diff --git a/.github/workflows/secretScan.yml b/.github/workflows/secretScan.yml
new file mode 100644
index 0000000..1af40d6
--- /dev/null
+++ b/.github/workflows/secretScan.yml
@@ -0,0 +1,13 @@
+name: gitleaks
+
+on: [push, pull_request]
+
+jobs:
+ gitleaks:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v2
+ with:
+ fetch-depth: '0'
+ - name: gitleaks-action
+ uses: gitleaks/gitleaks-action@v1.6.0
diff --git a/DESCRIPTION b/DESCRIPTION
index 2684339..5e81ed6 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -18,7 +18,7 @@ Description: The 'salmix' package fits time-varying density curves to run
adult Pacific salmon, though could also be applied to other kinds
of data such as hydrographs, plant phenology (flowering, leaf out).
License: GPL (>=3)
-URL: https://ericward-noaa.github.io/phenomix, https://github.com/ericward-noaa/phenomix
+URL: https://noaa-nwfsc.github.io/phenomix, https://github.com/noaa-nwfsc/phenomix
Depends:
R (>= 4.0.0)
Imports:
@@ -42,5 +42,5 @@ ByteCompile: true
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.1
SystemRequirements: GNU make
diff --git a/README.Rmd b/README.Rmd
index f4f7cec..0212aa9 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -17,16 +17,16 @@ knitr::opts_chunk$set(
# phenomix
R package for fitting distributions to run timing data via maximum likelihood
-[![R build status](https://github.com/ericward-noaa/phenomix/workflows/R-CMD-check/badge.svg)](https://github.com/ericward-noaa/phenomix/actions)
+[![R build status](https://github.com/noaa-nwfsc/phenomix/workflows/R-CMD-check/badge.svg)](https://github.com/noaa-nwfsc/phenomix/actions)
-pkgdown site: [https://ericward-noaa.github.io/phenomix/](https://ericward-noaa.github.io/phenomix/)
+pkgdown site: [https://noaa-nwfsc.github.io/phenomix/](https://noaa-nwfsc.github.io/phenomix/)
## Installation
You can install phenomix with:
```{r, eval=TRUE}
-remotes::install_github("ericward-noaa/phenomix",build_vignettes = TRUE)
+remotes::install_github("noaa-nwfsc/phenomix",build_vignettes = TRUE)
```
Load libraries
@@ -68,7 +68,7 @@ ggplot(df, aes(doy,exp(y),col=year,group=year)) +
## Examples
-The main functions are `create_data()` and `fit()`. See `?create_data` and `?fit` for additional details and examples. A vignette includes additional detail, and examples of several models as well as function arguments available [https://ericward-noaa.github.io/phenomix/](https://ericward-noaa.github.io/phenomix/).
+The main functions are `create_data()` and `fit()`. See `?create_data` and `?fit` for additional details and examples. A vignette includes additional detail, and examples of several models as well as function arguments available [https://noaa-nwfsc.github.io/phenomix/](https://noaa-nwfsc.github.io/phenomix/).
## References
diff --git a/README.md b/README.md
index 3d389ef..7eea037 100644
--- a/README.md
+++ b/README.md
@@ -7,17 +7,31 @@ R package for fitting distributions to run timing data via maximum
likelihood
[![R build
-status](https://github.com/ericward-noaa/phenomix/workflows/R-CMD-check/badge.svg)](https://github.com/ericward-noaa/phenomix/actions)
+status](https://github.com/noaa-nwfsc/phenomix/workflows/R-CMD-check/badge.svg)](https://github.com/noaa-nwfsc/phenomix/actions)
-[![DOI](https://zenodo.org/badge/243336401.svg)](https://zenodo.org/badge/latestdoi/243336401)
-pkgdown site:
+pkgdown site:
## Installation
You can install phenomix with:
``` r
-remotes::install_github("ericward-noaa/phenomix",build_vignettes = TRUE)
+remotes::install_github("noaa-nwfsc/phenomix",build_vignettes = TRUE)
+#> Downloading GitHub repo noaa-nwfsc/phenomix@HEAD
+#>
+#> ── R CMD build ─────────────────────────────────────────────────────────────────
+#> checking for file ‘/private/var/folders/ts/4x6hzmfx7d52vbhjqmrs_3pw0000gp/T/RtmpmdCE3X/remotes13c339d5e769/noaa-nwfsc-phenomix-9f8e792/DESCRIPTION’ ... ✔ checking for file ‘/private/var/folders/ts/4x6hzmfx7d52vbhjqmrs_3pw0000gp/T/RtmpmdCE3X/remotes13c339d5e769/noaa-nwfsc-phenomix-9f8e792/DESCRIPTION’
+#> ─ preparing ‘phenomix’:
+#> checking DESCRIPTION meta-information ... ✔ checking DESCRIPTION meta-information
+#> ─ cleaning src
+#> ─ installing the package to build vignettes
+#> creating vignettes ... ✔ creating vignettes (1m 39.8s)
+#> ─ cleaning src
+#> ─ checking for LF line-endings in source and make files and shell scripts
+#> ─ checking for empty or unneeded directories
+#> ─ building ‘phenomix_1.0.4.tar.gz’
+#>
+#>
```
Load libraries
@@ -25,19 +39,24 @@ Load libraries
``` r
library(phenomix)
library(ggplot2)
+#> Warning: package 'ggplot2' was built under R version 4.3.2
```
## Functions
-The package phenomix provides a suite of curve fitting to describe data
+The package pheomix provides a suite of curve fitting to describe data
that may be generated from a process when distributions in time might be
concentrated (from fisheries, this occurs with counts over time of
salmon returning from the ocean to spawn or juvenile fish emigrating
from streams to the ocean).
-![Predicted (black line) and observed counts (red dots) for hypothetical
-dataset. Multiple observations may exist for some days, or no
-observations on others.](README-figs/unnamed-chunk-5-1.png)
+
In a given year, the curve might be described by a symmetric or
asymmetric Gaussian or Student-t distribution (shown here in log-scale
@@ -45,14 +64,14 @@ on the y-axis). Questions of interest might be - are the means (x-axis)
shifting through time? - are the variances shifting through time? - does
the model support a symmetric or asymmetric distribution?
-![](man/figures/unnamed-chunk-6-1.png)
+![](README-figs/unnamed-chunk-6-1.png)
## Examples
The main functions are `create_data()` and `fit()`. See `?create_data`
and `?fit` for additional details and examples. A vignette includes
additional detail, and examples of several models as well as function
-arguments available .
+arguments available .
## References
diff --git a/docs/404.html b/docs/404.html
index f58d70c..f9eabe2 100644
--- a/docs/404.html
+++ b/docs/404.html
@@ -66,7 +66,7 @@
library(ggplot2)
-library(phenomix)
-#> Warning in checkMatrixPackageVersion(): Package version inconsistency detected.
-#> TMB was built with Matrix version 1.5.4
-#> Current Matrix version is 1.5.4.1
-#> Please re-install 'TMB' from source using install.packages('TMB', type = 'source') or ask CRAN for a binary version of 'TMB' matching CRAN's 'Matrix' package
+#> Warning: package 'ggplot2' was built under R version 4.3.2
+library(phenomix)library(dplyr)library(TMB)
diff --git a/docs/articles/a1_examples_files/figure-html/unnamed-chunk-14-1.png b/docs/articles/a1_examples_files/figure-html/unnamed-chunk-14-1.png
index 03134ce..0285b6e 100644
Binary files a/docs/articles/a1_examples_files/figure-html/unnamed-chunk-14-1.png and b/docs/articles/a1_examples_files/figure-html/unnamed-chunk-14-1.png differ
diff --git a/docs/articles/a1_examples_files/figure-html/unnamed-chunk-16-1.png b/docs/articles/a1_examples_files/figure-html/unnamed-chunk-16-1.png
index fe86385..e198bf7 100644
Binary files a/docs/articles/a1_examples_files/figure-html/unnamed-chunk-16-1.png and b/docs/articles/a1_examples_files/figure-html/unnamed-chunk-16-1.png differ
diff --git a/docs/articles/a2_covariates.html b/docs/articles/a2_covariates.html
index 01a2ac8..48644df 100644
--- a/docs/articles/a2_covariates.html
+++ b/docs/articles/a2_covariates.html
@@ -67,7 +67,7 @@
library(ggplot2)
-library(phenomix)
-#> Warning in checkMatrixPackageVersion(): Package version inconsistency detected.
-#> TMB was built with Matrix version 1.5.4
-#> Current Matrix version is 1.5.4.1
-#> Please re-install 'TMB' from source using install.packages('TMB', type = 'source') or ask CRAN for a binary version of 'TMB' matching CRAN's 'Matrix' package
+#> Warning: package 'ggplot2' was built under R version 4.3.2
+library(phenomix)library(dplyr)library(TMB)
library(ggplot2)
-library(phenomix)
-#> Warning in checkMatrixPackageVersion(): Package version inconsistency detected.
-#> TMB was built with Matrix version 1.5.4
-#> Current Matrix version is 1.5.4.1
-#> Please re-install 'TMB' from source using install.packages('TMB', type = 'source') or ask CRAN for a binary version of 'TMB' matching CRAN's 'Matrix' package
+#> Warning: package 'ggplot2' was built under R version 4.3.2
+library(phenomix)library(dplyr)library(TMB)
library(ggplot2)
-library(phenomix)
-#> Warning in checkMatrixPackageVersion(): Package version inconsistency detected.
-#> TMB was built with Matrix version 1.5.4
-#> Current Matrix version is 1.5.4.1
-#> Please re-install 'TMB' from source using install.packages('TMB', type = 'source') or ask CRAN for a binary version of 'TMB' matching CRAN's 'Matrix' package
+#> Warning: package 'ggplot2' was built under R version 4.3.2
+library(phenomix)library(dplyr)library(TMB)
We will start with the original simple model fit to the
diff --git a/docs/articles/index.html b/docs/articles/index.html
index 2b84b08..924627e 100644
--- a/docs/articles/index.html
+++ b/docs/articles/index.html
@@ -45,7 +45,7 @@
Ward EJ (2024).
phenomix: Fit Density Curves to Peak Timing Data that Varies over Time.
-https://ericward-noaa.github.io/phenomix, https://github.com/ericward-noaa/phenomix.
+https://noaa-nwfsc.github.io/phenomix, https://github.com/noaa-nwfsc/phenomix.
@Manual{,
title = {phenomix: Fit Density Curves to Peak Timing Data that Varies over Time},
author = {Eric J. Ward},
- year = {2023},
- note = {https://ericward-noaa.github.io/phenomix, https://github.com/ericward-noaa/phenomix},
+ year = {2024},
+ note = {https://noaa-nwfsc.github.io/phenomix, https://github.com/noaa-nwfsc/phenomix},
}
The package pheomix provides a suite of curve fitting to describe data that may be generated from a process when distributions in time might be concentrated (from fisheries, this occurs with counts over time of salmon returning from the ocean to spawn or juvenile fish emigrating from streams to the ocean).
-
-
Predicted (black line) and observed counts (red dots) for hypothetical dataset. Multiple observations may exist for some days, or no observations on others.
-
-
In a given year, the curve might be described by a symmetric or asymmetric Gaussian or Student-t distribution (shown here in log-scale on the y-axis). Questions of interest might be - are the means (x-axis) shifting through time? - are the variances shifting through time? - does the model support a symmetric or asymmetric distribution?
-
+
In a given year, the curve might be described by a symmetric or asymmetric Gaussian or Student-t distribution (shown here in log-scale on the y-axis). Questions of interest might be - are the means (x-axis) shifting through time? - are the variances shifting through time? - does the model support a symmetric or asymmetric distribution?
Count data collected by Washington Department of Fish and Wildlife on
chum salmon from the Skagit River (Washington state). Each row of the
dataframe contains an observation ("number") on a given date ("date").
The year ("year") and calendar day ("doy") are also included.