Fits a variety of cure models using excess hazard modeling methodology such as the mixture model proposed by Phillips et al. (2002) <doi:10.1002/sim.1101> The Weibull distribution is used to represent the survival function of the uncured patients; Fits also non-mixture cure model such as the time-to-null excess hazard model proposed by Boussari et al. (2020) <doi:10.1111/biom.13361>.

Bindings to 'libcurl' <https://curl.se/libcurl/> for performing fully
    configurable HTTP/FTP requests where responses can be processed in memory, on
    disk, or streaming via the callback or connection interfaces. Some knowledge
    of 'libcurl' is recommended; for a more-user-friendly web client see the 
    'httr2' package which builds on this package with http specific tools and logic.

An R client for the 'currencyapi.com' currency conversion API. The API requires registration of an API key. Basic features are free, some require a paid subscription. You can find the full API documentation at <https://currencyapi.com/docs> .

The currentSurvival package contains functions for
  the estimation of the current cumulative incidence (CCI) and
  the current leukaemia-free survival (CLFS). The CCI is the 
  probability that a patient is alive and in any disease 
  remission (e.g. complete cytogenetic remission in chronic 
  myeloid leukaemia) after initiating his or her therapy (e.g. 
  tyrosine kinase therapy for chronic myeloid leukaemia). The 
  CLFS is the probability that a patient is alive and in any 
  disease remission after achieving the first disease remission.

Provides an implementation of ‘Curricular Analytics’, a framework for analyzing and quantifying the complexity of academic curricula. Curricula are modelled as directed acyclic graphs and analytics are provided based on path lengths and edge density. This work directly comes from Heileman et al. (2018) <doi:10.48550/arXiv.1811.09676>.

Implementations of the family of map() functions with frequent saving of the intermediate results. The contained functions let you start the evaluation of the iterations where you stopped  (reading the already evaluated ones from cache), and work with the currently evaluated iterations while remaining ones are running in a background job. Parallel computing is also easier with the workers parameter.