Collection of datasets from the ASA 2006 data expo
This package contains tidied data from the ASA 2006 data expo,
as well as a number of useful other related data sets.

c(person(given = "Bahrul Ilmi",      family = "Nasution",    role = c("aut", "cre"), email = c("bahrulnst@gmail.com","14.8034@stis.ac.id")),
person(given = "Robert",      family = "Kurniawan",    role = "aut", email = "robertk@stis.ac.id"),
person(given = "Rezzy Eko",      family = "Caraka",    role = "aut", email = "rezzyekocaraka@gmail.com"))
Bahrul Ilmi Nasution [aut, cre],
Robert Kurniawan [aut],
Rezzy Eko Caraka [aut]
Implement and enhance the performance of spatial fuzzy clustering using Fuzzy Geographically Weighted Clustering with various optimization algorithms, mainly from Xin She Yang (2014) <ISBN:9780124167438> with book entitled Nature-Inspired Optimization Algorithms. The optimization algorithm is useful to tackle the disadvantages of clustering inconsistency when using the traditional approach. The distance measurements option is also provided in order to increase the quality of clustering results. The Fuzzy Geographically Weighted Clustering with nature inspired optimisation algorithm was firstly developed by Arie Wahyu Wijayanto and Ayu Purwarianti (2014) <doi:10.1109/CITSM.2014.7042178> using Artificial Bee Colony algorithm.

nat: NeuroAnatomy Toolbox for Analysis of 3D Image Data
Greg Jefferis with contributions from Ashley Manton
This package is a reboot of the AnalysisSuite
(https://github.com/jefferis/AnalysisSuite) bundle of R code that I have
been using for a number of years to analyse and visualise 3D biological
image data, especially traced neurons. AnalysisSuite is most easily
installed using the nat.as package presently available at
https://github.com/jefferis/nat.as. The nat package is considerably cleaner
but currently provides only a subset of the full functionality of nat.as /
AnalysisSuite. nat can read 3D images and surfaces in Amira formats. Traced
neurons can be imported from SWC and Amira SkeletonGraph format. These data
can then be visualised in 3D via rgl, manipulated including applying
calculated registrations, e.g. using the CMTK registration suite, and
analysed. There package also has a simple representation for neurons that
have been subjected to a 3D skeletonisation but not formally traced; this
allows morphological comparison between neurons including searches and
clustering.