We focus on the diagnostic ability assessment of medical tests when the outcome of interest is the status (alive or dead) of the subjects at a certain time-point t. This binary status is determined by right-censored times to event and it is unknown for those subjects censored before t. Here we provide three methods (unknown status exclusion, imputation of censored times and using time-dependent ROC curves) to evaluate the diagnostic ability of binary and continuous tests in this context. Two references for the methods used here are Skaltsa et al. (2010) <doi:10.1002/bimj.200900294> and Heagerty et al. (2000) <doi:10.1111/j.0006-341x.2000.00337.x>.

Provides functions for the selection of thresholds for use in 
    extreme value models, based mainly on the methodology in 
    Northrop, Attalides and Jonathan (2017) <doi:10.1111/rssc.12159>.
    It also performs predictive inferences about future extreme values, 
    based either on a single threshold or on a weighted average of inferences 
    from multiple thresholds, using the 'revdbayes' package 
    <https://cran.r-project.org/package=revdbayes>.   
    At the moment only the case where the data can be treated as 
    independent identically distributed observations is considered.

Pure R implementation of Apache Thrift.
    This library doesn't require any code generation.
    To learn more about Thrift go to <https://thrift.apache.org>.

Allow to compute and visualise convective parameters commonly
  used in the operational prediction of severe convective storms. Core algorithm
  is based on a highly optimized 'C++' code linked into 'R' via 'Rcpp'. Highly
  efficient engine allows to derive thermodynamic and kinematic parameters from
  large numerical datasets such as reanalyses or operational Numerical Weather 
  Prediction models in a reasonable amount of time. Package has been developed
  since 2017 by research meteorologists specializing in severe thunderstorms.
  The most relevant methods used in the package based on the following publications
  Stipanuk (1973) <https://apps.dtic.mil/sti/pdfs/AD0769739.pdf>,
  McCann et al. (1994) <doi:10.1175/1520-0434(1994)009%3C0532:WNIFFM%3E2.0.CO;2>,
  Bunkers et al. (2000) <doi:10.1175/1520-0434(2000)015%3C0061:PSMUAN%3E2.0.CO;2>,
  Corfidi et al. (2003) <doi:10.1175/1520-0434(2003)018%3C0997:CPAMPF%3E2.0.CO;2>,
  Showalter (1953) <doi:10.1175/1520-0477-34.6.250>,
  Coffer et al. (2019) <doi:10.1175/WAF-D-19-0115.1>,
  Gropp and Davenport (2019) <doi:10.1175/WAF-D-17-0150.1>,
  Czernecki et al. (2019) <doi:10.1016/j.atmosres.2019.05.010>,
  Taszarek et al. (2020) <doi:10.1175/JCLI-D-20-0346.1>,
  Sherburn and Parker (2014) <doi:10.1175/WAF-D-13-00041.1>,
  Romanic et al. (2022) <doi:10.1016/j.wace.2022.100474>.