Implements projection pursuit forest algorithm for supervised classification.

Methods for fitting point processes with parameters of generalised additive 
      model (GAM) form are provided. For an introduction to point processes see Cox, D.R & 
      Isham, V. (Point Processes, 1980, CRC Press), GAMs see Wood, S.N. (2017) 
      <doi:10.1201/9781315370279>, and the fitting approach see Wood, S.N., Pya, N. & 
      Safken, B. (2016) <doi:10.1080/01621459.2016.1180986>.

Reconstruction of paleoclimate niches using phylogenetic comparative 
    methods and projection reconstructed niches onto paleoclimate maps.
    The user can specify various models of trait evolution or estimate the best fit
    model, include fossils, use one or multiple phylogenies for inference, and make
    animations of shifting suitable habitat through time. This model was first used
    in Lawing and Polly (2011), and further implemented in Lawing et al (2016) and 
    Rivera et al (2020).
    Lawing and Polly (2011) <doi:10.1371/journal.pone.0028554> "Pleistocene climate,
    phylogeny and climate envelope models: An integrative approach to better 
    understand species' response to climate change"
    Lawing et al (2016) <doi:10.1086/687202> "Including fossils in phylogenetic 
    climate reconstructions: A deep time perspective on the climatic niche evolution
    and diversification of spiny lizards (Sceloporus)"
    Rivera et al (2020) <doi:10.1111/jbi.13915> "Reconstructing historical shifts 
    in suitable habitat of Sceloporus lineages using phylogenetic niche modelling.".

Projection Pursuit (PP) algorithm for dimension reduction based on Gaussian Mixture Models (GMMs) for density estimation using Genetic Algorithms (GAs) to maximise an approximated negentropy index. For more details see Scrucca and Serafini (2019) <doi:10.1080/10618600.2019.1598871>.

The Poverty Probability Index (PPI) is a poverty measurement tool 
    for organizations and businesses with a mission to serve the poor. The PPI 
    is statistically-sound, yet simple to use: the answers to 10 questions about 
    a household’s characteristics and asset ownership are scored to compute the 
    likelihood that the household is living below the poverty line – or above by 
    only a narrow margin. This package contains country-specific lookup data tables
    used as reference to determine the poverty likelihood of a household based
    on their score from the country-specific PPI questionnaire. These lookup 
    tables have been extracted from documentation of the PPI found at 
    <https://www.povertyindex.org> and managed by Innovations for Poverty Action 
    <https://poverty-action.org/>.