Implement maximum likelihood estimation for Poisson generalized
  linear models with grouped and right-censored count data. Intended to be used
  for analyzing grouped and right-censored data, which is widely applied in
  many branches of social sciences. The algorithm implemented is described
  in Fu et al., (2021) <doi:10.1111/rssa.12678>.

A tool for registering (aligning) gene expression profiles
    between reference and query data.

Provides algorithms for detection of spatial patterns from oceanographic data using image processing methods based on Gradient Recognition.

An ensemble of algorithms that enable the clustering of networks and data matrices (such as counts, categorical or continuous) with different type of generative models. Model selection and clustering is performed in combination by optimizing the Integrated Classification Likelihood (which is equivalent to minimizing the description length). Several models are available such as: Stochastic Block Model, degree corrected Stochastic Block Model, Mixtures of Multinomial, Latent Block Model. The optimization is performed thanks to a combination of greedy local search and a genetic algorithm (see <arXiv:2002:11577> for more details).

Summarises a collection of partitions into a single optimal partition. The objective function is the expected posterior loss, and the minimisation is performed through a greedy algorithm described in Rastelli, R. and Friel, N. (2017) "Optimal Bayesian estimators for latent variable cluster models" <DOI:10.1007/s11222-017-9786-y>.

Computes experimental designs for two-arm experiments with
    covariates using multiple methods, including: (0) complete randomization
    and randomization with forced-balance; (1) greedy optimization of a balance
    objective function via pairwise switching; (2) numerical optimization via
    'gurobi'; (3) rerandomization; (4) Karp's method for one covariate; (5)
    exhaustive enumeration for small sample sizes; (6) binary pair matching
    using 'nbpMatching'; (7) binary pair matching plus method (1) to further
    optimize balance; (8) binary pair matching plus method (3) to further
    optimize balance; (9) Hadamard designs; and (10) simultaneous multiple
    kernels. For the greedy, rerandomization, and related methods, three
    objective functions are supported: Mahalanobis distance, standardized sums
    of absolute differences, and kernel distances via the 'kernlab' library.
    This package is the result of a stream of research that can be found in
    Krieger, A. M., Azriel, D. A., and Kapelner, A. (2019). "Nearly Random
    Designs with Greatly Improved Balance." Biometrika 106(3), 695-701
    <doi:10.1093/biomet/asz026>. Krieger, A. M., Azriel, D. A., and
    Kapelner, A. (2023). "Better experimental design by hybridizing binary
    matching with imbalance optimization." Canadian Journal of Statistics,
    51(1), 275-292 <doi:10.1002/cjs.11685>.