When a network is partially observed (here, NAs in the adjacency matrix rather than 1 or 0 
  due to missing information between node pairs), it is possible to account for the underlying process
  that generates those NAs. 'missSBM', presented in 'Barbillon, Chiquet and Tabouy' (2022) <doi:10.18637/jss.v101.i12>,
  adjusts the popular stochastic block model from network data sampled under various missing data conditions, 
  as described in 'Tabouy, Barbillon and Chiquet' (2019) <doi:10.1080/01621459.2018.1562934>.

The Self-Organizing Maps with Built-in Missing Data Imputation. Missing values are imputed and regularly updated during the online Kohonen algorithm. Our method can be used for data visualisation, clustering or imputation of missing data. It is an extension of the online algorithm of the 'kohonen' package. The method is described
    in the article "Self-Organizing Maps for Exploration of Partially Observed Data and Imputation of Missing Values" by S. Rejeb, C. Duveau, T. Rebafka (2022) <arXiv:2202.07963>.

A flexible computational framework for mixture distributions with the focus on the composite models.

Various reliability analysis methods for rare event inference (computing failure probability and quantile from model/function outputs).

Miscellaneous functions for (1) data management (e.g., grand-mean and group-mean centering, coding variables and reverse coding items, scale and cluster scores, reading and writing Excel and SPSS files), (2) descriptive statistics (e.g., frequency table, cross tabulation, effect size measures), (3) missing data (e.g., descriptive statistics for missing data, missing data pattern, Little's test of Missing Completely at Random, and auxiliary variable analysis), (4) multilevel data (e.g., multilevel descriptive statistics, within-group and between-group correlation matrix, multilevel confirmatory factor analysis, level-specific fit indices, cross-level measurement equivalence evaluation,  multilevel composite reliability, and multilevel R-squared measures), (5) item analysis (e.g., confirmatory factor analysis, coefficient alpha and omega, between-group and longitudinal measurement equivalence evaluation), and (6) statistical analysis (e.g., confidence intervals, collinearity and residual diagnostics, dominance analysis, between- and within-subject analysis of variance, latent class analysis, t-test, z-test, sample size determination).