person("Sayantani","Karmakar", email = "sayantanikarmakar20@gmail.com", role = "aut"),
person("Md. Ashraful", "Haque", email = "ashrafulhaque664@gmail.com" , role = c("aut", "cre")),
person("Cini", "Varghese", email = "cini2204@gmail.com" , role = "aut"),
person("Seema", "Jaggi", email = "seema.jaggi@icar.gov.in", role ="aut"),
person("Eldho", "Varghese", email = "eldhoiasri@gmail.com" , role = "aut"),
person("Mohd.", "Harun", email = "harun.agribhu@gmail.com" , role = "aut")
Maintainer: Md. Ashraful Haque  <ashrafulhaque664@gmail.com>
Description: The Row-column designs are widely recommended for experimental situations when there are two well-identified factors that are cross-classified representing known sources of variability. These designs are expected to result a gain in accuracy of estimating treatment comparisons in an experiment as they eliminate the effects of the row and column factors. However, these designs are not readily available when the number of treatments is more than the levels of row and column blocking factors. This package named 'iRoCoDe' generates row-column designs with incomplete rows and columns, by amalgamating two incomplete block designs (D1 and D2). The selection of D1 and D2 (the input designs) can be done from the available incomplete block designs, viz., balanced incomplete block designs/ partially balanced incomplete block designs/ t-designs. (Mcsorley, J.P., Phillips, N.C., Wallis, W.D. and Yucas, J.L. (2005).<doi:10.1007/s10623-003-6149-9>).

Title: Invasive Species Distribution Modelling
Description: Functions for predicting and mapping potential and realized distributions of invasive species within the invaded range.
Imports: stats, utils, graphics, ade4, geometry, geosphere, maptools,
MASS, pdist, raster, rgdal, sp, colorRamps, virtualspecies, rgl

Title: Integrative Dimension Reduction Analysis for Multi-Source Data
Description: An implementation of integrative dimension reduction analysis procedures for multi-source data, which provides integrative sparse partial least squares, integrative sparse principal component analysis, integrative sparse canonical correlation analysis and corresponding meta-analysis versions, etc. The work is built and improved upon the references: (1) Fang, K., Fan, X., Zhang, Q., and Ma, S. (2018). "Integrative sparse principal component analysis." Journal of Multivariate Analysis, <doi:10.1016/j.jmva.2018.02.002>. (2) Liang, W., Ma, S., Zhang, Q., and Zhu, T. (2021). "Integrative sparse partial least squares." Statistics in Medicine, <doi:10.1002/sim.8900>.
Imports: caret, graphics, grDevices, irlba, stats