An intuitive and user-friendly package designed to aid undergraduate students in understanding and applying econometric methods in their studies,
    Tailored specifically for Econometrics and Regression Modeling courses, it provides a practical toolkit for modeling and analyzing econometric data with detailed inference capabilities.

Production efficiency and economic efficiency are crucial concepts in agriculture/horticulture for sustainable and profitable practices. It helps to determine the optimal use of resources to maximize outputs and profitability. Production efficiency focuses on the optimal use of resources to produce goods, while economic efficiency ensures these goods are produced and allocated in a way that maximizes economic welfare. Production efficiency and economic efficiency are calculated with the help of the formula given by (Kumar et al., 2017) <doi:10.21921/jas.v4i04.10202>.

Implements three complementary pipelines for causal analysis on macroeconomic time series:
    (1) Error-Correction Models with Multivariate Adaptive Regression Splines (ECM-MARS),
    (2) Bayesian Structural Time Series (BSTS), and 
    (3) Bayesian GLM with AR(1) errors validated with Leave-Future-Out (LFO). 
    Heavy backends (Stan) are optional and never used in examples or tests.

The 'EconDataverse' is a universe of open-source packages to work 
    seamlessly with economic data. This package is designed to make it easy to 
    install and load multiple 'EconDataverse' packages in a single step. Learn
    more about the 'EconDataverse' at <https://www.econdataverse.org>.

Tools for general properties including price, quantity, elasticity, convexity, marginal revenue and manifold of various economics demand systems including Linear, Translog, CES, LES and CREMR.

Provides methods for estimating parameter-dependent network centrality measures with linear-in-means models. Both non linear least squares and maximum likelihood estimators are implemented. The methods allow for both link and node heterogeneity in network effects, endogenous network formation and the presence of unconnected nodes. The routines also compare the explanatory power of parameter-dependent network centrality measures with those of standard measures of network centrality. Benefits and features of the 'econet' package are illustrated using data from Battaglini and Patacchini (2018) and Battaglini, Patacchini, and Leone Sciabolazza (2020). For additional details, see the vignette <doi:10.18637/jss.v102.i08>.