Nothing
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
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