Siyu Han [aut, cre], Jialing Huang [aut], Francesco Foppiano [aut], Cornelia Prehn [aut], Jerzy Adamski [aut], Karsten Suhre [aut], Ying Li [aut], Giuseppe Matullo [aut], Freimut Schliess [aut], Christian Gieger [aut], Annette Peters [aut], Rui Wang-Sattler [aut]
The R implementation of TIGER.
TIGER integrates random forest algorithm into an innovative ensemble learning architecture. Benefiting from this advanced architecture, TIGER is resilient to outliers, free from model tuning and less likely to be affected by specific hyperparameters.
TIGER supports targeted and untargeted metabolomics data and is competent to perform both intra- and inter-batch technical variation removal. TIGER can also be used for cross-kit adjustment to ensure data obtained from different analytical assays can be effectively combined and compared.
Reference: Han S. et al. (2022) <doi:10.1093/bib/bbab535>.

a problem solving environment for fitting separable nonlinear
models in physics and chemistry applications
Katharine M. Mullen, Joris J. Snellenburg, Sergey Laptenok,
David Nicolaides, Ivo H.M. van Stokkum
R (>= 2.7.0), methods, tcltk, vcd, fields (>= 4.1), gplots,
splines, gclus, nnls (>= 1.1), odesolve, minpack.lm (>= 1.1-1)
TIMP is a problem solving environment for fitting
separable nonlinear models to measurements arising in physics
and chemistry experiments, and has been extensively applied to
time-resolved spectroscopy and FLIM-FRET data.

R (>= 2.10.0), methods, tcltk, vcd, fields (>= 4.1), gplots,
splines, gclus, nnls (>= 1.1), deSolve, minpack.lm (>= 1.1-1)

Katharine M. Mullen, Joris J. Snellenburg, Sergey Laptenok, David Nicolaides, Ivo H.M. van Stokkum
vcd, gplots, colorspace, splines, gclus, nnls (>= 1.1),
deSolve, minpack.lm (>= 1.1-1), MASS

Katharine M. Mullen, Sergey Laptenok, David Nicolaides, Ivo H.
M. van Stokkum

Katharine M. Mullen, Joris J. Snellenburg, Sergey P. Laptenok, David Nicolaides, Ivo H.M. van Stokkum

Fitting Seperable Nonlinear Models in Spectroscopy and
Microscopy
TIMP is a problem-solving environment (PSE) for fitting
separable nonlinear models to measurements arising in physics
and chemistry experiments, and has been extensively applied to
time-resolved spectroscopy and FLIM-FRET data.