Title:	Estimate Phenological Metrics using Presence-Only Data
Version:	0.1.3
Description:	Generates Weibull-parameterized estimates of phenology for any percentile of a distribution using the framework established in Cooke (1979) <doi:10.1093/biomet/66.2.367>. Extensive testing against other estimators suggest the weib_percentile() function is especially useful in generating more accurate and less biased estimates of onset and offset (Belitz et al. 2020) <doi:10.1111/2041-210X.13448>. Non-parametric bootstrapping can be used to generate confidence intervals around those estimates, although this is computationally expensive. Additionally, this package offers an easy way to perform non-parametric bootstrapping to generate confidence intervals for quantile estimates, mean estimates, or any statistical function of interest.
Suggests:	knitr, rmarkdown, testthat
VignetteBuilder:	knitr
Depends:	R (≥ 3.5)
Imports:	boot, fitdistrplus, stats
License:	CC0
URL:	https://github.com/mbelitz/phenesse
BugReports:	https://github.com/mbelitz/phenesse/issues
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.1.0
NeedsCompilation:	no
Packaged:	2025-06-09 20:07:11 UTC; micha
Author:	Michael Belitz [aut, cre], Caitlin Campbell [ctb], Daijiang Li [ctb]
Maintainer:	Michael Belitz <michaelbelitz06@gmail.com>
Repository:	CRAN
Date/Publication:	2025-06-09 23:50:06 UTC

Calculate confidence intervals using bootstrap of any statistical function of interest.

Description

Calculate confidence intervals using bootstrap of any statistical function of interest.

Usage

estimate_ci(
  observations,
  .f,
  n_boots,
  parallelize = "no",
  ncpus = getOption("boot.ncpus", 1L),
  cl = NULL,
  type = "perc",
  conf = 0.95
)

Arguments

Value

A data frame with estimate, and the lower and upper points of its confidence interval

Example iNaturalist-sourced data

Description

Example data downloaded from iNaturalist.org for the Washington, DC area using the bounding box bounds = c(38, -77, 39, -76). Data was downloaded on 10/30/2019 for four species: Speyeria cybele, Danaus plexippus, Rudbeckia hirta, and Asclepias syriaca. These data were not scored to mark phenology, so all life stages/reproductive stages are included in the download.The download only includes 2019 observations and the doy (day of year)column was added post data download by MW Belitz using the lubridate package.

Usage

data(inat_examples)

Format

A data frame with 252 rows and 6 variables:

scientific_name

binomial of species

latitude

latitude where observations occurred

longitude

longitude where observations occurred

common_name

common name related to species

observed_on

original date listed of observation

doy

day of year when the observation occurred, variable created by MW Belitz using the package lubridate

References

https://www.inaturalist.org/

Examples

data(inat_examples)
## Not run: 
View(inat_examples)

## End(Not run)

Calculating the confidence intervals (CIs) of an arithmetic mean.

Description

mean_ciFunction estimates CIs using nonparametric bootstrapping around a mean estimate.

Usage

mean_ci(observations, bootstraps = 1e+05, conf = 0.95, type = "perc")

Arguments

Value

The estimated CIs around a mean estimate.

Functions

• mean_ci: Estimates CIs around a mean percentile estimate using non-parametric bootstrapping from the boot package

Examples

# Estimate when the mean observation of Rudbeckia hirta for the year 2019 up
# to October
data(inat_examples)
r_hirta <- subset(inat_examples, scientific_name == "Rudbeckia hirta")
mean_ci(observations = r_hirta$doy , bootstraps = 100)

# note low number of bootstraps for quick processing speed

Calculating the confidence intervals (CIs) of a quantile estimate of a a vector of observations using non-parametric bootstrapping.

Description

quantile_ciEstimates CIs around a quantile percentile estimate using non-parametric bootstrapping from the boot package

Usage

quantile_ci(
  observations,
  percentile,
  bootstraps = 1e+05,
  conf = 0.95,
  type = "perc"
)

Arguments

Value

The quantile estimate and confidence intervals.

Examples

# Gather sightings of iNaturalist observations for four species:
# Danaus plexippus, Speyeria cybele, Rudbeckia hirta, and Asclepias syriaca

# Estimate when the first 10 percent of individuals of the butterfly species
# Speyeria cybele are in flight.

data(inat_examples)
s_cybele <- subset(inat_examples, scientific_name == "Speyeria cybele")
quantile_ci(observations = s_cybele$doy, percentile = 0.1, bootstraps = 100)

# note low number of bootstraps for quick processing speed

Calculating a percentile estimate of a seasonal abundance distribution from incidental observations.

Description

weib_percentile uses empirical bootstrapping to estimate a percentile of the Weibull distribution, given random variables.

Usage

weib_percentile(observations, percentile, iterations = 500)

Arguments

Value

The Weibull-corrected estimate of the percentile of interest.

Examples

# Gather sightings of iNaturalist observations for four species:
# Danaus plexippus, Speyeria cybele, Rudbeckia hirta, and Asclepias syriaca

# Estimate when the first 50 percent of individuals of the milkweed species
# Asclepias syriaca have been observed.

data(inat_examples)
a_syriaca <- subset(inat_examples, scientific_name == "Asclepias syriaca")
weib_percentile(a_syriaca$doy, percentile = 0.5, iterations = 500)

# Estimate when 90 percent of individuals of the milkweed species A. syriaca
# have been observed, using only 100 iterations for quicker processing. To
# get a more stable result, more iterations should be used.

weib_percentile(a_syriaca$doy, percentile = 0.9, iterations = 100)

Calculating the CIs of a percentile estimate of a seasonal abundance distribution using the non-parametric bootstrapping.

Description

weib_percentile_ci uses non-parametric bootstrapping from the boot package to estimate 95

Usage

weib_percentile_ci(
  observations,
  iterations,
  percentile,
  bootstraps,
  type = "perc",
  conf = 0.95,
  parallelize = "no",
  ncpus = getOption("boot.ncpus", 1L),
  cl = NULL
)

Arguments

Value

The Weibull-corrected estimate of the percentile of interest and CIs.

Examples

# Gather sightings of iNaturalist observations for four species:
# Danaus plexippus, Speyeria cybele, Rudbeckia hirta, and Asclepias syriaca

# Estimate when the first 50 percent of individuals of the butterfly species
# Speyeria cybele are in flight. Note, only 10 iterations are beingg used
# in this example to keep computation time low, but more iterations should
# be used to get a more stable result. See vignette for parallelization
# options.


data(inat_examples)
s_cybele <- subset(inat_examples, scientific_name == "Speyeria cybele")
weib_percentile_ci(observations = s_cybele$doy, iterations = 10,
                   percentile = 0.5, bootstraps = 100)