Many clustering methods have been proposed, but
    most of them cannot work for longitudinal gene expression data. 
    'BClustLonG' is a package that allows us to perform clustering analysis for 
    longitudinal gene expression data. It adopts a linear-mixed effects framework 
    to model the trajectory of genes over time, while clustering is jointly 
    conducted based on the regression coefficients obtained from all genes. 
    To account for the correlations among genes and alleviate the 
    high dimensionality challenges, factor analysis models are adopted
    for the regression coefficients. The Dirichlet process prior distribution 
    is utilized for the means of the regression coefficients to induce clustering.
    This package allows users to specify which variables to use for clustering 
    (intercepts or slopes or both) and whether a factor analysis model is desired.
    More details about this method can be found in Jiehuan Sun, et al. (2017) <doi:10.1002/sim.7374>.

Various layers of B.C., including administrative boundaries,
    natural resource management boundaries, census boundaries etc. All
    layers are available in BC Albers
    (<https://spatialreference.org/ref/epsg/3005/>) equal-area projection,
    which is the B.C. government standard. The layers are sourced from the
    British Columbia and Canadian government under open licenses,
    including B.C. Data Catalogue (<https://data.gov.bc.ca>), the
    Government of Canada Open Data Portal
    (<https://open.canada.ca/en/using-open-data>), and Statistics Canada
    (<https://www.statcan.gc.ca/en/reference/licence>).

Inference on the marginal model of the mixed effect model with 
    the Box-Cox transformation and on the model median differences between
    treatment groups for longitudinal randomized clinical trials. These 
    statistical methods are proposed by Maruo et al. (2017) 
    <doi:10.1002/sim.7279>.

The Behavioral Change Point Analysis (BCPA) is a method of
    identifying hidden shifts in the underlying parameters of a time series,
    developed specifically to be applied to animal movement data which is
    irregularly sampled.  The method is based on: E. Gurarie, R. Andrews and 
    K. Laidre A novel method for identifying behavioural changes in animal 
    movement data (2009) Ecology Letters 12:5 395-408. A development version is 
    on <https://github.com/EliGurarie/bcpa>. NOTE: the BCPA method may be useful 
    for any univariate, irregularly sampled Gaussian time-series, but animal 
    movement analysts are encouraged to apply correlated velocity change point 
    analysis as implemented in the smoove package, as of this writing on GitHub 
    at <https://github.com/EliGurarie/smoove>. An example of a univariate analysis
    is provided in the UnivariateBCPA vignette.