For estimation of a variable of interest using Kalman filter by incorporating results from previous assessments, i.e. through development weighted estimates where weights are assigned inversely proportional to the variance of existing and new estimates. For reference see Ehlers et al. (2017) <doi:10.20944/preprints201710.0098.v1>.

Based on a SAS data step. This allows for row-wise dynamic building
    of data, iteratively importing slices of existing dataframes, conducting
    analyses, and exporting to a results frame. This is particularly useful for
    differential or time-series analyses, which are often not well suited to vector-
    based operations.

Provides a set of functions and a class to connect, extract and
    upload information from the 'Refinitiv Datastream' database. This
    package uses the 'DSWS' API and server used by the 'Datastream DFO addin'.  
    Details of this API are available at <https://www.lseg.com/en/data-analytics>.
    Please report issues at <https://github.com/CharlesCara/DatastreamDSWS2R/issues>.

Pulls together a collection of datasets from Miguel de Carvalho research articles. Including, for example:
    - de Carvalho (2012) <doi:10.1016/j.jspi.2011.08.016>;
    - de Carvalho et al (2012) <doi:10.1080/03610926.2012.709905>;
    - de Carvalho et al (2012) <doi:10.1016/j.econlet.2011.09.007>);
    - de Carvalho and Davison (2014) <doi:10.1080/01621459.2013.872651>;
    - de Carvalho and Rua (2017) <doi:10.1016/j.ijforecast.2015.09.004>.

Allows you to define rules which can be used to verify a given
    dataset.
    The package acts as a thin wrapper around more powerful data packages such
    as 'dplyr', 'data.table', 'arrow', and 'DBI' ('SQL'), which do the heavy lifting.

Provides access to Dataverse APIs <https://dataverse.org/> (versions 4-5),
    enabling data search, retrieval, and deposit. For Dataverse versions <= 3.0,
    use the archived 'dvn' package <https://cran.r-project.org/package=dvn>.

Gives access to data visualisation methods that are relevant from the data scientist's point of view. The flagship idea of 'DataVisualizations' is the mirrored density plot (MD-plot) for either classified or non-classified multivariate data published in Thrun, M.C. et al.: "Analyzing the Fine Structure of Distributions" (2020), PLoS ONE, <DOI:10.1371/journal.pone.0238835>. The MD-plot outperforms the box-and-whisker diagram (box plot), violin plot and bean plot and geom_violin plot of ggplot2. Furthermore, a collection of various visualization methods for univariate data is provided. In the case of exploratory data analysis, 'DataVisualizations' makes it possible to inspect the distribution of each feature of a dataset visually through a combination of four methods. One of these methods is the Pareto density estimation (PDE) of the probability density function (pdf). Additionally, visualizations of the distribution of distances using PDE, the scatter-density plot using PDE for two variables as well as the Shepard density plot and the Bland-Altman plot are presented here. Pertaining to classified high-dimensional data, a number of visualizations are described, such as f.ex. the heat map and silhouette plot. A political map of the world or Germany can be visualized with the additional information defined by a classification of countries or regions. By extending the political map further, an uncomplicated function for a Choropleth map can be used which is useful for measurements across a geographic area. For categorical features, the Pie charts, slope charts and fan plots, improved by the ABC analysis, become usable. More detailed explanations are found in the book by Thrun, M.C.: "Projection-Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9>.