With careful implementation, these methods potentially harness the positive effects of educational technology while diminishing the negative effects of media multitasking.
While differing in implementation, these methods consistently rely on identifying proximal markers through linkage disequilibrium.
Availability and implementation: We provide implementation of these methods in an R package cancerTiming, which is available from the Comprehensive R Archive Network (CRAN) at http://CRAN.R-project.org/.org/
We test several implementations of these methods using the automated tools including bounded model checking (CBMC), static analysis (Coverity) with BugBench, and compare and evaluate the results.
Since 2001 the implementations of these methods are carried out according to different requirements for tramway vehicles, metro vehicles, and main line EMU vehicles etc.
Moreover, although new data analysis methods are also appearing, the individual implementations of these methods may only be available on specific platforms and must be accessed according to differing protocols.
The next section recalls some basic mathematical concepts of the continuation approaches, with a focus on the numerical implementations of these methods.
However, there are noticeable shortcomings in implementations of these methods especially the use of arbitrary function as a starting value, an in-appropriate choice of starting function may affect the rate of convergence, see [11, 12].
Finally, a further assumption for unbiased treatment effect estimates in parametric implementations of these methods is that the model reflects the true relationship between the outcome of interest and the covariates used for the adjustment [17].
We observed that these models were statistically better (Table 5) despite the statistical drawbacks that have been attributed to the current implementations of these methods [ 26].
