Functions used in courses taught by Dr. Small at Drew University.

Animal movement models including Moving-Resting Process
    with Embedded Brownian Motion
    (Yan et al., 2014, <doi:10.1007/s10144-013-0428-8>;
    Pozdnyakov et al., 2017, <doi:10.1007/s11009-017-9547-6>),
    Brownian Motion with Measurement Error
    (Pozdnyakov et al., 2014, <doi:10.1890/13-0532.1>),
    Moving-Resting-Handling Process with Embedded Brownian Motion
    (Pozdnyakov et al., 2020, <doi:10.1007/s11009-020-09774-1>),
    Moving-Resting Process with Measurement Error
    (Hu et al., 2021, <doi:10.1111/2041-210X.13694>),
    Moving-Moving Process with two Embedded Brownian Motions.

Permits determination of a set of 
    optimal dynamic treatment regimes and sample size for a SMART design 
    in the Bayesian setting with binary outcomes. Please see Artman (2020) <arXiv:2008.02341>.

SMART trial design, as described by He, J., McClish, D., Sabo, R. (2021)  <doi:10.1080/19466315.2021.1883472>, includes multiple stages of randomization, where participants are randomized to an initial treatment in the first stage and then subsequently re-randomized between treatments in the following stage.

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.

A collection of recycled and modified R functions to aid in file manipulation, data exploration, wrangling, optimization, and object manipulation. Other functions aid in convenient data visualization, loop progression, software packaging, and installation.

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.