the top (1-alpha)% percent of curves (LIST) |
|
top_curves_to_points(<list>)
|
Pull the points from the top (1-alpha)% percent of curves (LIST) |
|
trans_mat_to_D_fxn()
|
Take a character formula transition matrix and turn it into a function |
|
update_agents()
|
Update agents based on Bernoulli draws |
|
update_approved_layers()
|
Updates approved layers of ggtern |
|
which_index() which_not_index()
|
find the index of a tidy_dist_mat relative to a data.frame |