文件列表:

.Rbuildignore (225, 2023-10-26)
CODE_OF_CONDUCT.md (2392, 2023-10-26)
DESCRIPTION (998, 2023-10-26)
NAMESPACE (1795, 2023-10-26)
NEWS.md (1319, 2023-10-26)
R (0, 2023-10-26)
R\acceptreject.R (1139, 2023-10-26)
R\bsims_all.R (2833, 2023-10-26)
R\bsims_animate.R (4990, 2023-10-26)
R\bsims_detect.R (8802, 2023-10-26)
R\bsims_init.R (754, 2023-10-26)
R\bsims_populate.R (2324, 2023-10-26)
R\bsims_transcribe.R (4578, 2023-10-26)
R\col2hex.R (253, 2023-10-26)
R\dist_fun2.R (775, 2023-10-26)
R\draw_ellipse.R (2456, 2023-10-26)
R\estimate.R (1198, 2023-10-26)
R\events.R (970, 2023-10-26)
R\expand_list.R (613, 2023-10-26)
R\get_detections.R (2003, 2023-10-26)
R\get_events.R (722, 2023-10-26)
R\get_raw_events.R (432, 2023-10-26)
R\getters.R (1385, 2023-10-26)
R\lines.bsims_detections.R (374, 2023-10-26)
R\lines.bsims_events.R (383, 2023-10-26)
R\lines.bsims_transcript.R (398, 2023-10-26)
R\plot.bsims_detections.R (2069, 2023-10-26)
R\plot.bsims_detectionss_table.R (1148, 2023-10-26)
R\plot.bsims_events.R (1216, 2023-10-26)
R\plot.bsims_events_table.R (910, 2023-10-26)
R\plot.bsims_landscape.R (941, 2023-10-26)
R\plot.bsims_population.R (523, 2023-10-26)
R\plot.bsims_transcript.R (3294, 2023-10-26)
R\points.bsims_detections.R (285, 2023-10-26)
... ...

# bSims: Bird Point Count Simulator A highly scientific and utterly addictive bird point count simulator to test statistical assumptions and to aid survey design. [](https://CRAN.R-project.org/package=bSims) [](https://peter.solymos.org/bSims/) [](https://github.com/psolymos/bSims/actions/workflows/check.yml) > *“I’ve yet to see any problem, however complicated, which when you > looked at it the right way didn’t become still more complicated.”* – > Poul Anderson, Call Me Joe > *“Love the simulation we’re dreaming in”* - Dua Lipa, Physical The goal of the package is to: - test statistical assumptions, - aid survey design, - and have fun while doing it! Design objectives: - small (point count) scale implementation, - habitat is considered homogeneous except for edge effects, - realistic but efficient implementation of biological mechanisms and observation process, - defaults chosen to reflect common practice and assumptions, - extensible (PRs are welcome). See the package in action in the [**QPAD Book**](https://peter.solymos.org/qpad-book/). Check out the [**QPAD workshop**](https://peter.solymos.org/qpad-workshop/). Read/cite the paper [**Agent-based simulations improve abundance estimation**](https://rdcu.be/doDwI) (DOI 10.1007/s42977-023-00183-2). ## Install CRAN version: ``` r install.packages("bSims") ``` Development version: ``` r remotes::install_github("psolymos/bSims") ``` See what is new in the [NEWS](NEWS.md) file. ## License [GPL-2](https://www.gnu.org/licenses/old-licenses/gpl-2.0.html) Please cite (see `citation("bSims")`) the paper: Solymos, P. 2023. Agent-based simulations improve abundance estimation. *Biologia Futura*, [DOI 10.1007/s42977-023-00183-2](https://doi.org/10.1007/s42977-023-00183-2), [link to PDF](https://rdcu.be/doDwI). ## Contributing Feedback and contributions are welcome: - submit feature request or report issues [here](https://github.com/psolymos/bSims/issues), - fork the project and submit pull request, see [CoC](CODE_OF_CONDUCT.md). ## Examples ### Command line ``` r library(bSims) phi <- 0.5 tau <- 1:3 dur <- 10 rbr <- c(0.5, 1, 1.5, Inf) tbr <- c(3, 5, 10) l <- bsims_init(10, 0.5, 1) p <- bsims_populate(l, 1) a <- bsims_animate(p, vocal_rate=phi, duration=dur) o <- bsims_detect(a, tau=tau) x <- bsims_transcribe(o, tint=tbr, rint=rbr) get_table(x) #> 0-3min 3-5min 5-10min #> 0-50m 2 0 0 #> 50-100m 3 0 1 #> 100-150m 4 1 0 #> 150+m 8 3 0 head(get_events(a)) #> x y t v a i #> 1 4.6025783 -3.3454607 0.02565467 1 302 99 #> 2 -0.7210853 -1.4680273 0.03151759 1 190 43 #> 3 -2.7191418 1.6200508 0.03152603 1 78 7 #> 4 -2.1041934 -1.6092706 0.03936096 1 295 19 #> 5 -4.9531338 -0.4093427 0.04890537 1 217 11 #> 6 -3.3127196 -0.2145475 0.08899310 1 215 15 head(get_detections(o)) #> x y t v a d f i j #> 2 -0.7210853 -1.46802728 0.03151759 1 190 1.6355636 NA 43 43 #> 8 -0.7210853 -1.46802728 0.11174961 1 323 1.6355636 NA 43 43 #> 13 0.5770***4 -0.47429169 0.17337408 1 152 0.7469***5 NA 62 62 #> 17 -1.3566956 -0.44725546 0.2077***72 1 213 1.4285169 NA 38 38 #> 25 0.5355406 -3.24873232 0.42514210 1 330 3.2925773 NA 63 63 #> 29 0.9538955 0.07***1822 0.48471447 1 160 0.9569516 NA 56 56 ``` ### Shiny apps A few [Shiny](https://shiny.posit.co/) apps come with the package. These can be used to interactively explore the effects of different settings. Compare distance functions: ``` r run_app("distfunH") run_app("distfunHER") ``` Compare simulation settings for single landscape: ``` r run_app("bsimsH") run_app("bsimsHER") ``` ### Replicating simulations Interactive sessions can be used to explore different settings. Settings can be copied from the Shiny apps and replicated using the `bsims_all` function: ``` r b <- bsims_all(extent=5, road=1, density=c(1,1,0)) b #> bSims wrapper object with settings: #> extent : 5 #> road : 1 #> density: 1, 1, 0 ``` The object has handy methods: ``` r b$settings() # retrieve settings b$new() # replicate once b$replicate(10) # replicate 10x ``` The `$replicate()` function also runs on multiple cores: ``` r library(parallel) b <- bsims_all(density=0.5) B <- 4 # number of runs nc <- 2 # number of cores ## sequential system.time(bb <- b$replicate(B, cl=NULL)) #> user system elapsed #> 0.225 0.001 0.227 ## parallel clusters cl <- makeCluster(nc) ## note: loading the package is optional system.time(clusterEvalQ(cl, library(bSims))) #> user system elapsed #> 0.001 0.000 0.519 system.time(bb <- b$replicate(B, cl=cl)) #> user system elapsed #> 0.004 0.001 0.188 stopCluster(cl) ## parallel forking if (.Platform$OS.type != "windows") { system.time(bb <- b$replicate(B, cl=nc)) } #> user system elapsed #> 0.128 0.032 0.168 ```

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