Proxy forward modelling for sediment archived climate proxies such 
  as Mg/Ca, d18O or Alkenones. The user provides a hypothesised "true" past climate, 
  such as output from a climate model, and details of the sedimentation rate and 
  sampling scheme of a sediment core. Sedproxy returns simulated proxy records. 
  Implements the methods described in Dolman and Laepple (2018) 
  <doi:10.5194/cp-14-1851-2018>.

Provides plotting utilities supporting packages in the 'easystats'
    ecosystem (<https://github.com/easystats/easystats>) and some extra themes,
    geoms, and scales for 'ggplot2'. Color scales are based on
    <https://materialui.co/>.
    References: Lüdecke et al. (2021) <doi:10.21105/joss.03393>.

Output colors used in literal vectors, palettes and plot objects (ggplot).

Functions for dimension reduction through the seeded canonical correlation analysis are provided. A classical canonical correlation analysis (CCA) is one of useful statistical methods in multivariate data analysis, but it is limited in use due to the matrix inversion for large p small n data. To overcome this, a seeded CCA has been proposed in Im, Gang and Yoo (2015) \doi{10.1002/cem.2691}. The seeded CCA is a two-step procedure. The sets of variables are initially reduced by successively projecting cov(X,Y) or cov(Y,X) onto cov(X) and cov(Y), respectively, without loss of information on canonical correlation analysis, following Cook, Li and Chiaromonte (2007) \doi{10.1093/biomet/asm038} and Lee and Yoo (2014) \doi{10.1111/anzs.12057}. Then, the canonical correlation is finalized with the initially-reduced two sets of variables.

Seeded Sequential LDA can classify sentences of texts into pre-define topics with a small number of seed words (Watanabe & Baturo, 2023) <doi:10.1177/08944393231178605>.
    Implements Seeded LDA (Lu et al., 2010) <doi:10.1109/ICDMW.2011.125> and Sequential LDA (Du et al., 2012) <doi:10.1007/s10115-011-0425-1> with the distributed LDA algorithm (Newman, et al., 2009) for parallel computing.