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dose_predict(). The returned error was too large and did
not make much sense due to an internal calculation error. Along with the
fix, the manual was updated to detail the uncertainty calculation. (PR
#42 by @RLumSK)set_energy() but did not show correctly when plotted using
the standard plot method, e.g., plot(cal, pks) would show
only observed but not expected energy lines in the secondary x-axis. Now
the expected energy lines (if set) are show. (#29, PR #32 by @RLumSK).var(2 * x) where
x is the integrated signal. Now the formula considers plain
Poisson statistics. Along with this change, the calculation is now
detailed in the manual (PR #46 by @RLumSK).read()(#28 by @RLumSK).GammaSpectra-class objects for
energy_calibrate()(issue: #22, PR #31 by @RLumSK).PeakPosition-class to
list (exported as as.list()) and from
list to PeakPosition-class. This enables
better plotting functionality if the peak positions for where provided
manually as list and not via, e.g.,
peak_find() (PR #37 by @RLumSK).dose_predict() and calculate a “final” dose based on the
mean of the findings from the count and the energy threshold (PR #43 by
@RLumSK)lm,
CalibrationCurve-class, and
GammaSpectrum-class toenergy_calibrate() for
GammaSpectrum-class and GammaSpectra-class()
objects for the argument lines. In simple words, instead of
providing data for an energy/channel calibration such calibration can be
copied over from another already calibrated spectrum (PR #49, #52 by
@RLumSK).dose_fit() and dose_predict(). What does it
mean? (1) If an energy calibration was performed on the spectra used for
the dose rate model fitting, the model information is forwarded to the
info slot of the model. (2) The function dose_fit() can
read this information and double-check whether the user tries to predict
the dose with calibrated or uncalibrated data. If the calibration has
data but the spectrum does not, the function tries to use the available
calibration. Given that the energy calibration often does not change
considerably, this should dramatically simplify the workflow once the
equipment was calibrated (PR #49 by @RLumSK).use_MC to dose_predict()
method. The default is FALSE to maintain compatibility with
old code and output exceptions. If set to TRUE the
uncertainty on the gamma dose rate uses a Monte Carlo simulation
approach for a more realistic error estimation (PR #46 by RLumSK)water_content to
dose_predict() to allow for an estimate of the dry gamma
dose rate using the correction factor by Aitken (1985). The default is
NULL, in this case nothing is corrected (PR #48 by @RLumSK)set_energy_calibration() and
get_energy_calibration() and corresponding methods for
GammaSpectrum and GammaSpectra objects. They
build on energy_calibrate() but enable a more
comprehensible scripting (PR XX by RLumSK).dose_predict() to work with a
numeric input for background as claimed in the
documentary. This value can also be set to c(0,0) if no
background subtraction is wanted (PR #38 by @RLumSK)dose_predict(),
which had some loopholes (PR #43 by @RLumSK)clermont dataset for
better transparency.clermont_2024 based on the
original clermont dataset but with dose rate conversion
factors and gamma dose rate calculated for different conversion factor
datasets (PR #40 by @RLumSK)set_energy<- so that argument value
appears in the method at the end of the argument list.alpha argument in dose_fit() to
follow changes in IsoplotR.default.stringsAsFactors(); fixed (#23,
@RLumSK)\doi instead of \href in
documentation.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.