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actiread with
except = "tzoffset_to_tz", so actibase can
export tzoffset_to_tz() later without an import
conflict.GGIR is no longer listed in Suggests; it
was not used.agcounts and stepcount now use
requireNamespace(pkg, partial = TRUE) instead of
rlang::check_installed()/rlang::is_installed(),
so their .onLoad hooks (which initialize Python via
reticulate) do not run during the check. This fixes the
CRAN NOTE in “checking dependencies in R code”.acti_sleep_hdcza(),
acti_sleep_hlrb(), acti_sleep_horangle(),
acti_sleep_l5(), acti_sleep_notworn(),
acti_sleep_setwindow(), and
acti_sleep_diary(), dispatched by
acti_sleep_guider() (see
acti_sleep_guider_requirements()).
acti_sleep_sib() detects sustained inactivity bouts,
acti_sleep_label() and acti_sleep_labels()
turn guiders into epoch-level sleep/wake labels,
acti_sleep_fuse_labels() combines labels with STAPLE, and
acti_sleep_ensemble() runs the whole pipeline from raw or
epoch data.acti_threshold_counts() and
acti_threshold_enmo() for user-supplied cut-points, and
published cut-points in acti_threshold_montoye(),
acti_threshold_sasaki(),
acti_threshold_romanzini(), and
acti_threshold_hildebrand() (with
acti_threshold_hildebrand_wrist() and
acti_threshold_hildebrand_hip()).acti_calculate_wear_raw() estimates non-wear from
raw tri-axial acceleration, implemented without calling GGIR or actipy.
Like acti_calculate_wear(), it takes a method
argument and passes ... to the method function:
acti_calculate_wear_raw_ggir()
(method = "ggir", default): the GGIR non-wear and clipping
algorithm (van Hees et al. 2011, 2013), exposing GGIR’s settings: the
medium/long window sizes, the "2023" and
"2013" approaches, the standard-deviation and range
thresholds, the clipping threshold or dynamic range, edge correction,
and the night-time short non-wear filter.acti_calculate_wear_raw_actipy()
(method = "actipy"): stationary episodes of Doherty et
al. (2017), as in actipy’s detect_nonwear(), with the
window, patience, and standard-deviation threshold exposed.acti_calculate_wear() remains the count-based
(Choi/Troiano) method.acti_process() frees the raw data once counts are
calculated, before non-wear detection and day inclusion.
acti_calculate_stepcount() (and
py_acti_calculate_stepcount()) now return times in the
input’s time zone. stepcount reports local clock times
without a time zone, which were previously labelled UTC, shifting steps
by the input’s UTC offset (e.g. one hour for British Summer Time). A new
tz argument sets the time zone, which is needed for file
input (default "UTC").
epoch/unit arguments are standardized
with actibase::acti_standardize_epoch(), so
acti_calculate_counts() accepts
epoch = "1 min" and the measure functions accept numeric
seconds or strings such as "30s".
ensure_all_time = TRUE no longer errors for units such as
"5 seconds" or "1 minute", and
acti_calculate_mims() no longer errors for units without a
space such as "1min".
acti_calculate_ai() and
acti_calculate_mad() now default to
verbose = TRUE.
acti_apply_cole_kripke(),
acti_apply_sadeh(), and
acti_apply_tudor_locke() no longer add a superfluous
vector.magnitude column (a copy of counts) to
their output.
acti_apply_cole_kripke() and
acti_apply_sadeh() no longer return the count
column that actigraph.sleepr uses internally for scoring
(axis1 truncated and, for Cole-Kripke, divided by 100). It
overwrote any count column in the input, which is now
kept.
acti_calculate_measures() uses
acti_flag_qc_all() when actibase provides it and
flag_qc_all() otherwise, since actibase 0.8.0 renames
it.acti_process() and acti_process_study()
now aim to match ActiLife for GT3X data: files are read with
digits = 8 (unless digits is given), resampled
to 30Hz, then rounded to 3 digits half away from zero, as ActiLife does.
Previously the data were read at 5 digits and not rounded, giving counts
that differed from ActiLife by 1-3 in many epochs. Rounding 5-digit data
is not a fix: it rounds twice and differs from ActiLife more often.
Counts from GT3X files will change slightly.acti_calculate_counts() and acti_process()
gain round_digits to round
X/Y/Z half away from zero (base
round() rounds 0.0625 to 0.062)
after resampling and before counting. In acti_process(),
the default NULL uses 3 for GT3X data and no rounding
otherwise; NA turns it off.acti_apply_cole_kripke(),
acti_apply_sadeh(), and
acti_calculate_wear(use_magnitude = FALSE) now error if
axis1 is missing. Both sleep algorithms score the vertical
axis, so vector-magnitude counts are no longer silently converted with
axis1 = VM / sqrt(3).acti_calculate_wear() errors with an informative
message unless the data are 60-second epochs (required by Choi and
Troiano).acti_calculate_stepcount(),
acti_calculate_verisense(),
acti_calculate_forest(), acti_calculate_sdt())
now return NA for epochs where all values are missing
(e.g. non-wear) rather than 0 steps; walking from
acti_calculate_stepcount() is likewise
NA.allow_truncation = TRUE,
acti_calculate_auc() and
acti_calculate_fast_mims() mark an epoch with any
out-of-range axis as -0.01 (all axes and the total), as
MIMSunit does, rather than adding the -1
sentinel into the total.acti_process_study() processes every supported raw
file (GT3X, CWA, GENEActiv BIN, CSV) in a directory with
acti_process() and saves RDS files. Failures are always
reported with a warning and listed in the failed attribute
of the result. CSV files are read with readr, which is now
in Imports.acti_apply_tudor_locke() gains sleep_col
to use a sleep-state column with another name (e.g. from
acti_apply_tudor_locke_diary()).acti_calculate_ai() has its own documentation page
describing the (noise-unadjusted) Activity Index of Bai et
al. (2016).acti_calculate_counts() subsets to
time/X/Y/Z before
calling agcounts, whose start-of-record padding failed on
data with extra columns (e.g. GENEActiv
light/temperature).agcounts keeps all
columns, attributes, and the timezone, builds the padded times on an
exact sample grid, and no longer re-prefixes the existing transformation
history. It is kept by default alongside the upstream fix
(bhelsel/agcounts#51), as it also avoids agcounts’ duplicate zero-count
rows and works with older agcounts.acti_calculate_wear() now passes ... to
actigraph.sleepr (arguments such as
min_period_len were ignored) and is not affected by
existing wear columns.mims_default_extrapolation(),
mims_default_interpolation(), and
mims_default_filtering() now record their
transformations.acti_calculate_ai() skips 1-second windows with a
single sample instead of making the whole epoch NA, and
warns for data sampled under 2 Hz.acti_calculate_verisense() validates
method with match.arg(), so the default is
"original".acti_calculate_fast_mims() uses the sample rate of the
processed (100 Hz) data; a supplied sample_rate is ignored
with a warning.sample_rate in
acti_calculate_measures() is now passed through to the
activity-count calculation, and a missing sample rate gives an
informative error from
acti_calculate_counts(resample = FALSE)."30s" and
"1min" are parsed with lubridate, consistent
with lubridate::floor_date().py_acti_calculate_stepcount() and
py_acti_calculate_forest() return their result
visibly.aggregated_at_ transformation label and
documentation of verbose and lfe_select.Add sleep diary labeling for Tudor–Locke and adapt diary labels
for actigraph.sleepr::apply_tudor_locke().
Declare actigraph.sleepr as an installation
dependency for sleep wrappers.
Fixing bug with acti_calculate_mims due to not
subsetting XYZ.
agcounts bug from
https://github.com/bhelsel/agcounts/issues/50.globalVariables.SSQ_PARALLEL=0 before import
for forest.actisleepThese 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.
Health stats visible at Monitor.