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one more edit #9

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one more edit
ZacharyRWeaver Nov 30, 2023
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ZacharyRWeaver Nov 30, 2023
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ZacharyRWeaver Nov 30, 2023
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2 changes: 1 addition & 1 deletion DESCRIPTION
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Expand Up @@ -36,7 +36,7 @@ Description: Calculates profile repeatability for replicate stress response
Depends:
R (>= 2.10)
License: MIT + file LICENSE
URL: https://github.com/ubeattie/profrep
URL: https://ubeattie.github.io/profrep/
BugReports: https://github.com/ubeattie/profrep/issues
Encoding: UTF-8
Roxygen: list(markdown=TRUE)
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4 changes: 2 additions & 2 deletions README.Rmd
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Expand Up @@ -29,7 +29,7 @@ If researchers have multiple stress response curves for an individual, they may
The current standard in the field is to use linear mixed-effect models (Baugh et al. 2014; Dingemanse and Dochtermann, 2013), however this type of repeatability estimate can only be done on populations and on only one timepoint at a time.
Reed et al. (2019) have proposed "Profile Repeatability," which uses the full stress response curve (across time) to estimate repeatability for individuals.

'profrep' is a R package for computing profile repeatability on any number of individuals, any number of timepoints, and any number of replicate stress resposne curves. A full explanation of the math behind Profile Repeatability can be found in Reed et al. (2019).
'profrep' is a R package for computing profile repeatability on any number of individuals, any number of timepoints, and any number of replicate stress response curves. A full explanation of the math behind Profile Repeatability can be found in Reed et al. (2019).

## Installation

Expand Down Expand Up @@ -61,7 +61,7 @@ library(profrep)

my_data <- profrep::synthetic_data_four_point
n_trials <- 4 # or however many trials/rows of data per individual exist
profrep::profrep(df=my_data, n_trials=n_trials)
profrep::profrep(df=my_data, n_timepoints=n_trials)
```

## License
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4 changes: 2 additions & 2 deletions README.md
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Expand Up @@ -31,7 +31,7 @@ to estimate repeatability for individuals.

‘profrep’ is a R package for computing profile repeatability on any
number of individuals, any number of timepoints, and any number of
replicate stress resposne curves. A full explanation of the math behind
replicate stress response curves. A full explanation of the math behind
Profile Repeatability can be found in Reed et al. (2019).

## Installation
Expand Down Expand Up @@ -71,7 +71,7 @@ library(profrep)

my_data <- profrep::synthetic_data_four_point
n_trials <- 4 # or however many trials/rows of data per individual exist
profrep::profrep(df=my_data, n_trials=n_trials)
profrep::profrep(df=my_data, n_timepoints=n_trials)
#> individual n_crossings max_variance ave_variance base_score final_score rank
#> 1 E 0 6.67 5.42 12.10 0.9925 1
#> 2 B 0 15.00 12.92 27.95 0.9912 2
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4 changes: 2 additions & 2 deletions inst/CITATION
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Expand Up @@ -2,8 +2,8 @@ bibentry(
"Article",
title = "Investigating metrics of individual repeatability of the stress response",
author = "Beattie, U.K., Romero, L.M., and Reed, J.M.",
year = "in prep.",
journal = "",
year = "2024",
journal = "in prep.",
volume = 0,
number = 0,
pages = 0,
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