Methods for simultaneous clustering and dimensionality reduction such as: Double k-means, Reduced k-means, Factorial k-means, Clustering with Disjoint PCA but also methods for exclusively dimensionality reduction: Disjoint PCA, Disjoint FA. The statistical methods implemented refer to the following articles: de Soete G., Carroll J. (1994) "K-means clustering in a low-dimensional Euclidean space" <doi:10.1007/978-3-642-51175-2_24> ; Vichi M. (2001) "Double k-means Clustering for Simultaneous Classification of Objects and Variables" <doi:10.1007/978-3-642-59471-7_6> ; Vichi M., Kiers H.A.L. (2001) "Factorial k-means analysis for two-way data" <doi:10.1016/S0167-9473(00)00064-5> ; Vichi M., Saporta G. (2009) "Clustering and disjoint principal component analysis" <doi:10.1016/j.csda.2008.05.028> ; Vichi M. (2017) "Disjoint factor analysis with cross-loadings" <doi:10.1007/s11634-016-0263-9>.

Utility functions to be used to analyse datasets obtained from seed germination/emergence assays. Fits several types of seed germination/emergence models, including those reported in Onofri et al. (2018) "Hydrothermal-time-to-event models for seed germination", European Journal of Agronomy, 101, 129-139 <doi:10.1016/j.eja.2018.08.011>. Contains several datasets for practicing.

A specific and comprehensive framework for the analyses of time-to-event data in agriculture. Fit non-parametric and parametric time-to-event models. Compare time-to-event curves for different experimental groups. Plots and other displays. It is particularly tailored to the analyses of data from germination and emergence assays. The methods are described in Onofri et al. (2022) "A unified framework for the analysis of germination, emergence, and other time-to-event data in weed science", Weed Science, 70, 259-271 <doi:10.1017/wsc.2022.8>.

Fit logistic functions to observed dose-response continuous
    data and evaluate goodness-of-fit measures. See Malyutina A., Tang J.,
    and Pessia A. (2023) <doi:10.18637/jss.v106.i04>.