Researches under classical statistics often relies on precise, determinate data to estimate population parameters. However, in certain situations, data may be indeterminate or imprecise. Neutrosophic statistics, a generalization of classical statistics, has been introduced to address these challenges by handling vague, indeterminate, and uncertain information effectively. Several estimators, including ratio estimators, have been proposed in neutrosophic statistics. These ratio estimators perform well when the correlation between the auxiliary and study variables is strong. However, in this study, regression-type estimators were developed, demonstrating superior performance in cases where the correlation between the study and auxiliary variables is high, weak, or moderate.The R package is designed for neutrosophic regression type estimator to estimate the Finite Population Parameters. This package provides three different function i.e. compute_all_metrics, inputs and calculate_all_mse_neutrosophic. It provide neutrosophic descriptive statistics. Users can input values for population size as well as sample size for neutrosophic population at run time . In this package we can obtain the mse value for neutrosophic ratio-type estimators, neutrosophic exponential ratio-type estimator and neutrosophic regression type estimator.

Performance metrics are essential tools for evaluating the accuracy and effectiveness of predictive models. These metrics provide quantifiable measures to assess how well a model’s predictions match the actual outcomes.

Application of Empirical Mode Decomposition based Support Vector Regression model

The Quadroot function helps the user to find simple quadratic roots from any quadratic equation.

The researchers can use this package to fit Empirical Mode Decomposition and Artificial Neural Network based hybrid model for nonlinear and non stationary time series da

The VMDML R package is designed for application of Variational Mode Decomposition based different Machine Learning models for univariate time series forecasting. This package provides five different function i.e. VMDARIMA, VMDELM, VMDRF, VMDSVR and VMDTDNN.

The HealthCal R package is designed to find out different parameters like basal metabolic rate, body mass index etc. related to the fitness and health of a person. This package shows the values of the parameters with the present health status (healthy or not) according the WHO prescribed norms.

The cointegration based support vector regression model is a combination of error correction model and support vector regression (http://krishi.icar.gov.in/jspui/handle/123456789/72361). This hybrid model allows the researcher to make use of the information extracted by the cointegrating vector as an input in the support vector regression model.