Estimate the linear and nonlinear autoregressive distributed lag (ARDL & NARDL) models and the corresponding error correction models, and test for longrun and short-run asymmetric. The general-to-specific approach is also available in estimating the ARDL and NARDL models. The Pesaran, Shin & Smith (2001) (<doi:10.1002/jae.616>) bounds test for level relationships is also provided. The 'ardl.nardl' package also performs short-run and longrun symmetric restrictions available at Shin et al. (2014) <doi:10.1007/978-1-4899-8008-3_9> and their corresponding tests.

Contains functions to help create an Analysis Results Dataset.
    The dataset follows industry recommended structure.  The dataset can be 
    created in multiple passes, using different data frames as input.  Analysis
    Results Datasets are used in the pharmaceutical and biotech industries
    to capture analysis in a common tabular data structure.

Calculate the area of triangles and polygons using the shoelace 
 formula. Area may be signed, taking into account path orientation, or unsigned, 
 ignoring path orientation. The shoelace formula is described at 
 <https://en.wikipedia.org/wiki/Shoelace_formula>.

Considering an (n x m) data matrix X, this package is based on the method proposed 
             by Gower, Groener, and Velden (2010) <doi:10.1198/jcgs.2010.07134>, and 
             utilize the  resulting matrices from the extended version of the NIPALS decomposition 
             to determine n triangles whose areas are used  to  visually  estimate the elements of
             a specific column of X. After a 90-degree rotation of the sample points, the triangles
             are drawn regarding the following points: 1.the origin of the axes; 2.the sample points;
             3. the vector endpoint representing some variable.

A pipeable, transparent implementation of areal weighted interpolation
    with support for interpolating multiple variables in a single function call.
    These tools provide a full-featured workflow for validation and estimation
    that fits into both modern data management (e.g. tidyverse) and spatial 
    data (e.g. sf) frameworks.