Tools for automatic model selection and diagnostics for Climate and Environmental data.  In particular the envcpt() function does automatic model selection between a variety of trend, changepoint and autocorrelation models.  The envcpt() function should be your first port of call.

Prints out information about the R working environment
    (system, R version,loaded and attached packages and versions) from a single
    function "env_doc()".  Optionally adds information on git repository,
    tags, commits and remotes (if available).

A collection of functions for microbial ecology and other
   applications of genomics and metagenomics. Companion package for the
   Enveomics Collection (Rodriguez-R, L.M. and Konstantinidis, K.T., 2016
   <DOI:10.7287/peerj.preprints.1900v1>).

Estimates an ecological niche using occurrence data, covariates, and kernel
        density-based estimation methods. For a single species with presence and absence data,
        the 'envi' package uses the spatial relative risk function that is estimated using the
        'sparr' package. Details about the 'sparr' package methods can be found in the tutorial:
        Davies et al. (2018) <doi:10.1002/sim.7577>. Details about kernel density estimation can
        be found in J. F. Bithell (1990) <doi:10.1002/sim.4780090616>. More information about
        relative risk functions using kernel density estimation can be found in J. F. Bithell
        (1991) <doi:10.1002/sim.4780101112>.

Gas/Liquid Chromatography-Mass Spectrometer(GC/LC-MS) Data Analysis for Environmental Science. This package covered topics such molecular isotope ratio, matrix effects and Short-Chain Chlorinated Paraffins analysis etc. in environmental analysis.

Fast and very memory-efficient calculation of isotope patterns,
    subsequent convolution to theoretical envelopes (profiles) plus valley
    detection and centroidization or intensoid calculation. Batch processing,
    resolution interpolation, wrapper, adduct calculations and molecular
    formula parsing.
	Loos, M., Gerber, C., Corona, F., Hollender, J., Singer, H. (2015) 
	<doi:10.1021/acs.analchem.5b00941>.