Package index • networkreporting

All functions

add.kp(): attach known populations to a dataframe
add_esc_ind_vis(): add individual visibility based on sib reports to ego X sib X cell reports
agenames(): make labels for age groups
apply_visibility_rule(): Apply a visibility rule to ego X alter X cell reports
calculate_sib_ind_visibility(): given a sib dataset, calculate individual visibility weight for each sib
cell_config(): Specify the cells (age groups, time period) to produce estimates for
compare_ties(): Put estimates from several ties side by side
cpp_compute_occ_exp(): cpp_compute_occ_exp
cpp_compute_occ_exp2(): cpp_compute_occ_exp
df.to.kpvec(): turn a dataframe into a known population vector
estimate.error(): given an estimated subpopn size or prevalence and the correct value, produce some measurements of how close the esimate is
estimated_total(): Read a population total off an estimate
example.knownpop.dat: Example known population data
example.survey: Example household survey data
get_agg_est_from_ec(): helper function for calculating aggregate visibility estimate from ego X cell data
get_boot_ests_matrix(): Fast bootstrap estimation using matrix multiplication
get_ec_reports(): get ego X cell reports
get_esc_reports(): Get ego X sibling X cell reports
get_ic_reports(): get a dataset with reports used for internal-consistency checks
get_ind_est_from_ec(): helper function for calculating individual visibility estimate from ego X cell data
get_sibship_info(): calculate number of sibs on frame for each respondent
get_visibility(): calculate visibility for each sibship and ego
gwsm.estimator(): indirect estimator (generalized weight share method / gwsm)
is_tie_config(): Is this a tie_config?
is_visibility_rule(): Is this object a visibility rule?
kp.degree.estimator(): kp.degree.estimator (DEPRECATED)
kp.estimator_() kp.estimator(): Average personal network size estimates using known population method
kp.individual.estimator() kp.individual.estimator_(): Individual personal network size estimates using the known population method
make.age.groups(): make an age.groups object
make.even.age.groups(): make an age.groups object with evenly-sized intervals
make.time.periods(): make a time.periods object
make_vis_refit(): Build a per-replicate refit function for an estimated visibility rule
make_vis_refit_esc(): Build a per-replicate refit that recomputes visibility at the report level
multiplicity.estimator(): multiplicity.estimator
network.survival.estimator_() network.survival.estimator(): network survival estimator
network_survival_estimator(): Estimate death rates from network reporting data
networkreporting networkreporting-package package-networkreporting: Network reporting estimators
nmx_to_nqx(): convert age-specific mortality rates to probabilities of dying
nsum.estimator(): nsum.estimator
nsum.internal.consistency(): nsum.internal.consistency
occ.exp(): tabulate occurrences and exposures
plot_meanties_truth(): plot_meanties_truth
pool_ties(): Average estimates from several ties into one
q15_to_50(): probability of dying between exact ages 15 and 50
rdsII.estimator(): rdsII.estimator
report.aggregator_() report.aggregator(): aggregate a reported quantity by groups
sib_ic_checks(): get calculate internal consistency checks for sibling reports
summation.estimator(): summation.estimator
tie_config() print(<tie_config>): Declare what kind of tie a set of reports is about
ties_union_check(): Can these ties be unioned?
topcode.data(): topcode a group of variables
topcode.var(): topcode a vector of numerical values
total.degree.estimator(): total.degree
true_visibility_from_network(): True visibility, read off a known reporting network
vis_aggregate(): Visibility from aggregate relational data
vis_coalesce(): Try visibility rules in priority order
vis_from_clique(): Visibility from a clique tie
vis_from_donor(): Visibility borrowed from a donor population
vis_from_group_size(): Visibility from a group size the caller supplies
vis_from_model(): Visibility predicted from a fitted model
vis_from_report(): Visibility as reported by the respondent
vis_is_cell_constant(): Can a rule's visibility be predicted from ego X cell data?
visibility_accuracy(): Score a visibility rule against a known truth
window_intersect(): window_intersect