calc_sdt()
. This function could be exported in future.wrangle_data()
and now there is tarflow.iquizoo::parse_data()
to replace it (#86).wrangle_data()
to preproc_data()
(#85).memory_score
to drm()
which integrates both true memory and false memory."cutoff"
method in response time outlier detection, which is used in srt()
now."transform"
method in response time outlier detection, which was stated to be used but the code was not right. Now it is used as default if not specified.calc_sdt()
throw error when type column is not valid.calc_indices()
.wrangle_data()
and preproc_data()
functions, which were previously in tarflow.iquizoo package.fit_numerosity()
will stuck in infinite loop.calc_staircase_wetherill()
to be even. Odd number of reversals will be automatically adjusted to even number by removing the first reversal from the condition with larger number of reversals (#75).condstairs()
to analyze data from self-adaptive filtering task.capacity()
to analyze data from visual working memory capacity task.fname()
to analyze data from face-name associative task.ant_orient()
and ant_alert()
to analyze data from split version of attention network tests (ANT).countcorrect()
."outcome"
column from "acc"
column in span()
. This might need further configuration of "outcome_rule"
(default to 1, means no more error count of 1) in argument .extra
.fit_numerosity()
. Future work might be expanded to support setting this (rather easy but not so important now).span()
when correctness column has NA
values not in all rows, in which case the function would not calculate partical credit scores for all cases.pc
) of cpt()
to its original nc
..by
argument back to all functions for speed improvement.class
as input data
. This is somewhat so-called class invariant.rapm()
for Raven's Advanced Progressive Matrices test.fit_numerosity()
.calc_spd_acc()
.racer()
.reinf()
.span()
(#65)..by
argument from all functions is removed. The decision to include that grouping argument is now considered a redundant feature.racer()
to support NeuroRacer tests.reinf()
to support for reinforcement learning tests.staircase()
with inflection point.synwin()
caused by a new design of the test.dualnback()
to support for dual n-back tests.staircase()
to support for tests shipped with staircase method.synwin()
to support modified SynWin test.span()
can deal with location memory test.countcorrect()
and countcorrect2()
.options()
support is removed, but accept two additional arguments .input
and .extra
.where
from global variables.options()
, for now including input variable names in data and specific values for certain variables.preproc()
is removed because it is believed there is no place for it after this refactoring.prep_fun
and by
to .fn
and .by
respectively. See here for the reasons.vars_input
is also renamed as .input
.preproc()
accepts custom-defined functions and custom-defined configurations (input and output variable names) in a custom .json
file.game_info
data is moved to {data.iquizoo} package.wrangle_data()
back to {tarflow.iquizoo}, and then renamed preproc_data()
to preproc()
.game_info
data.wrange_data()
(originally in {tarflow.iquizoo}). Now it removes duplicates from data.refframe()
of distance error aggregating both conditions.span()
when number of correct responses can be recovered from columns "stim"
and "resp"
. Use it with care, for it does take order into consideration!cpt()
.cpt()
preprocessing function.preproc_data()
to make sure it restores all the attributes from input data
.nback()
. Specifically, you can now use "filler"
and "target"
keywords."filler"
and "Filler"
are both okay for now, but only "Filler"
is errorproof for previous versions.game_info
, mostly are questionnaires (#27, thanks to @Blockhead-yj).preproc_data()
with an input argument of by
(#23, #25).game_info
data.conflict()
, which is defunct for many versions.NA
s as NA_real
.?tarflow.iquizoo:::check_resp_metric
to see details. Note this function is not exported for now.game_info
, which stores games information of preprocessing functions. Run ?game_info
in R to read more details.london()
when user did not respond and the data still scored the user as correct.london()
. Now the score is normal only when the response rate is no less than 80%.complexswitch()
, now switchcost()
will also return abnormal result (all NA
s) if at least one block has no responses.multisense()
when users did not respond to sound trials.wxpred()
after filling NA
values to name_block
(mostly is just Block
) column.driving()
when minus signs, which are confused with hyphens, are logged into data.complexswitch()
will return abnormal result (all NA
s) if at least one block has no responses.calc_sdt()
to calculate signal detection theory related indices.drm()
.NEWS.md
file to track changes to the package.pc_all
index, meaning overal percent of correct, to wxpred
function.Add the following code to your website.
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