# A Model for Bone Compactness. A Model for Bone Compactness. The lastest version of this package can always been installed using: install.packages(c("imager", "tiff", "ijtiff", "HelpersMG", "knitr", "rmarkdown", "openxlsx", "shiny", "fields")) install.packages("http://marc.girondot.free.fr/CRAN/HelpersMG.tar.gz", repos=NULL, type="source") install.packages("http://marc.girondot.free.fr/CRAN/BoneProfileR.tar.gz", repos=NULL, type="source") BoneProfileR uses a new results management software that is developed as part of the HelpersMG package. Using this results management system (RM), all the results are stored as part of the analyzed image. Online simplified version can be found at: http://marc.girondot.free.fr/BoneProfilerR.html This results management software has been developed to help users to maintain the results associated with the methodology used to obtain it. It is part of the large movement in science of replicative research. An analysis is then stored with the image in a single file with the following information: name, timestamp, bg, fg, threshold, contour, centers, peripherie, compactness, array.compactness, cut.distance.center, cut.angle, used.centers, compactness.synthesis, partial, rotation.angle, global.compactness, optim, optimRadial Several analyses can be stored within a single file. ## Details A model for bone compactness. ||| |:--|:--| |Package:|BoneProfileR| |Type:|Package| |Version:|4.0 build 1089| |Date:|2025-07-07| |License:|GPL (>= 2)| |LazyLoad:|yes| ## Examples ```r ## Not run: # Not run: library(BoneProfileR) path_Hedgehog <- system.file("extdata", "Erinaceus_europaeus_fem_2-1_small.png", package = "BoneProfileR") bone <- BP_OpenImage(file=path_Hedgehog) plot(bone, type="original") bone <- BP_DetectBackground(bone=bone, analysis="logistic") bone <- BP_DetectForeground(bone=bone, analysis="logistic") bone <- BP_DetectCenters(bone=bone, analysis="logistic", method="Accurate") # Note that some parts of the section are concave but it does not give problems in the analysis # For section with very strong concavity, it is safer to use: # bone <- BP_DetectCenters(bone=bone, analysis="logistic", method="AccurateConvex") plot(bone, type="original") plot(bone, type="mineralized") plot(bone, type="unmineralized") plot(bone, type="section") plot(bone, type="colors") plot(bone, type="3Dcolors") bone <- BP_EstimateCompactness(bone, analysis="logistic", center="ontogenetic") plot(bone, type="original") plot(bone, type="mineralized") plot(bone, type="observations") bone <- BP_FitMLCompactness(bone, analysis="logistic") plot(bone, type="model", analysis=1) plot(bone, type="observations+model", analysis=1) fittedpar <- BP_GetFittedParameters(bone, analysis="logistic", type="global")[, "mean"] bone <- BP_DuplicateAnalysis(bone, from="logistic", to="flexit") bone <- BP_FitMLCompactness(bone, fitted.parameters=c(fittedpar, K1=1, K2=1), fixed.parameters=NULL, analysis="flexit") compare_AIC(Logistic=BP_GetFittedParameters(bone, analysis="logistic", ML=TRUE, return.all=TRUE), Flexit=BP_GetFittedParameters(bone, analysis="flexit", ML=TRUE, return.all=TRUE)) # pdf(file = "Figure 2.pdf", width = 8, height = 10, pointsize = 12) layout(1:2) plot(bone, type="observations+model", analysis="logistic", restorePar=FALSE, mar=c(4, 4, 2, 5)) plot(bone, type="observations+model", analysis="flexit", restorePar=FALSE, mar=c(4, 4, 2, 5)) layout(1) # dev.off() out4p <- plot(bone, type="observations+model", analysis="logistic") out6p <- plot(bone, type="observations+model", analysis="flexit") bone <- BP_FitBayesianCompactness(bone, analysis="logistic") plot(bone, type="observations+model", CI="MCMC") bone <- BP_FitBayesianCompactness(bone, analysis="flexit") plot(bone, type="observations+model", CI="MCMC", analysis="flexit") plot(bone, type="mcmc", parameter="P", options.mcmc=list(xlim=c(0.55, 0.57), breaks=seq(from=0, to=1, by=0.001))) plot(bone, type="mcmc", parameter="S", options.mcmc=list(xlim=c(0.02, 0.05), breaks=seq(from=0.02, to=.05, by=0.001))) plot(bone, type="mcmc", parameter="Min", options.mcmc=list(xlim=c(0.05, 0.08), breaks=seq(from=0, to=1, by=0.001))) plot(bone, type="mcmc", parameter="Max", options.mcmc=list(xlim=c(0.95, 0.97), breaks=seq(from=0, to=1, by=0.001))) outMCMC <- RM_get(x = bone, RM = "RM", RMname = "logistic", valuename = "mcmc") summary(outMCMC) outMCMC <- RM_get(x = bone, RM = "RM", RMname = "flexit", valuename = "mcmc") summary(outMCMC) # pdf(file = "Figure 3.pdf", width = 8, height = 10, pointsize = 12) layout(1:2) plot(bone, type="mcmc", parameter="K1", analysis="flexit", options.mcmc=list(xlim=c(-1, 3), ylim=c(0,10), breaks=seq(from=-1, to=3, by=0.001), legend = FALSE, show.prior = FALSE, mar=c(4, 4, 1, 6)), restorePar=FALSE) segments(x0=1, x1=1, y0=0, y1=10, lty=4, lwd=3) text(x=ScalePreviousPlot(x=0.95, y=0.95)$x, y=ScalePreviousPlot(x=0.95, y=0.95)$y, labels="A", cex=3) plot(bone, type="mcmc", parameter="K2", analysis="flexit", options.mcmc=list(xlim=c(-1, 3), ylim=c(0,10), breaks=seq(from=-1, to=3, by=0.001), legend = FALSE, show.prior = FALSE, mar=c(4, 4, 1, 6)), restorePar=FALSE) segments(x0=1, x1=1, y0=0, y1=10, lty=4, lwd=3) text(x=ScalePreviousPlot(x=0.95, y=0.95)$x, y=ScalePreviousPlot(x=0.95, y=0.95)$y, labels="B", cex=3) # dev.off() bone <- BP_FitMLRadialCompactness(bone, analysis = "flexit") plot(bone, type="radial", parameter.name=c("P", "S"), analysis = "flexit") plot(bone, type="radial", parameter.name=c("P", "S", "Min", "Max"), analysis = "flexit") out <- RM_get(x=bone, RMname="flexit", valuename = "optimRadial")$synthesis mean(out[, "P"]); sd(out[, "P"]) range(out[, "S"]) quantile(out[, "S"]) # pdf(file = "Figure 4.pdf", width=7, height = 9, pointsize = 12) layout(1:2) plot(bone, type="radial", parameter.name="P", analysis = "flexit", restorePar=FALSE) text(x=ScalePreviousPlot(x=0.95, y=0.95)$x, y=ScalePreviousPlot(x=0.95, y=0.95)$y, labels="A", cex=3) plot(bone, type="radial", parameter.name="S", analysis = "flexit", restorePar=FALSE) text(x=ScalePreviousPlot(x=0.95, y=0.95)$x, y=ScalePreviousPlot(x=0.95, y=0.95)$y, labels="B", cex=3) # dev.off() #' # How many times this package has been download library(cranlogs) BoneProfileR <- cran_downloads("BoneProfileR", from = "2021-10-07", to = Sys.Date() - 1) sum(BoneProfileR$count) plot(BoneProfileR$date, BoneProfileR$count, type="l", bty="n", xlab="Download date", ylab="Number of downloads") ## End(Not run) ``` ## References Girondot M, Laurin M (2003) Bone Profiler: a tool to quantify, model, and statistically compare bone-section compactness profiles. Journal of Vertebrate Paleontology 23: 458-461 Laurin M, Girondot M, Loth M-M (2004) The evolution of long bone microstructure and lifestyle in lissamphibians. Paleobiology 30: 589-613 Gônet, Jordan, Michel Laurin, and Marc Girondot. 2022. BoneProfileR: The Next Step to Quantify, Model and Statistically Compare Bone Section Compactness Profiles. Paleontologica Electronica. 25(1): a12 Gônet, J., Bardin, J., Girondot, M., Hutchinson, J., Laurin, M., (2023). Deciphering locomotion in reptiles: application of elliptic Fourier transforms to femoral microanatomy. Zoological Journal of the Linnean Society 198, 1070-1091. Gônet, J., Bardin, J., Girondot, M., Hutchinson, J.R., Laurin, M., (2023). Locomotor and postural diversity among reptiles viewed through the prism of femoral microanatomy: palaeobiological implications for some Permian and Mesozoic taxa. Journal of Anatomy 242, 891-916. Gônet, J., Bardin, J., Girondot, M., Hutchinson, J.R., Laurin, M., (2023). Unravelling the postural diversity of mammals: contribution of humeral cross-sections to palaeobiological inferences. Journal of Mamalian Evolution 30, 321-337. ## Author(s) Marc Girondot marc.girondot@gmail.com

BoneProfileR-package