Theme and colour palettes for The Globe and Mail's graphics. Includes colour and fill scale functions, colour palette helpers and a Globe-styled 'ggplot2' theme object.

Deconvolving thermoluminescence glow curves according to various 
        kinetic models (first-order, second-order, general-order, and mixed-order) using 
        a modified Levenberg-Marquardt algorithm (More, 1978) <DOI:10.1007/BFb0067700>. 
        It provides the possibility of setting constraints or fixing any of parameters. 
        It offers an interactive way to initialize parameters by clicking with a mouse on 
        a plot at positions where peak maxima should be located. The optimal estimate is 
        obtained by "trial-and-error". It also provides routines for simulating first-order, 
        second-order, and general-order glow peaks.

Density, distribution function, quantile function and random generation for the Truncated Generalised Gamma Distribution (also in log10(x) and ln(x) space).

Efficient implementation of K-Means++ algorithm. For more
    information see (1) "kmeans++ the advantages of the k-means++
    algorithm" by David Arthur and Sergei Vassilvitskii (2007),
    Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete
    algorithms, Society for Industrial and Applied Mathematics,
    Philadelphia, PA, USA, pp. 1027-1035, and (2) "The Effectiveness of
    Lloyd-Type Methods for the k-Means Problem" by Rafail Ostrovsky, Yuval
    Rabani, Leonard J. Schulman and Chaitanya Swamy
    <doi:10.1145/2395116.2395117>.

Bayesian nonstationary, semiparametric nonlinear regression 
 and design by treed Gaussian processes (GPs) with jumps to the limiting 
 linear model (LLM).  Special cases also implemented include Bayesian 
 linear models, CART, treed linear models, stationary separable and 
 isotropic GPs, and GP single-index models.  Provides 1-d and 2-d plotting functions 
 (with projection and slice capabilities) and tree drawing, designed for 
 visualization of tgp-class output.  Sensitivity analysis and 
 multi-resolution models are supported. Sequential experimental 
 design and adaptive sampling functions are also provided, including ALM, 
 ALC, and expected improvement.  The latter supports derivative-free
 optimization of noisy black-box functions.  For details and tutorials, 
 see Gramacy (2007) <doi:10.18637/jss.v019.i09> and Gramacy & Taddy (2010) 
 <doi:10.18637/jss.v033.i06>.