The CLIC Package

Collecting Landmarks for Identification and Characterization (CLIC)

– Linux (32 and 64 bits, but please boot from within midnight- or gnome-commander),

– Windows (XP, W7, W8, … 32 and 64 bits)

– and Apple (Yosemite).

One download to rule them all… :)

Download the CLIC package!
CLIC_99.tgz 91.6 MB 11/30/2017 8:31 am
CLIC_LEAME.txt 2.7 KB 7/29/2014 2:46 am
CLIC_README.txt 3.0 KB 7/29/2014 2:47 am
First help (o-help): what does each CLIC module do?
asi_start.txt 3.6 KB 8/19/2015 7:51 pm
coo_start.txt 7.4 KB 8/19/2015 7:51 pm
cov_start.txt 3.0 KB 8/19/2015 7:52 pm
fog_start.txt 3.0 KB 8/19/2015 7:52 pm
mog_start.txt 2.2 KB 8/19/2015 7:52 pm
pad_start.txt 1.8 KB 8/19/2015 7:52 pm
tet_start.txt 2.5 KB 8/19/2015 7:52 pm
var_start.txt 1.5 KB 8/19/2015 7:52 pm
HELP ? see the ‘…_start.txt’ files above. One by module. The CLIC package now contains the following modules: COO (see coo_start.txt), MOG (see mog_start.txt), FOG (see fog_start.txt), TET (see tet_start.txt above),PAD (see pad_start.txt), COV (see coo_start.txt), VAR and ASI (see asi_start.txt). It does not contain the BAC module, not maintained anymore.Update the CLIC package faster!

Modules having a more recent date than the one of the big CLIC folder should replace the current ones.

Rhelp_97-7.tar 5.4 MB 5/27/2015 6:46 am
ohelp.tar 40.0 KB 5/27/2015 6:47 am

The above .tar files are FOLDERS, they are the separated MODULES of the last version of CLIC (except for R_help_97-7.tar and ohelp.tar). If you want (need) to update your CLIC (>70MB), you could just decide to update only one or two modules. MODULES here above (if any) are FOLDERS (with names like COO.tar, TET.tar, MOG.tar etc.). Please download the folder MODULE.tar you want to update, and after unzipping replace your old folder module by the new one.

Modules having a more recent date than the one of the big CLIC folder should replace the current ones.

Please note that the SEPARATE MODULES DO NOT WORK IF NOT located INSIDE THE CLIC_[version number] FOLDER

Help videos about CLIC (screen recording)

lmsl.mp4 12.2 MB 8/6/2015 10:48 am
lmsl_droso.mp4 8.4 MB 2/5/2016 7:29 pm
scalingForSize.mp4 1.5 MB 11/4/2017 4:32 pm
sizescale.mp4 6.1 MB 8/6/2015 10:48 am

lmsl.mp4 and lmsl_droso.mp4 about using COO to collect LANDMAKS and SEMILANDMARKS

sizescaling.mp4 is about scaling the image (in case images have been captured at different magnification levels)

A few help lines for you hereunder in case you want to use the CLIC package as a tool for cryptic species recognition. Thus, in addition to the reference images (CLIC bank), you will only need two modules of the CLIC package: COO and MOG (or FOG). COO helps you to collect landmarks, semilandmarks, or to capture outlines, MOG performs the relevant analyses for landmark based data (FOG performs an EFA on outlines).

  • Collecting landmarks (button COO)
    Only reference images are downloaded, not the coordinates of their landmarks (see BMC Research Note). You have to select a set of LM and digitize them. You have to do that on the reference images and, separately, on the images of your own specimens.
  • Obtaining shape variables (button MOG).
  • Identifying “unknown” specimens (MOGmodule)
    • Procrustes classification. After the partial warps (PW) have been computed on your input data, a new button appears (EXT/UNK) which allows you to enter external data in the same format (…_format.txt) as the input data. Then a first classification of your external, unknown specimens will be automatically performed on the basis of pairwise Procrustes distances. This sequence is chronological, not logical since Procrustes distances computation does not need the previous transformation of residuals into PW. The Procrustes classification will use two algorithms, one based on the shortest Procrustes distance to each consensus (each unknown with each consensus), and another one based on the K nearest neighbors method (KNN, each unknown with each reference image).
    • Mahalanobis classification. After the Procrustes classification of the unknown individual(s), you can ask for a Classification based on discriminant analysis (DA). You will not have the choice of any input or external file. As many times as there are external individuals, the MOG “Mahalanobis classification” successively recreates situations where only one individual has to be classified.
    • This is called the “one-by-one” procedure:
      (1) for each individual classification, the PW will be recomputed from the raw reference data and the raw coordinates of the single individual to be classified,
      (2) the Mahalanobis distances will be estimated from these PW, and classified as for a KNN analysis.
      (3) If the reference data have small groups, the set of a few first RW is used as input data instead of the totality of the PW.
    • If you have many unknown individuals, the “one-by-one” procedure can be slow but it is considered as essential for an optimal shape-based classification (see BMC Research Note ).


The use of the reference images and the publishing of related results imply your obligation to cite the paper associated with each set of reference images.
The software is free software, under GPL license.