The functions in this package inspect, read, edit and run files for 'APSIM' "Next Generation" ('JSON')
             and 'APSIM' "Classic" ('XML'). The files with an 'apsim' extension correspond to
	     'APSIM' Classic (7.x) - Windows only - and the ones with an 'apsimx' extension correspond to 'APSIM' "Next Generation".
	     For more information about 'APSIM' see (<https://www.apsim.info/>) and for 'APSIM'
	     next generation (<https://apsimnextgeneration.netlify.app/>).

Asymmetric price transmission between two time series is assessed. Several functions are available for linear and nonlinear threshold cointegration, and furthermore, symmetric and asymmetric error correction model.

We provide tools to estimate two prediction accuracy metrics,
    the average positive predictive values (AP) as well as the well-known AUC
    (the area under the receiver operator characteristic curve) for risk scores. 
    The outcome of interest is either binary or censored event time.
    Note that for censored event time, our functions' estimates, the AP and the
    AUC, are time-dependent for pre-specified time interval(s). A function that
    compares the APs of two risk scores/markers is also included. Optional
    outputs include positive predictive values and true positive fractions at
    the specified marker cut-off values, and a plot of the time-dependent AP
    versus time (available for event time data).

Provides a quick method for visualizing non-aggregated line-list
    or aggregated census data stratified by age and one or two categorical
    variables (e.g. gender and health status) with any number of values. It
    returns a 'ggplot' object, allowing the user to further customize the
    output. This package is part of the 'R4Epis' project 
    <https://r4epis.netlify.app/>.

Developed for use by those tasked with the routine detection, characterisation
          and quantification of discrete changes in air quality time-series, such as
          identifying the impacts of air quality policy interventions. The main functions use
          signal isolation then break-point/segment (BP/S) methods based on 'strucchange' and
          'segmented' methods to detect and quantify change events (Ropkins & Tate, 2021,
          <doi:10.1016/j.scitotenv.2020.142374>).