Analyze telemetry datasets generalized to allow
    any technology. The filtering steps check for false positives caused by 
    reflected transmissions from surfaces and false pings from other noise 
    generating equipment. The filters are based on JSATS filtering algorithms
    found in package 'filteRjsats' <https://CRAN.R-project.org/package=filteRjsats>
    but have been generalized to allow the user to define many of the filtering 
    variables. Additionally, this package contains scripts used to help identify 
    an optimal maximum blanking period as defined in Capello et al (2015) 
    <doi:10.1371/journal.pone.0134002>. The functions were written according to
    their manuscript description, but have not been reviewed by the authors for 
    accuracy. It is included here as is, without warranty.

Fits Bayesian finite mixtures with an unknown number of components using the telescoping sampler and different component distributions. For more details see Frühwirth-Schnatter et al. (2021) <doi:10.1214/21-BA1294>, Malsiner-Walli et al. (in press) <doi:10.1007/s11634-025-00640-x> and Malsiner-Walli et al. (2026) <doi:10.48550/arXiv.2603.00277>.

Using The Free Evocation of Words Technique method with some functions, this package will make a
    social representation and other analysis. The Free Evocation of Words Technique consists of collecting a number of words evoked by a subject facing exposure to an inducer term. The purpose of this technique is to understand the relationships created between words evoked by the individual and the inducer term. This technique is included in the theory of social representations, therefore, on the information transmitted by an individual, seeks to create a profile that define a social group.

Streamline the processing of 'Telraam' data, sourced from open data
    mobility sensors. These tools range from data retrieval (without the need for
    API knowledge) to data visualization, including data preprocessing.

Temporal disaggregation methods are used to disaggregate and
    interpolate a low frequency time series to a higher frequency series, where
    either the sum, the mean, the first or the last value of the resulting
    high frequency series is consistent with the low frequency series. Temporal
    disaggregation can be performed with or without one or more high frequency
    indicator series. Contains the methods of Chow-Lin, Santos-Silva-Cardoso,
    Fernandez, Litterman, Denton and Denton-Cholette, summarized in Sax and
    Steiner (2013) <doi:10.32614/RJ-2013-028>. Supports most R time series
    classes.