Dynamically retrieve data from the web to render HTML tables
    on inspection in R Markdown HTML documents.

Downloads and organizes datasets using BCB's API <https://www.bcb.gov.br/>. Offers options for caching with the 'memoise' package and
    , multicore/multisession with 'furrr' and format of output data (long/wide).

Provides functions that automate downloading and importing
    University of East Anglia Climate Research Unit ('CRU') 'CL' v. 2.0
    climatology data, facilitates the calculation of minimum temperature
    and maximum temperature and formats the data into a data.table object
    or a 'terra' 'SpatRaster' object.  'CRU' 'CL' v. 2.0 data are a
    gridded climatology of 1961-1990 monthly means released in 2002 and
    cover all land areas (excluding Antarctica) at 10 arc minutes
    (0.1666667 degree) resolution.  For more information see the
    description of the data provided by the University of East Anglia
    Climate Research Unit,
    <https://crudata.uea.ac.uk/cru/data/hrg/tmc/readme.txt>.

Since their introduction by Bose and Nair (1939) <https://www.jstor.org/stable/40383923>, partially balanced incomplete block (PBIB) designs remain an important class of incomplete block designs. The concept of association scheme was used by Bose and Shimamoto (1952) <doi:10.1080/01621459.1952.10501161> for the classification of these designs. The constraint of resources always motivates the experimenter to advance towards PBIB designs, more specifically to higher associate class PBIB designs from balanced incomplete block designs. It is interesting to note that many times higher associate PBIB designs perform better than their counterpart lower associate PBIB designs for the same set of parameters v, b, r, k and lambda_i (i=1,2...m). This package contains functions named GETD() for generating m-associate (m>=2) class PBIB designs along with parameters (v, b, r, k and lambda_i, i = 1, 2,…,m) based on Generalized Triangular (GT) Association Scheme. It also calculates the Information matrix, Average variance factor and canonical efficiency factor of the generated design. These designs, besides having good efficiency, require smaller number of replications and smallest possible concurrence of treatment pairs.