Provides automated visual inference of residual plots using computer vision models, facilitating diagnostic checks for classical normal linear regression models.

Weather indices are formed from weather variables in this package. The users can input any number of weather variables recorded over any number of weeks. This package has no restriction on the number of weeks and weather variables to be taken as input.The details of the method can be seen (i)'Joint effects of weather variables on rice yields' by  R. Agrawal, R. C. Jain  and M. P. Jha in Mausam, vol. 34, pp. 189-194, 1983,<doi:10.54302/mausam.v34i2.2392>,(ii)'Improved weather indices based Bayesian regression model for forecasting crop yield' by  M. Yeasin, K. N. Singh, A. Lama and B. Gurung in Mausam, vol. 72, pp.879-886, 2021,<doi:10.54302/mausam.v72i4.670>.

When many possible multiplier method estimates of a target population are available, a weighted sum of estimates from each back-calculated path can be achieved with this package.  Variance-minimizing weights are used and with any admissible tree-structured data.  The methodological basis used to create this package can be found in Flynn (2023) <http://hdl.handle.net/2429/86174>.

Probability surveys often use auxiliary continuous data from administrative records, but the utility of this data is diminished when it is discretized for confidentiality. We provide a set of survey estimators to make full use of information from the discretized variables. See Williams, S.Z., Zou, J., Liu, Y., Si, Y., Galea, S. and Chen, Q. (2024), Improving Survey Inference Using Administrative Records Without Releasing Individual-Level Continuous Data. Statistics in Medicine, 43: 5803-5813. <doi:10.1002/sim.10270> for details.

Provides tools for assessing and selecting auxiliary variables using LASSO. 
    The package includes functions for variable selection and diagnostics, facilitating 
    survey calibration analysis with emphasis on robust auxiliary vector selection. For 
    more details see Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x> and 
    Caughrey and Hartman (2017) <doi:10.2139/ssrn.3494436>.