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 a
    two-arm experiment with covariates via a number of methods:
    (0) complete randomization and randomization with forced-balance,
    (1) Greedily optimizing a
    balance objective function via pairwise switching. This optimization 
    provides lower variance for the treatment effect estimator (and higher 
    power) while preserving a design that is close to complete randomization.
    We return all iterations of the designs for use in a permutation test,
    (2) The second is via numerical optimization 
    (via 'gurobi' which must be installed, see <https://www.gurobi.com/documentation/9.1/quickstart_windows/r_ins_the_r_package.html>) 
    a la Bertsimas and Kallus, 
    (3) rerandomization, 
    (4) Karp's method for one covariate, 
    (5) exhaustive enumeration to find the 
    optimal solution (only for small sample sizes),
    (6) Binary pair matching using the 'nbpMatching' library,
    (7) Binary pair matching plus design number (1) to further optimize balance,
    (8) Binary pair matching plus design number (3) to further optimize balance,
    (9) Hadamard designs,
    (10) Simultaneous Multiple Kernels.
    In (1-9) we allow for three objective functions:
    Mahalanobis distance,
    Sum of absolute differences standardized and
    Kernel distances via the 'kernlab' library. This package is the result of a stream of research that can be found in 
    Krieger, A, Azriel, D and Kapelner, A "Nearly Random Designs with Greatly Improved Balance" (2016) <arXiv:1612.02315>,
    Krieger, A, Azriel, D and Kapelner, A "Better Experimental Design by Hybridizing Binary Matching with Imbalance 
    Optimization" (2021) <arXiv:2012.03330>.