Implementation of sequential trial emulation for the analysis of observational databases.
    The 'SEQTaRget' software accommodates time-varying treatments and confounders, as well as binary
    and failure time outcomes. 'SEQTaRget' allows to compare both static and dynamic strategies,
    can be used to estimate observational analogs of intention-to-treat
    and per-protocol effects, and can adjust for potential selection bias
    induced by losses-to-follow-up. (Paper to come).

Sequential triangular test for the arithmetic mean in one- and two-
    samples, proportions in one- and two-samples, and the Pearson's correlation
    coefficient.

A collection of Radix Tree and Trie algorithms for finding similar sequences and calculating sequence distances (Levenshtein and other distance metrics). This work was inspired by a trie implementation in Python: "Fast and Easy Levenshtein distance using a Trie." Hanov (2011) <https://stevehanov.ca/blog/index.php?id=114>.

A rudimentary sequencer to define, manipulate and mix sound samples.
    The underlying motivation is to sonify data, as demonstrated in the blog <https://globxblog.github.io/>, the presentation by Renard and Le Bescond (2022, <https://hal.science/hal-03710340v1>) or the poster by Renard et al. (2023, <https://hal.inrae.fr/hal-04388845v1>).

Contains fast functions to calculate the exact Bayes posterior
    for the Sparse Normal Sequence Model, implementing the algorithms
    described in Van Erven and Szabo (2021,
    <doi:10.1214/20-BA1227>). For general hierarchical
    priors, sample sizes up to 10,000 are feasible within half an hour
    on a standard laptop. For beta-binomial spike-and-slab priors, a
    faster algorithm is provided, which can handle sample sizes of
    100,000 in half an hour. In the implementation, special care has
    been taken to assure numerical stability of the methods even for
    such large sample sizes.

Functions to calculate exact critical values, statistical power, expected time to signal, and required sample sizes for performing exact sequential analysis. All these	calculations can be done for either Poisson or binomial data, for continuous or group sequential analyses, and for different types of rejection boundaries. In case of group sequential analyses, the group sizes do not have to be specified in advance and the alpha spending can be arbitrarily settled. For regression versions of the methods, Monte Carlo and asymptotic methods are used.