Package index • hts_weighting

All functions

add_geometry_to_table(): Add Geometry to Zone Groups Table

adjust_pums_to_reference(): Adjust PUMS weights to match ACS

adjust_ref_counts_dataset(): Adjust ACS counts to match county totals

adjust_reference_to_target(): Adjust reference counts to match targets

adjust_target_to_study_zones(): Adjust targets to client zones

adjust_unrelated_per_wts(): Adjust person weights for unrelated persons

adjust_unrelated_pums(): Split unrelated householders in PUMS

adjust_unrelated_survey(): Adjust survey data to account for unrelated householders

aggregate_targets(): Aggregate targets to higher-level geography

append_var_lab(): Append value labels to variable

archive_file(): Archive file with timestamp if exists

blend_initial_weights(): Blend initial weights for ABS and NPS samples

build_update_map(): : Build Update Map for Combined Target Categories

calc_alpha(): Calculate blending factor for NPS sample

calc_complete_hhdays(): Calculate complete household weekdays

calc_day_trip_class(): Classify day trip type

calc_day_weights(): Calculate day weights for each person-day

calc_daypat_probabilities(): Save day pattern summary results

calc_initial_weights(): Calculate initial survey weights

calc_linked_trip_weights(): Calculate linked trip weights

calc_max_error(): Calculate maximum error across weighted variables

calc_person_type(): Calculate person type classification

calc_person_weights(): Calculate person weights from household weights

calc_sample_plan_counts(): Allocate ACS counts to sample plan geographies

calc_survey_ci(): Calculate survey confidence intervals by zone group and region

calc_target_ci(): Calculate target confidence intervals by zone group and region

calc_tour_weights(): Calculate tour weights for each person

calc_trip_rate_factor(): Calculate trip rate factor from model

calc_trip_weights(): Calculate trip weights for each person-trip

calc_uwe(): Calculate unequal weighting effect (UWE)

calc_weight_fit(): Calculate fit between target and survey confidence intervals

calc_weight_mape(): calc_target_fit

calculate_acs_proportions(): Calculate ACS proportions from counts

calculate_aggegrate_trip_rates(): Aggregate trip rates by platform and type

cap_trip_rate_factors(): Cap trip rate factors

check_all_settings(): Validate all YAML configs under a directory

check_daypat_results(): Check day pattern model results against original data

check_group_sum(): Check group sum consistency for target columns

check_group_sums(): Check group sums for target columns

check_initial_weights(): Check initial weights against PUMS reference counts

check_ref_counts(): Check reference counts against PUMS data

check_settings(): Validate a single YAML config against the JSON schema

check_settings_doc_coverage(): CI/PR gate: ensure settings are documented in the schema

check_weight_skew(): Check skew between household and person weights in PUMS

check_write_to_db(): Confirm database write intent

clean_data_dict(): Clean PUMS data dictionary

clip.vector(): Clip vector to bounds

cluster_pumas(): Cluster PUMAs

connect_to_pops(): Connect to the POPS server using Rpostgres

create_full_xwalk(): Create PUMA-to-zone group crosswalk

create_ie_adjustment_data(): Create adjustment factors to match expansion data to target year

create_importance_list(): create_importance_list

create_target_update_table(): Create target update mapping table for control file

cut_and_label(): Cut variable into labeled intervals

db_table_exists(): Check if table exists on DB

db_table_has_geometry(): Check if database table has geometry column

diff_settings_vs_schema(): Compare used settings against documented schema properties

discover_settings_in_code(): Discover settings keys used in the codebase

entropy_zone_groups(): Group PUMAs by sample rate using entropy-based clustering

extract_trip_rate_factors(): Summarize trip rate factors by platform and day group

fetch_acs(): Fetch ACS table with caching

fetch_all_hts_tables(): Load all HTS tables from POPS or disk

fetch_from_db(): Fetch table from database with geometry support

fetch_hts_table(): Load HTS table from POPS, cache, or CSV

fetch_pums(): Fetch PUMS data with caching

fetch_study_region(): Fetch study region geometry from POPS or cache

fillna(): Fill NA values in data.table

filter_settings(): Exclude loader-generated and deprecated settings from coverage checks

find_income_overlaps(): Match income bins by overlap

find_level_idx(): Find index for compound range in target string

find_ncols(): find_ncols

find_project_root(): Locate SharePoint project root directory

fix_value_labels_on_load(): Fix value labels on load

force_balance_pums_weights(): Peg household weights to person weights

format_income_bins(): Format income bin labels

geoid_pad(): Pad GEOID columns to fixed width

get_acs_bg_counts(): Get ACS block group counts (multi-unit)

get_acs_bg_counts_base(): Get ACS reference counts for block groups

get_acs_ethnicity(): Fetch ACS ethnicity table for imputation

get_acs_race(): Fetch and format ACS race table for imputation

get_age_upper_bounds(): Parse upper bounds from age class labels

get_bg_geom(): Fetch block group shapefile for study region

get_county_fips(): Get county FIPS codes for study region

get_day_groups(): Get day group data table

get_db_table_name(): Resolve full database table name for HTS

get_hh_person_sums(): Aggregate person-level variables to household totals

get_income_bounds(): Get income breaks from labels

get_income_broad(): Recode household income to broad bins

get_income_broad_xwalk(): Create income broad crosswalk

get_is_wfh(): Determine work-from-home status

get_max_income_bin(): Get maximum income bin

get_overlap_allocation(): Compute overlap allocation ratios between polygons

get_puma_geom(): Fetch PUMA shapefile for study region

get_puma_ids(): Get PUMA IDs for study region

get_pumas(): Get PUMA shapefile

get_python_path(): Get Python path from settings

get_settings(): Load and validate weighting pipeline settings

get_state_fips(): Get state FIPS codes for study region

get_target_level(): Extract Target Name and Level from Target String

get_target_map(): Extract target mapping from settings

get_target_methods(): List available target preparation methods

get_test_settings(): Initialize test settings for project

get_tracts_puma_xwalk(): Get tracts-to-PUMA crosswalk from Census

get_user_specs(): Get user specifications for puma groups or clustering method

get_xwalk_sfx_to_sfy(): Reallocate data between polygon sets

group_from_defined_list(): Prepare PUMA/Client Zone groups from defined list

impute_ethnicity(): Impute ethnicity for survey persons using ACS

impute_gender(): Impute gender for ambiguous responses

impute_income_nonrelatives(): Impute household income for nonrelative households

impute_income_pnta(): Impute household income for missing responses

impute_race(): Impute race for survey persons

integerize(): DEPRECATED. Integerize grouped columns to match totals

integerize_probs(): Deprecated Integerize probability columns by sampling#' @description Converts groups of probability columns to binary indicators by sampling, ensuring each row sums to 1. Use for multinomial assignment from probabilities.

kmeans_zone_groups(): Run k-means clustering on PUMA centroids and data

label_targets(): Label Targets Table

load_sf_obj(): Load the SF Object

load_xwalk(): Load tract-to-PUMA crosswalk file

make_binary(): Convert probability columns to binary indicators (max selection)

make_probs_binary(): DEPRECATED. Convert probability columns to binary indicators (sampling)

make_schema_patch_snippet(): Generate a JSON snippet to patch missing schema properties

parse_code_root(): Resolve code root directory

parse_raw_data_root(): Resolve raw data directory

parse_settings_path(): Resolve and validate settings file path

plot_bias_variance(): Create bias/variance plot, if data has been prepared

plot_weight_distribution(): Violin Plot of Household Weight Distribution by Zone Group

plot_weight_fit(): Plot survey vs. target fit by group

plot_zones_map(): Plot a map of the zone groups

popsim_calculate_importance(): Calculate target importance from confidence intervals

popsim_make_control_config(): Create control config for PopulationSim

popsim_make_geoxwalk(): Create geo crosswalk for PopulationSim

popsim_make_input_data(): Create seed and control data for PopulationSim

popsim_make_settings(): Write PopulationSim settings.yaml file

popsim_make_weights(): Run PopulationSim and generate final weights

popsim_search(): Run PopulationSim grid search over expansion factors and bounds

popsim_search_stats(): Compute diagnostics for PopulationSim grid search results

popsim_settings_defaults(): popsim_settings_defaults

popsim_settings_updates(): Update PopulationSim settings for additional geographies

prep_control_counts(): Prepare the control counts for use in reporting.

prep_control_tables(): Prepare the control (PUMS) tables for use in reporting.

prep_hhs_for_income_imputation(): Prepare household-level data for income imputation

prep_initial_expansion_data(): Prepare initial expansion summary table for weighting memo

prep_mape_totals(): Prepare the MAPE totals for use in reporting.

prep_seed_counts(): Prepare the seed counts for use in reporting.

prep_seed_tables(): Prepare the seed (survey) data for use in reporting.

prep_seed_weights(): Prepare the seed (survey) data + weights for use in reporting.

prep_target_adults(): Prepare number of adults target variable for weighting

prep_target_age(): Prepare age target variable for weighting

prep_target_commutemode(): Prepare commute mode target variable for weighting

prep_target_cross(): Prepare cross-tabulated targets for weighting

prep_target_edulevel(): Prepare education level target variable for weighting

prep_target_employment(): Prepare employment target variable for weighting

prep_target_ethnicity(): Prepare ethnicity target variable for weighting

prep_target_gender(): Prepare gender target variable for weighting

prep_target_h_size(): Prepare household size target variable for weighting

prep_target_income(): Prepare income target variable for weighting

prep_target_kids(): Prepare number of children target variable for weighting

prep_target_race(): Prepare race target variable for weighting

prep_target_univstudent(): Prepare university student target variable for weighting

prep_target_vehicles(): Prepare household vehicles target variable for weighting

prep_target_workers(): Prepare household workers target variable for weighting

prep_transit_target(): Prepare transit boardings target by day group

prep_weight_summary(): Prepare the weight summary for use in reporting.

prep_zone_groups_table(): Prepare the zone group table for the weighting memo

prep_zones_sf(): Prepare a simple features object of the zone groups

prepare_acs_income(): Prepare ACS income fractions for imputation

prepare_targets(): Prepare targets for weighting

prepare_trip_adj_model_dt(): Prepare data for trip rate model fitting

prepare_zone_groups(): Create zone group crosswalk for weighting

print_params(): Print loaded run parameters

pums_checksum(): Calculate PUMS checksums

read_from_db(): Read data from database as data.table

read_pums_codebook(): Read PUMS data dictionary from tidycensus

read_schema_properties(): Read the set of properties defined in the JSON schema

read_survey_codebook(): Deprecated: Read survey codebook

record_checksum(): Record checksums of arbitrary data

report_settings_schema_diff(): Console report for settings/schema differences

rescale_trip_rate_factors(): Rescale trip rate factors to minimum 1

run_tabulate_method(): Tabulate target variable by method

sampled_latlon_to_bg(): Convert sampled lat/lon points to block groups

save_daypat_results(): Calculate day pattern probabilities

set_defaults(): Set default values in settings

spectral_zone_groups(): Group PUMAs by sample rate using spectral clustering

summarize_data(): Summarize survey data with confidence intervals

summarize_person_types(): Summarize person type assignment

summarize_pums(): Summarize PUMS data with target updates and confidence intervals

summarize_survey(): Summarize survey data with target updates and confidence intervals

tabulate_target(): Tabulate frequency counts for variable

test_results(): Test project results against database

test_teardown(): Teardown test data after tests

unlist_levels(): Unlist a list to n-levels deep

update_data_root(): Setup weighting data root directory

update_daypat_seed(): Update seed and targets for day pattern model

update_daypat_targets(): Update day pattern model targets with predicted probabilities

update_directories(): Setup project subdirectories

update_income_broad_labels(): Update income_broad value labels

update_rate_report(): Update rate report with adjusted factors

update_settings_pums_vars(): Setup PUMS variable lists

update_targets(): Update targets by combining levels

weighted.median(): Calculate weighted median

weighted.percentile(): Calculate weighted percentile

write_to_db(): Write table to DB

zone_group_plots(): Plot Zone Groups