| Name : R-WienR
| |
| Version : 0.3.15
| Vendor : obs://build_opensuse_org/devel:languages:R
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| Release : lp153.1.4
| Date : 2024-06-14 11:04:56
|
| Group : Development/Libraries/Other
| Source RPM : R-WienR-0.3.15-lp153.1.4.src.rpm
|
| Size : 0.62 MB
| |
| Packager : (none)
| |
| Summary : Derivatives of the First-Passage Time Density and Cumulative Distribution Function, and Random Sampling from the (Truncated) First-Passage Time Distribution
|
Description :
First, we provide functions to calculate the partial derivative of the
first-passage time diffusion probability density function (PDF) and
cumulative distribution function (CDF) with respect to the
first-passage time t (only for PDF), the upper barrier a, the drift
rate v, the relative starting point w, the non-decision time t0, the
inter-trial variability of the drift rate sv, the inter-trial
variability of the rel. starting point sw, and the inter-trial
variability of the non-decision time st0. In addition the PDF and CDF
themselves are also provided. Most calculations are done on the
logarithmic scale to make it more stable. Since the PDF, CDF, and their
derivatives are represented as infinite series, we give the user the
option to control the approximation errors with the argument
\'precision\'. For the numerical integration we used the C library
cubature by Johnson, S. G. (2005-2013)
< https://github.com/stevengj/cubature>. Numerical integration is
required whenever sv, sw, and/or st0 is not zero. Note that numerical
integration reduces speed of the computation and the precision cannot
be guaranteed anymore. Therefore, whenever numerical integration is
used an estimate of the approximation error is provided in the output
list. Note: The large number of contributors (ctb) is due to copying a
lot of C/C++ code chunks from the GNU Scientific Library (GSL). Second,
we provide methods to sample from the first-passage time distribution
with or without user-defined truncation from above. The first method is
a new adaptive rejection sampler building on the works of Gilks and
Wild (1992; < doi:10.2307/2347565>) and Hartmann and Klauer (in press).
The second method is a rejection sampler provided by Drugowitsch (2016;
< doi:10.1038/srep20490>). The third method is an inverse transformation
sampler. The fourth method is a \"pseudo\" adaptive rejection sampler
that builds on the first method. For more details see the corresponding
help files.
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RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
