c(person("Achal", "Lama", role = c("aut","cre"),email="achal.lama@icar.gov.in"),person("Kamlesh", "N Singh", role = "aut",email="kn.Singh@icar.gov.in"),person(" Bishal", "Gurung", role = "aut",email="bishal.gurung@icar.gov.in"))
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>.
Achal Lama [aut, cre],
Kamlesh N Singh [aut],
Bishal Gurung [aut]

Development of Visualization Tools for Protein Sequence
The image of the amino acid transform on the protein level is drawn, and the automatic routing of the functional elements such as the domain and the mutation site is completed.

Minimum Distance Estimation in Autoregressive Model
person("Jiwoong", "Kim", email = "kimjiwo2@stt.msu.edu", role = c("aut", "cre"))
Consider autoregressive model of order p where the distribution function of innovation is unknown, but innovations are independent and symmetrically distributed. The package contains a function named ARMDE which takes X (vector of n observations) and p (order of the model) as input argument and returns minimum distance estimator of the parameters in the model.

Retrieve Disease-Related Genes from Public Sources
c(
person("Jiawei", "Wu", email = "jared_wood@163.com",
role = c("aut","cre")),
person("Xu", "Li", email = "tsl-lixu@tasly.com",
role = "aut"))
For researchers to quickly and comprehensively acquire disease genes, so as to understand the mechanism of disease,
we developed this program to acquire disease-related genes.
The data is integrated from three public databases. The three databases are 'eDGAR', 'DrugBank'
and 'MalaCards'. The 'eDGAR' is a comprehensive database, containing data on the relationship between disease and genes.
'DrugBank' contains information on 13443 drugs and 5157 targets. 'MalaCards' integrates human disease information, including disease-related genes.