Identifies gendered words and phrases in text using a built in
    dictionary of more than two hundred gendered terms paired with gender
    neutral alternatives. Reports the share of gendered language in a text,
    lists every gendered term found together with its suggested neutral
    replacement, and can rewrite a text in gender neutral form. Plain text
    files are read with base R, while other document formats such as PDF and
    Word are supported through the optional 'readtext' package. The
    dictionary is informed by published guidance on gender inclusive
    language, including the United Nations guidelines
    <https://www.un.org/en/gender-inclusive-language/> and the European
    Parliament guidance on gender neutral language.

Computes the probability density function (pdf), cumulative distribution function (cdf), quantile function (qf) and generates random values (rg) for the following general models : mixture models, composite models, folded models, skewed symmetric models and arc tan models.

Segmentation and classification procedures for data from the 'Activinsights GENEActiv' <https://activinsights.com/technology/geneactiv/> accelerometer that provides the user with a model to guess behaviour from test data where behaviour is missing.
    Includes a step counting algorithm, a function to create segmented data with custom features and a function to use recursive partitioning provided in the function rpart() of the 'rpart' package to create classification models.

Analytics to read in and segment raw 'GENEActiv' accelerometer data into epochs and events.
    For more details on the 'GENEActiv' device, see <https://activinsights.com/resources/geneactiv-support-1-2/>.

Functions and analytics for GENEA-compatible accelerometer data into R objects. 
             See topic 'GENEAread' for an introduction to the package. 
             See <https://activinsights.com/technology/geneactiv/> for more details on the GENEActiv device.

The GeneCycle package implements the approaches of Wichert
        et al. (2004) <doi:10.1093/bioinformatics/btg364>, Ahdesmaki 
        et al. (2005) <doi:10.1186/1471-2105-6-117> and Ahdesmaki et al.
        (2007) <DOI:10.1186/1471-2105-8-233> for detecting periodically 
        expressed genes from gene expression time series data.