Geographically referenced data and statistics of nighttime lights from NASA Black Marble <https://blackmarble.gsfc.nasa.gov/>.

Can be used to read and write a fwf with an accompanying 'Blaise' datamodel.
    Blaise is the software suite built by Statistics Netherlands (CBS). It is essentially a 
    way to write and collect surveys and perform statistical analysis on the data. It stores its data in 
    fixed width format with an accompanying metadata file, this is the Blaise format. The package automatically 
    interprets this metadata and reads the file into an R dataframe.
    When supplying a datamodel for writing, the dataframe will be automatically converted 
    to that format and checked for compatibility.
    Supports dataframes, tibbles and LaF objects.
    For more information about 'Blaise', see <https://blaise.com/products/general-information>.

Fast and accurate calculation of Blaker's binomial and Poisson confidence limits (and some related stuff).

Bland-Altman Plots using either base graphics or ggplot2,
    augmented with confidence intervals, with detailed return values and
    a sunflowerplot option for data with ties.

Carries out Bland Altman analyses (also known as a Tukey
    mean-difference plot) as described by JM Bland and DG Altman in
    1986 <doi:10.1016/S0140-6736(86)90837-8>. This package was created in 
    2015 as existing Bland-Altman analysis functions did not calculate 
    confidence intervals. This package was created to rectify this, 
    and create reproducible plots. This package is also available as a module
    for the 'jamovi' statistical spreadsheet (see <https://www.jamovi.org>
    for more information).

Laplace approximations and penalized B-splines are combined
    for fast Bayesian inference in latent Gaussian models. The routines can be
    used to fit survival models, especially proportional hazards and promotion 
    time cure models (Gressani, O. and Lambert, P. (2018) 
    <doi:10.1016/j.csda.2018.02.007>). The Laplace-P-spline methodology can also
    be implemented for inference in (generalized) additive models
    (Gressani, O. and Lambert, P. (2021) <doi:10.1016/j.csda.2020.107088>).
    See the associated website for more information and examples.