Auto-Adaptive Parentage Inference Software Tolerant to Missing Parents (APIS)
Parentage assignment package. Parentage assignment is performed based on observed average Mendelian transmission probability distributions. The main function of this package is the function APIS(), which is the parentage assignment function.

Functional Latent Data Models for Clustering Heterogeneous Curves (‘FLaMingos’) (flamingos)
Provides a variety of original and flexible user-friendly statistical latent variable models for the simultaneous clustering and segmentation of heterogeneous functional data (i.e time series, or more generally longitudinal data, fitted by unsupervised algorithms, including EM algorithms. Functional Latent Data Models for Clustering heterogeneous curves (‘FLaMingos’) are originally introduced and written in ‘Matlab’ by Faicel Chamroukhi <https://…&type=public&language=matlab>. The references are mainly the following ones. Chamroukhi F. (2010) <https://…/FChamroukhi-PhD.pdf>. Chamroukhi F., Same A., Govaert, G. and Aknin P. (2010) <doi:10.1016/j.neucom.2009.12.023>. Chamroukhi F., Same A., Aknin P. and Govaert G. (2011). <doi:10.1109/IJCNN.2011.6033590>. Same A., Chamroukhi F., Govaert G. and Aknin, P. (2011) <doi:10.1007/s11634-011-0096-5>. Chamroukhi F., and Glotin H. (2012) <doi:10.1109/IJCNN.2012.6252818>. Chamroukhi F., Glotin H. and Same A. (2013) <doi:10.1016/j.neucom.2012.10.030>. Chamroukhi F. (2015) <https://…/FChamroukhi-HDR.pdf>. Chamroukhi F. and Nguyen H-D. (2019) <doi:10.1002/widm.1298>.

Create Split Packed Bubble Charts (hpackedbubble)
By binding R functions and the ‘Highcharts’ <http://…/> charting library, ‘hpackedbubble’ package provides a simple way to draw split packed bubble charts.

Count Transformation Models (cotram)
Count transformation models featuring parameters interpretable as discrete hazard ratios, odds ratios, reverse-time discrete hazard ratios, or transformed expectations. An appropriate data transformation for a count outcome and regression coefficients are simultaneously estimated by maximising the exact discrete log-likelihood using the computational framework provided in package ‘mlt’, technical details are given in Hothorn et al. (2018) <DOI:10.1111/sjos.12291>.