add_char_demo           Add characteristic by matching on demographics
add_characteristic      Add a characteristic to an existing population
align_pums              Match the pums variables with marginal totals
allocate_count          Re-allocate excess counts to other locations
assign_place            Assign a place to a person
assign_place_coords     Assign a place with long/lat coords to a
                        synthetic population
assign_schools          Assign schools to a synthetic population.
assign_schools_inner    Function which assigns schools
assign_weights          Assign weights for ipf-based sampling
assign_workplaces       Assign an ESRI workplace to synthetic
                        population
assign_workplaces_inner
                        Function which assigns workplaces
base_map_theme          The base map theme for SPEW
calc_dists              Calculate distance b/w cont table row and pums
call_spew               Wrapper for reading, formatting, and writing
                        SPEW ecosystems
cap_default             How to weight the capacities of of school.
ccount                  Adjust number of households
checkDF                 Check if df is in the right format
check_logfile           Check to see if a SPEW log-file is complete
check_path              Check the path to output to run diags
check_place_ids         Check the Place ID's match
check_pop_table         Check the pop_table has all the necessary
                        components
check_puma_ids          Check the puma id's match
check_pums              Check that the pums has all the required
                        components
check_shapefile         Check the shapefile has the necessary
                        components
check_var_names         Check to see if variable names are in SPEW
                        outputs
clean_names             Remove whitespace, capitals, and non ASCII
combine_counts          Combine two rows of a pop_table into one
create_column           Parse a SPEW Log-file to into an appropriate
                        column
delaware                Input data for Keny County, Delaware
demo_sample             Sample extra characteristics from char pums and
                        add them to the pop df
euclidean_dist          Get the euclidean distance between two points
                        (x1, y1) and (x2, y2)
extract_st_co_tr        Extract the state, county, and tract ID from a
                        string
extrapolate_probs_to_pums
                        Take unique probabilites for table and spread
                        them to rest of PUMS
extrapolate_probs_to_pums_joint
                        Take unique probabilites for table and spread
                        them to rest of PUMS
fill_cont_table         Fill marginal contingency with ipf
fips_to_name            Translate FIPS number to place name
format_data             Format data before entering make
get_base_map            Get the base map for plotting
get_centers             Get the center longitude and latitude for each
                        region
get_coords_scaled       Getting a plotting data frame
get_data_group          Extract data-group from location name
get_dfs                 Get the dataframes from SPEW summary output
get_dist_mat            Get the distance matrix
get_dists               Get the distances between the schools and the
                        people.
get_envs                Gather the unique assignments for the region
get_filenames           Get the filenames of the SPEW output, separated
                        by the level
get_header              Extract the header from a population
get_level               Obtain the correct level for ipums data
get_pop_totals          Get the population totals from a summarized
                        SPEW region
get_rows                Extract rows with a certain character
get_shapefile_indices   Obtain the shapefile indices corresponding to
                        the pop table
get_targets             Obtain the target marginals for IPF
get_total_time          Extract the total run-time from a SPEW log-file
get_weight_dists        Weight place assignment probabilities
haversine               Get the haversine distance between two points
                        (x1, y1) and (x2, y2) scaled between 0 and 1.
haversine_dist          Get the haversine distance between two points
                        (x1, y1) and (x2, y2) scaled between 0 and 1.
impute_missing_vals     Impute Missing Values in a data frame
make_ipf_obj            Set up for creating a set of marginal
                        information for IPF sampling
make_mm_obj             Make moment matching object
merge_reduce            Wrapper function for merge
organize_summaries      Organize the summaries into a more palatable
                        format
people_to_households    Convert a population count to household count
plot_agents             Plot the agents of synthetic ecosystem
plot_bds                Plot the boundaries of the synthetic ecosystem
plot_characteristic_proportions
                        Plot characteristic summary output from
                        summarize_top_spew_region
plot_env                Plot the environments of the synthetic
                        ecosystem
plot_interior           Plot the interior of the synthetic ecosystem
plot_labs               Add the labels and the theme to the plot
plot_pop_totals         Plot characteristic summary output from
                        summarize_top_spew_region as totals
plot_region             Plot SPEW region
plot_roads              Plot the roads of the synthetic ecosystem
plot_syneco             Plot Synthetic Ecosystem
print_region_list       Write out information on each region
read_data               Read SPEW input data from files
read_marginals          Read in the marginals population characteristic
                        totals
read_moments            Read in the R data object for moment matching
read_pop_table          Read in the population counts
read_roads              Read in road lines shapefiles
read_shapespatial_to_ogr
                        Read in shapefile using readOGR
remove_count            Remove a row from the pop_table
remove_excess           Remove comma's, accents, etc. from name
remove_holes            Remove holes from an object of class Polygon
remove_words            Remove excess words
replace_word            Replace an existing word
samp_roads              Sample the locations from a SpatialLines object
sample_households       Sample appropriate indices from household PUMS
sample_ipf              Sample households PUMS accoording to IPF
sample_locations        Generic sampling locations function
sample_locations_roads
                        Sample coordinates from roads
sample_locations_uniform
                        Sample from a particular polygon shapefile
sample_mm               Sample households PUMS according to MM
sample_people           Sample from the individual person PUMS data
                        frame
sample_uniform          Sample households uniformly
sample_with_cont        Sample from pums
solve_mm_for_joint      Do the Moment Matching solving for joint
                        distribution
solve_mm_for_var        Do the MM solving for an individual variable
solve_mm_weights        Weight the records of the PUMS so the averages
                        in mm_df will be obtained
spew                    SPEW algorithm to generate synthetic ecosystems
spew-package            spew: an R package for generating synthetic
                        ecosystems
spew_mc                 Run SPEW in Parallel with a Multicore backend
spew_mpi                Run SPEW in Parallel with an MPI backend
spew_place              Generate synthetic ecosystem for single place
spew_seq                Run SPEW Sequentially
spew_sock               Run SPEW in Parallel with a SOCK backend
spewlog_to_df           Convert a SPEW Logfile into a data-frame
standardize_pop_table   Make sure pop_table has the appropriate columns
subset_pums             Align pums with marginal totals
subset_schools          Subset the schools to that of the county
subset_shapes_roads     Subset the shapefile and road lines to proper
                        roads within specified tract
summarize_environment   Return the unique environment assignments in a
                        region
summarize_features      Summarize individual features of a region
summarize_spew          Summarize a SPEW region
summarize_spew_out      Summarize spew output
summarize_spew_region   Summarize a singular region from spew output
summarize_syneco        Summarize synthetic ecosystem for SPEW console
                        output
summarize_top_region    Summarize the region in a more human-readable
                        format
tartanville             Input data for Tartanville
update_freqs            Update frequencies to match # of households
uruguay                 Input data for Uruguay
us                      Table of US states and counties
us_pums_sf              An example marginal distribution table
verify_column           Verify the column is the correct size
weight_dists            Weight school assignment probabilities
weight_dists2           Weight school assignment probabilities
weight_dists_C          Weight school assignment probabilities by
                        capacity only
weight_dists_D          Weight school assignment probabilities,
                        distance only
write_data              Output our final synthetic populations as csv's
write_pop_table         Write out the final, formatted table
write_schools           Write school environment
write_workplaces        Write workplaces environment
