Interface to the 'ChEA3' transcription factor enrichment API.
    'ChEA3' integrates evidence from ChIP-seq, co-expression, and literature
    resources to prioritize transcription factors regulating a given set
    of genes. This package provides convenient R functions to query the API,
    retrieve ranked results across collections (including integrated scores),
    and standardize output for downstream analysis in R/Bioconductor workflows.
    See <https://maayanlab.cloud/chea3/> or Keenan (2019) <doi:10.1093/nar/gkz446>
    for further details.

Data exploration and prediction with focus on high dimensional data and chemometrics.
    The package was initially designed about partial least squares regression and discrimination models and variants, in particular locally weighted PLS models (LWPLS). Then, it has been expanded to many other methods for analyzing high dimensional data.
    The name 'rchemo' comes from the fact that the package is orientated to chemometrics, but most of the provided methods are fully generic to other domains.
    Functions such as transform(), predict(), coef() and summary() are available. Tuning the predictive models is facilitated by generic functions gridscore() (validation dataset) and gridcv() (cross-validation). Faster versions are also available for models based on latent variables (LVs) (gridscorelv() and gridcvlv()) and ridge regularization (gridscorelb() and gridcvlb()).

Provides a dataset of functions in all base and recommended packages of R versions 0.50 onwards.

Provides algorithms to locate multiple
    distributional change-points in piecewise stationary time series. The
    algorithms are provably consistent, even in the presence of long-range
    dependencies. Knowledge of the number of change-points is not
    required. The code is written in Go and interfaced with R.

Detect and remove chimeras from your amplicon sequence analysis 
    using reference-based or de novo approaches. The 'rchime' package 
    implements the 'VSEARCH' algorithms described in Rognes et al. (2016) 
    <doi:10.7717/peerj.2584>. 'VSEARCH' builds on the work of Edgar,R.C. et al.
    (2011) <doi:10.1093/bioinformatics/btr381>.