Package index • rbms

All functions

boot_sample(): boot_sample Generate n bootstrap sample of the monitoring sites to be used for each iteration.
check_names(): check_names Verify for the required column names in the data object.
check_package(): check_package Internal function to check if a package is installed.
check_pheno(): check_pheno Check for the flight curve of a specific year. If the specific year is missing, use the nearest year available within a 5-year period to impute missing count. Function used in impute_count.
collated_index(): collated_index compute a collated index from the site indices, using a Generalized Linear Model.
collated_index_old(): collated_index_old compute a collated index from the site indices, using a Generalized Linear Model.
day_week_summary(): day_week_summary Summarize the count(s) per day or week using either the maximum or the average count when multiple counts (visit) are observed.
df_visit_season(): df_visit_season Link each recorded visit to a corresponding monitoring season, this function is used in ts_monit_site
fit_bam_multi(): fit_bam_multi Fit a Generalized Additive Model using bam() with factor-smooth interactions to butterfly count data across multiple years simultaneously. This function fits a single model across multiple years using factor-smooth interactions (fs), which allows year-specific smooth terms while sharing information across years. This is more efficient than fitting separate models per year.
fit_gam(): fit_gam fit a Generalized Additive Model to butterfly count data along a temporal variable and accounting for site effect when multiple are available.
flight_curve(): flight_curve Compute the annual flight curve from butterfly count data collated across sites.
flight_curve_multi(): flight_curve_multi Compute flight curves across multiple years simultaneously using a single BAM model with factor-smooth interactions. This is more efficient than fitting separate models per year and can provide better estimates for years with sparse data.
flight_curve_optimised(): flight_curve_optimised Optimised version: Compute the annual flight curve from butterfly count data collated across sites.
get_nm(): get_nm Compute the normalized flight curve by fitting a spline in a Generalized Additive Model for one year 'y' to butterfly count data.
get_nm_optimised(): get_nm_optimised Optimised version: find the nearest year with a computed flight curve
get_nny(): get_nny find the nearest year with a computed flight curve
impute_count(): impute_count
initiate_project(): initiate_project Build the folder structure for a generic research project
m_count: Toy data set with butterfly count for x species across y sites
m_visit: Toy data set with the date when the sites have been visited for monitoring
plot(<pheno_curve>): plot.pheno_curve Generic method to plot the flight curve, where values are extracted from a pheno_curve object (outcome of the light_curve() function)
plot_flight_curve_multi(): plot_flight_curve_multi Plot annual flight curves from multi-year BAM model with each year displayed in a different color.
points(<pheno_curve>): points.pheno_curve Generic method to add points on a plot of the flight curve, where values are extracted from a pheno_curve object (outcome of the light_curve() function)
set_anchor(): set_anchor Add Anchors of "zeros" at determined distance on each side of the monitoring season with specific weight (length), this function is used by ts_monit_season()
site_index(): site_index Extract abundance indices per site and year based on flight curve imputation.
ts_date_seq(): ts_date_seq Generate a time-series with dates starting from January of the starting year to December of the ending years.
ts_dwmy_table(): ts_dwmy_table Generate a time-series of dates with day, week, month and year (dwmy) from one initial to an end years
ts_monit_count_site(): ts_monit_count_site Generate a full time series of observed count, for all sites and each day since a starting and ending years of the defined time-series
ts_monit_season(): ts_monit_season Build a time-series of dates with specific detail about the monitoring season
ts_monit_site(): ts_monit_site Augment the time series in m_season with all sites and visits with "zeros", leaving all non visited day with and <NA>