Lake temperature records, metadata, and climate drivers for 291 global lakes during the time period 1985-2009. Temperature observations were collected using satellite and in situ methods. Climatic drivers and geomorphometric characteristics were also compiled and are included for each lake. Data are part of the associated publication from the Global Lake Temperature Collaboration project (http://www.laketemperature.org). See citation('laketemps') for dataset attribution.

Determining consensus seriations for binary incidence matrices, using a two-step process of Procrustes-fit correspondence analysis for heuristic selection of partial seriations and iterative regression to establish a single consensus. Contains the Lakhesis Calculator, a graphical platform for identifying seriated sequences. Collins-Elliott (2024) <https://volweb.utk.edu/~scolli46/sceLakhesis.pdf>.

Includes some procedures for latent variable modeling with a 
    particular focus on multilevel data.
    The 'LAM' package contains mean and covariance structure modelling
    for multivariate normally distributed data (mlnormal(); Longford, 1987;
    <doi:10.1093/biomet/74.4.817>), a general Metropolis-Hastings algorithm 
    (amh(); Roberts & Rosenthal, 2001, <doi:10.1214/ss/1015346320>) and 
    penalized maximum likelihood estimation (pmle(); Cole, Chu & Greenland, 
    2014; <doi:10.1093/aje/kwt245>).

A variety of latent Markov models, including hidden Markov models, hidden semi-Markov models, 
  state-space models and continuous-time variants can be formulated and estimated within the same framework via directly maximising the likelihood function using the so-called forward algorithm. 
  Applied researchers often need custom models that standard software does not easily support. 
  Writing tailored 'R' code offers flexibility but suffers from slow estimation speeds. 
  We address these issues by providing easy-to-use functions (written in 'C++' for speed) for common tasks like the forward algorithm. 
  These functions can be combined into custom models in a Lego-type approach, offering up to 10-20 times faster estimation via standard numerical optimisers. 
  To aid in building fully custom likelihood functions, several vignettes are included that show how to simulate data from and estimate all the above model classes.