Implements various decision support tools related to the Econometrics & Technometrics.
             Subroutines include correlation reliability test, Mahalanobis distance measure for outlier detection, combinatorial search (all possible subset regression), non-parametric efficiency analysis measures: DDF (directional distance function), DEA (data envelopment analysis), HDF (hyperbolic distance function), SBM (slack-based measure), and SF (shortage function), benchmarking, Malmquist productivity analysis, risk analysis, technology adoption model, new product target setting, network DEA, dynamic DEA, intertemporal budgeting, etc.

Provides functions to facilitate access to the 'DKAN' API (<https://dkan.readthedocs.io/en/latest/apis/index.html>), including the 'DKAN' REST API (metadata), and the 'DKAN' datastore API (data). Includes functions to list, create, retrieve, update, and delete datasets and resources nodes. It also includes functions to search and retrieve data from the 'DKAN' datastore.

Provides time series regression models with one predictor using finite distributed lag models, polynomial (Almon) distributed lag models, geometric distributed lag models with Koyck transformation, and autoregressive distributed lag models. It also consists of functions for computation of h-step ahead forecasts from these models. See Demirhan (2020)(<doi:10.1371/journal.pone.0228812>) and Baltagi (2011)(<doi:10.1007/978-3-642-20059-5>) for more information.

An implementation of the differentiable lasso (dlasso) and SCAD (dSCAD) using iterative ridge algorithm. This package allows selecting the tuning parameter by AIC, BIC, GIC and GIC.

The Dirichlet Laplace shrinkage prior in Bayesian linear regression and variable selection, featuring:
    utility functions in implementing Dirichlet-Laplace priors such as visualization; 
    scalability in Bayesian linear regression; 
    penalized credible regions for variable selection.

The load estimation method is based on a general factor model to solve the estimates of load and specific variance. The philosophy of the package is described in Guangbao Guo. (2022). <doi:10.1007/s00180-022-01270-z>.