Terrestrial laser scanning (TLS) data processing and post-hurricane damage severity classification at the individual tree level using deep Learning. Further details were published in Klauberg et al. (2023) <doi:10.3390/rs15041165>.

Native 'R' interface to 'TMB' (Template Model Builder) so models can be written entirely in 'R' rather than 'C++'. Automatic differentiation, to any order, is available for a rich subset of 'R' features, including linear algebra for dense and sparse matrices, complex arithmetic, Fast Fourier Transform, probability distributions and special functions. 'RTMB' provides easy access to model fitting and validation following the principles of Kristensen, K., Nielsen, A., Berg, C. W., Skaug, H., & Bell, B. M. (2016) <DOI:10.18637/jss.v070.i05> and Thygesen, U.H., Albertsen, C.M., Berg, C.W. et al. (2017) <DOI:10.1007/s10651-017-0372-4>.

Fit (exponential or diffusion) response-time extended multinomial processing tree (RT-MPT) models 
      by Klauer and	Kellen (2018) <doi:10.1016/j.jmp.2017.12.003> and Klauer, Hartmann, and Meyer-Grant (submitted). 
      The RT-MPT class not only incorporate	frequencies like traditional multinomial processing tree (MPT) models, 
      but also latencies. This enables it	to estimate process completion times and encoding plus motor execution times 
      next to the process probabilities	of traditional MPTs. 'rtmpt' is a hierarchical Bayesian framework and posterior 
      samples are sampled using a Metropolis-within-Gibbs sampler (for exponential RT-MPTs) or Hamiltonian-within-Gibbs 
      sampler (for diffusion RT-MPTs).

Quickly imports, processes, analyzes, and visualizes mass-spectrometric 
	data.  Includes functions for easily extracting specific data and measurements
	from large (multi-gigabyte) raw Bruker data files, as well as a set of S3 object
	classes for manipulating and measuring mass spectrometric peaks and plotting
	peaks and spectra using the 'ggplot2' package.

Truncated Newton function minimization with bounds constraints
	based on the 'Matlab'/'Octave' codes of Stephen Nash.