Wrapper functions for the implementation of lagged weighted quantile sum regression, as per 'Gennings et al' (2020) <doi:10.1016/j.envres.2020.109529>.

An implementation of algorithms described in Jewell and Witten (2017) <arXiv:1703.08644>.

Provide access to  the 'lz-string' 
    <http://pieroxy.net/blog/pages/lz-string/index.html> 'C++' library for 
    Lempel-Ziv (LZ) based compression and decompression of strings.

Prediction of behaviour from movement 
	characteristics using observation and random forest for the analyses of movement
	data in ecology.
	From movement information (speed, bearing...) the model predicts the
	observed behaviour (movement, foraging...) using random forest. The
	model can then extrapolate behavioural information to movement data
	without direct observation of behaviours.
	The specificity of this method relies on the derivation of multiple predictor variables from the
	movement data over a range of temporal windows. This procedure allows to capture
	as much information as possible on the changes and variations of movement and
	ensures the use of the random forest algorithm to its best capacity. The method
	is very generic, applicable to any set of data providing movement data together with
	observation of behaviour.

Persistent interface to 'Macaulay2' <https://www.macaulay2.com>
    and front-end tools facilitating its use in the 'R' ecosystem. For details see 
    Kahle et. al. (2020) <doi:10.18637/jss.v093.i09>.

A new method to implement clustering from multiple modality data of certain samples, 
    the function M2SMF() jointly factorizes multiple similarity matrices into a shared sub-matrix 
    and several modality private sub-matrices, which is further used for clustering. Along with 
    this method, we also provide function to calculate the similarity matrix and function to 
    evaluate the best cluster number from the original data.

A new method to implement clustering from multiple modality data of certain samples, 
    the function M2SMjF() jointly factorizes multiple similarity matrices into a shared sub-matrix 
    and several modality private sub-matrices, which is further used for clustering. Along with 
    this method, we also provide function to calculate the similarity matrix and function to 
    evaluate the best cluster number from the original data.