Compute Time series Resistant Smooth 4253H, twice smoothing method.

Sleep cycles are largely detected according to the originally proposed criteria by Feinberg & Floyd (1979) <doi:10.1111/j.1469-8986.1979.tb02991.x> as described in Blume & Cajochen (2021) <doi:10.1016/j.mex.2021.101318>.

Use behavioural variables to score activity and infer sleep from bouts of immobility. 
    It is primarily designed to score sleep in fruit flies from Drosophila Activity Monitor (TriKinetics) and Ethoscope data.
    It implements sleep scoring using the "five-minute rule" (Hendricks et al. (2000) <DOI:10.1016/S0896-6273(00)80877-6>),
    activity classification for Ethoscopes (Geissmann et al. (2017) <DOI:10.1371/journal.pbio.2003026>) 
    and a new algorithm to detect when animals are dead.

A tool to interactively explore the
  embeddings created by dimension reduction methods such as 
  Principal Components Analysis (PCA), Multidimensional Scaling (MDS), 
  T-distributed Stochastic Neighbour Embedding (t-SNE),
  Uniform Manifold Approximation and Projection (UMAP) or any other.

Efficient regression analysis under general two-phase sampling, where Phase I includes error-prone data and Phase II contains validated data on a subset.

The implementation of the algorithm for estimation of mutual information and channel capacity from experimental data by classification procedures (logistic regression). Technically, it allows to estimate information-theoretic measures between finite-state input and multivariate, continuous output. Method described in Jetka et al. (2019) <doi:10.1371/journal.pcbi.1007132>.

A framework for simulating spatially explicit genomic data which
    leverages real cartographic information for programmatic and visual encoding
    of spatiotemporal population dynamics on real geographic landscapes. Population
    genetic models are then automatically executed by the 'SLiM' software by Haller
    et al. (2019) <doi:10.1093/molbev/msy228> behind the scenes, using a custom
    built-in simulation 'SLiM' script. Additionally, fully abstract spatial models
    not tied to a specific geographic location are supported, and users can also
    simulate data from standard, non-spatial, random-mating models. These can be
    simulated either with the 'SLiM' built-in back-end script, or using an efficient
    coalescent population genetics simulator 'msprime' by Baumdicker et al. (2022)
    <doi:10.1093/genetics/iyab229> with a custom-built 'Python' script bundled with the
    R package. Simulated genomic data is saved in a tree-sequence format and can be
    loaded, manipulated, and summarised using tree-sequence functionality via an R
    interface to the 'Python' module 'tskit' by Kelleher et al. (2019)
    <doi:10.1038/s41588-019-0483-y>. Complete model configuration, simulation and
    analysis pipelines can be therefore constructed without a need to leave the R
    environment, eliminating friction between disparate tools for population genetic
    simulations and data analysis.