S4 class object for creating and managing group sequential designs. It calculates the efficacy and futility boundaries at each look. It allows modifying the design and tracking the design update history.

Based on the structure of the SPSS version of the Swiss
    Household Panel (SHP) data, provides a function seqFromWaves()
    that seeks the data of variables specified by the user in each 
    of the wave files and collects them as sequences. The function also 
    matches the sequences with variables from other files such as the 
    master files of persons (MP) and households (MH), and social origins (SO). 
    It can also match with activity calendar data (CA).

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.