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Momocs <a href="http://momx.github.io/Momocs/"><img src="man/figures/logo.png" align="right" height="139" alt="Momocs website" /></a>

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News

• Momocs is now retiring and, one day, will no longer be supported in favour of Momocs2 and the entire MomX ecosystem. (as of Jan 26)
• I’m available for consulting, training and collaboration, worldwide.
• Momocs is back on CRAN and no longer relies on the retired rgeos dependency
• The tutorial/introduction is back! Download it there

Installation

The last released version can be installed from CRAN with:

install.packages("Momocs")

But I recommend using (and only support) the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("MomX/Momocs")

<!-- ## Features __Matrices of xy-coordinates__ * ~100 generic tools like centering, scaling, rotating, calculating area, perimeter, etc. Full list with `apropos("coo_")` * generic plotters: `coo_plot` and `g` (work in progress) &#10;__Data acquisition + Babel__ &#10;* Outline extraction from black mask/silhouettes `.jpgs` * Landmark definition on outlines (`def_ldk` or via [StereoMorph](https://github.com/aaronolsen/StereoMorph)) * Open curves digitization with bezier curves (via [StereoMorph](https://github.com/aaronolsen/StereoMorph)) * Import/Export from/to `.nts`, `.tps`, `PAST`, `.txt`, etc. &#10;__Outline analysis__ &#10;* Elliptical Fourier analysis (`efourier`) * Radii variation (`rfourier`) * Radii variation - curvilinear abscissa (`sfourier`) * Tangent Angle Fourier analysis (`tfourier`) &#10;__Open-outlines__ &#10;* Natural (raw) polynomials (`npoly`) * Orthogonal (Legendre) polynomials (`opoly`) * Discrete Cosinus Transform (`dfourier`) * `bezier` core functions &#10;__Configuration of landmarks__ &#10;* Full Generalized Procrustes Adjustment (`fgProcrustes`) * Sliding semi-landmarks (`fgsProcrustes`) &#10;__Traditional morphometrics and global shape descriptors__ &#10;* Facilities for multivariate analysis (see `flowers`) * A long list of shape scalars (eg. `coo_eccentricity`, `coo_rectilinearity`, etc.) &#10;__Data handling__ &#10;* Easy data manipulation with `filter`, `select`, `slice`, `mutate` and other verbs ala [dplyr](https://github.com/hadley/dplyr/) * New verbs useful for morphometrics such as `combine` and `chop`, to handle several 2D views * Permutation methods to resample data (`perm`, `breed`) &#10;__Multivariate analysis__ &#10;* Mean shape (groupwise) calculations (`mshapes`) * Principal component analysis (`PCA`) * Multivariate analysis of variance (`MANOVA` + pairwise testing `MANOVA_PW`) * Linear discriminant analysis and screening (`LDA`) * Hierarchical clustering (`CLUST`) * K-means (`KMEANS`) &#10;__Graphical methods__ &#10;* Family pictures and quick inspection of whole datasets (`stack` and `panel`) * Some `ggplot2` plots, when useful (and convet Momocs' objects into `data.frames it with `as_df`) * Morphological spaces for PCA * Thin plate splines and variation around deformation grids &#10; __Misc__ &#10;* Datasets for all types of data (`apodemus`, `bot`, `chaff`, `charring`, `flower`, `hearts`, `molars`, `mosquito`, `mouse`, `oak`, `olea`, `shapes`, `trilo`, `wings`) * [Shiny](http://shiny.rstudio.com/) demonstrators/helpers. See [Momecs](https://github.com/vbonhomme/Momecs/) * [Online documentation](http://vbonhomme.github.io/Momocs/) -->

Example

This is a basic example of a complete analysis doing: inspection, normalization of raw outlines, elliptical Fourier transforms, dimmensionality reduction and classification, using a single line.

library(Momocs)

devtools::load_all()
#> ℹ Loading Momocs
#> Registered S3 method overwritten by 'vegan':
#>   method     from      
#>   rev.hclust dendextend

hearts %T>%                    # A toy dataset
  stack() %>%                  # Take a family picture of raw outlines
  fgProcrustes() %>%           # Full generalized Procrustes alignment
  coo_slide(ldk = 2) %T>%      # Redefine a robust 1st point between the cheeks
  stack() %>%                  # Another picture of aligned outlines
  efourier(6, norm=FALSE) %>%  # Elliptical Fourier Transforms
  PCA() %T>%                   # Principal Component Analysis
  plot_PCA(~aut) %>%           # A PC1:2 plot
  LDA(~aut) %>%                # Linear Discriminant Analysis
  plot_CV()                    # And the confusion matrix after leave one out cross validation

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