Exploratory analysis on any input data describing the structure and the relationships present in the data. The package automatically select the variable and does related descriptive statistics. Analyzing information value, weight of evidence, custom tables, summary statistics, graphical techniques will be performed for both numeric and categorical predictors.

Tools for accessing and processing datasets prepared by the Foundation SmarterPoland.pl. Among all: access to API of Google Maps, Central Statistical Office of Poland, MojePanstwo, Eurostat, WHO and other sources.

Preview spatial data as 'leaflet' maps with minimal
  effort. smartmap is optimized for interactive use and distinguishes itself 
  from similar packages because it does not need real spatial ('sp' or 'sf')
  objects an input; instead, it tries to automatically coerce everything that 
  looks like spatial data to sf objects or leaflet maps. It - for example -  
  supports direct mapping of: a vector containing a single coordinate pair,
  a two column matrix, a data.frame with longitude and latitude columns, or
  the path or URL to a (possibly compressed) 'shapefile'.

Methods for analysis of energy consumption data (electricity, gas, 
    water) at different data measurement intervals. The package provides feature extraction 
    methods and algorithms to prepare data for data mining and machine learning 
    applications. Deatiled descriptions of the methods and their application can be found 
    in Hopf (2019, ISBN:978-3-86309-669-4) "Predictive Analytics for Energy Efficiency and 
    Energy Retailing" <doi:10.20378/irbo-54833> and Hopf et al. (2016) <doi:10.1007/s12525-018-0290-9> 
    "Enhancing energy efficiency in the residential sector with smart meter data analytics".

Sample size calculation to detect dynamic treatment regime (DTR) effects based on change in clinical attachment level (CAL) outcomes from a non-surgical chronic periodontitis treatments study. The experiment is performed under a Sequential Multiple Assignment Randomized Trial (SMART) design. The clustered tooth (sub-unit) level CAL outcomes are skewed, spatially-referenced, and non-randomly missing. The implemented algorithm is available in Xu et al. (2019+) <arXiv:1902.09386>.