Package index • casteval

All functions

accuracy(): Get accuracy from quantiles

accuracy_help(): Calculate accuracy given a single quantile pair

apply_facets(): Apply facets to plot

bias(): Calculate forecast bias

calc_specified_time(): Calcuate relative/absolute time specified by user

create_forecast(): Create a forecast object

create_forecast_ensemble(): Create forecast from time vector and ensemble of realizations

crps(): Compute Continuous Ranked Probability Score for forecast

denmark2020df: Denmark 2020 COVID-19 forecast data frame

denmark2020ens: Denmark 2020 COVID-19 forecast ensemble

denmark2020fc: Denmark 2020 COVID-19 forecast object

denmark2020obs: Denmark 2020 COVID-19 observations

filter_forecast_time(): Isolate projected values from fit values

get_group_cols(): Get group column names

get_group_names(): Get group names of forecast data

get_plotting_groups(): Find the groups relevant to plotting

get_quant_col(): Get quantile column from data frame

get_quant_percentages(): Get quantile numbers from column names

get_quantile(): Obtain quantiles from data frame

get_time_point(): Get a row of a date frame for a given time

get_time_type(): Get type of data frame time column

group_all(): Group forecast data frame

groups1: Grouping example forecast 1

groups2: Grouping example forecast 2

groups_obs: Grouping example observations

has_groups(): Does the forecast data have groups?

integer_breaks(): Integer breaks on ggplot2 axes

is_forecast(): Is it a forecast?

is_valid_forecast(): Is it a valid forecast?

join_data(): Join two data frames

join_fcst_obs(): Join a forecast and observations into a single data frame

log_score(): Compute logarithmic score for forecast

log_score_approx_normal(): Logarithmic score for forecast assuming a normal distribution given paired quantiles.

make_accuracy(): accuracy() function factory

make_log_score(): log_score() function factory

pair_quantiles(): Pair up matching quantiles

parse_quant_pairs(): Parse quantile pair(s)

plot_KDE(): Plot the KDE used in log_score()

plot_ensemble(): Plot ensemble of forecast realizations

plot_forecast(): Plot a forecast

plot_mean(): Plot forecast mean

plot_obs_score(): Plot and score against observations

plot_observations(): Plot observations

plot_quant_intervals(): Plot quantile intervals

plot_quantiles(): Plot forecast quantiles

quant_name(): Turn quantile number into column name

score(): Score forecasts

validate_column(): Check that column is in data frame

validate_data_frame(): Validate forecast data frame

validate_fcst_obs_pair(): Validate a forecast-observations pair

validate_forecast(): Validate forecast in named-list format

validate_group_names(): Validate forecast group names

validate_obs(): Validate observations data frame

validate_plotting_groups(): Check that groups can be plotted

validate_quant(): Validate a single quantile

validate_quant_name(): Validate quantile column name

validate_quant_order(): Make sure quantile values are logically possible

validate_quant_pair(): Validate quantile interval vector

validate_time(): Check that time compatible with forecast

validate_time_column(): Validate time column