Changelog

mnirs 0.8.0

The initial release of analyse_kinetics() and family of kinetics modelling functions!

`analyse_kinetics()`

analyse_kinetics() fits oxygenation response kinetics with parametric and non-parametric methods. It accepts a single “mnirs” data frame, a list of data frames, or a grouped data frame, analyses nirs_channels in each interval, and returns a formatted table of results.
See ?analyse_kinetics for the canonical (i.e. human-verified) documentation of all methods, arguments, and returned objects.

analyse_kinetics(
    data,
    nirs_channels = c(smo2_left, smo2_right),
    method = "monoexponential",
    use_TD = TRUE,      ## use time delay parameter
    direction = "auto"  ## auto-detect response direction
) |> 
    print() |>  ## the formatted table prints the coefficients
    plot()      ## plot observations & fitted data

method argument selects the kinetics model. Each has its own additional arguments:
- "response_time" — non-parametric fractional (e.g. 50%) response time.
- "peak_slope" — peak rolling linear least-squares regression slope.
- "monoexponential" — 3- or 4-parameter exponential curve fit via stats::nls().
- "exponential_drift" — two-phase fast monoexponential primary response plus slow linear secondary drift.
- "biexponential" — two-phase fast primary and slow secondary exponential phases.
- "sigmoidal" — 4-parameter symmetric generalised logistic or Gompertz-family curve.
- "sigmoidal_drift" — two-phase fast sigmoidal primary response plus slow linear secondary drift.
Most arguments can be supplied globally or per-channel and per-interval. However, method itself currently only accepts a single global model for all channels.
Results are returned as a structured list of class “mnirs_kinetics”, containing:
- method: the selected kinetics model.
- model: the lm or nls objects.
- coefficients: resultant model parameters.
- data: the input data augmented with *_fitted columns per nirs_channel.
- interval_times: start_times and end_times of the analysed intervals.
- diagnostics: fit quality and model validation parameters used to evaluate and compare model fits.
- channel_args: selected per-channel and per-interval args.
- warnings: any warning and error messages generated during fitting.
- call: the matched call.
print.mnirs_kinetics() returns a formatted coefficients table, and plot.mnirs_kinetics() displays the observed data overlaid with fitted curves for each channel and interval.

Vector-level and model functions

The individual fitting methods called by analyse_kinetics() can be called directly outside of the “mnirs” data structure:

response_time() and peak_slope() estimate kinetics directly from a numeric vector x over t, and return a named list of coefficients (with the lm model object for peak_slope()).

peak_slope(x, t, width = 5, direction = "auto")

response_time(x, t, response_fraction = c(0.5, 0.632))

monoexponential(), exponential_drift(), biexponential(), logistic(), gompertz(), gompertz_left(), and sigmoidal_drift() contain the parametric equations for each model response curves. They can be used to construct a pure curve from explicit parameters, to simulate data, or plotting a fitted model.

t <- 1:100
monoexponential(t, A = 10, B = 100, tau = 8, TD = 15)

sigmoidsl(t, A = 10, B = 100, xmid = 30, slope = 4)

SSmonoexponential(), SSexponential_drift(), SSbiexponential(), SSlogistic(), SSgompertz(), SSgompertz_left(), and SSsigmoidal_drift() are the matching self-starting (selfStart) wrappers, which generate their own initial parameter estimates and can be fit directly with stats::nls().

nls(x ~ SSmonoexponential(t, A, B, tau, TD), data = data)

nls(x ~ SSlogistic(t, A, B, xmid, slope), data = data)

mV̇O₂ recovery kinetics and muscle Oxidative Capacity assessment

An emerging method using in mNIRS research, a series of repeated brief occlusions can be used to estimate the recovery rate of muscle oxygen uptake from NIRS channels, as a proxy for muscle oxidative capacity. This method can be performed in {mnirs} using recursive calls to analyse_kinetics():

A sequence of data frames containing occlusion intervals (i.e. extracted with extract_intervals()) can be passed to analyse_kinetics(method = "peak_slope") with appropriate arguments.
The result can be passed directly to another call of analyse_kinetics(), with resulting coefficients supplied explicitly as time_channel and nirs_channels. method will usually be selected as "monoexponential" to determine the rate constant (k) of mV̇O₂ recovery (also see Articles below).

## fit an exponential through the peak slopes of successive occlusions
analyse_kinetics(
    occlusion_intervals,
    nirs_channels = hhb,
    method = "peak_slope",
    span = 3,
) |> 
    print() |>  ## print intermediate results and pass along
    analyse_kinetics(
        nirs_channels = slope,
        time_channel = peak_slope_time,
        method = "monoexponential",
        group_intervals = list(trial1 = 1:10, trial2 = 11:20)
    )

Correcting for blood volume changes

correct_blood_volume() is used to normalise NIRS components signals — i.e. oxy[haem] and deoxy[haem] — for changes in total[haem], which is a proxy for local blood volume/perfusion. This can be done before further analysis, to isolate metabolic O₂ from mechanical haemodynamics. See ?correct_blood_volume.

Articles

“Analysing muscle oxidative capacity with mnirs” walks through a full arterial occlusion OxCap analysis: correcting for blood volume, extracting occlusion intervals, finding peak deoxy[haem] slopes, and fitting a monoexponential through the slope estimates to estimate the mV̇O₂ recovery rate constant k.
“Reading and analysing PIONIRS data with mnirs” demonstrates reading new TD-NIRS .ftn & .ftn2 files, and compares different fit methods to occlusion reoxygenation kinetics.

mnirs 0.7.2

`read_mnirs()`

Reading all files, in particular .csv, is faster and allocates less memory.
Files can now be read from PIONIRS NIRSBOX, an advanced time-domain TD-NIRS device.
- PIONIRS explors file types .ftn and .ftn2 for single- and dual-channel TD-NIRS, respectively.
- read_mnirs() will automatically detect channels StO2, Time, and TagLabel.
- Example file pionirs_occlusion.ftn2 can be called with example_mnirs() (Thanks to Marianna, Dr. Porcelli, and PIONIRS for the demo files).

example_mnirs("pionirs")
#> [1] "<R library>/mnirs/inst/extdata/pionirs_occlusion.ftn2"

Artinis Oxysoft file exports are now automatically read more consistently, using the file metadata and Legend to rename channels:
- “(Sample number)” (column 1) is renamed “sample” with a derived “time” column which is set to time_channel.
- “(Event)” (the last numbered column with event markers) is renamed “event” and is set as event_channel. The trailing un-numbered column with event labels is renamed “labels”, and can be explicitly renamed: event_channel = c(labels = "labels").
- All other channels in the Legend are renamed and returned as nirs_channels by default with clean, lower case names (e.g. “Rx1 - Tx1 O2Hb” is renamed as “rx1_tx1_o2hb”).
- Channels can be renamed from either their literal Legend names; e.g. nirs_channels = c(o2hb = 2), c(o2hb = "rx1_tx1_o2hb"), or c(o2hb = "Rx1 - Tx1 O2Hb").
create_mnirs_data() can now rename nirs_channels, time_channel, and event_channel and add the renamed column names to metadata.

df <- create_mnirs_data(
    PIONIRS_ftn2,
    nirs_channels = c(o2hb = "O2Hb(CH1)", hhb = "HHb(CH1)", thb = "THb(CH1)"),
    time_channel = c(time = "Time"),
    event_channel = c(labels = "TagLabel")
)

attr(df, "nirs_channels")
# [1] "o2hb" "hhb"  "thb"

Core processing functions

extract_intervals() now accepts a list of multiple start and/or end values with mixed by_time(), by_label(), by_lap(), or by_sample(). Intervals are matched by user-specified order.

## combine multiple specification types for one boundary
extract_intervals(
    data, 
    start = list(by_lap(2), by_time(400)),
    end = list(by_lap(3), by_label("10-min marker"))
)

extract_intervals() Also now properly retains event_channel column in ensemble-averaged intervals (group_intervals = "ensemble" or custom groups).
plot_mnirs(): small adjustments to plot spacing & point sizes.
palette_mnirs() now returns unnamed colours, which was disrupting use with ggplot2::scale_colour_manual().

custom_colours <- c(
    smo2_left_vl = palette_mnirs("pink"),
    smo2_right_vl = palette_mnirs("light blue"),
    smo2_left_rf = palette_mnirs("purple"),
    smo2_right_rf = palette_mnirs("dark blue")
)
# smo2_left_vl smo2_right_vl  smo2_left_rf smo2_right_rf 
#  "#ff80ff"   "#0080ff"   "#9f79ee" "#00468Bff" 

plot(result) +
    scale_colour_manual(
        values = c(
            smo2_left_vl = palette_mnirs("pink"),
            smo2_right_vl = palette_mnirs("light blue"),
            smo2_left_rf = palette_mnirs("purple"),
            smo2_right_rf = palette_mnirs("dark blue")
        )
    )

print.mnirs() now returns its object invisibly, so can be called incrementally within a function pipeline (which I just learned was possible!).

read_mnirs(...) |> 
    print() |>  ## intermediate view data frame
    extract_intervals(...) |> 
    print() |>  ## view returned list of data frames
    plot()      ## and plot those results

Package accessories

“README” and “Reading and Cleaning Data with mnirs” vignette edited with updated functionality and consistent formatting.
Included example “moxy_intervals.csv” modified “Lap” column coincides with intervals start & end, for testing with extract_intervals().

mnirs 0.7.1

shift_mnirs() now properly excludes partial windows at data edges, where fewer samples can bias calculation of “min” or “max” shift values on noise.
replace_mnirs(), shift_mnirs(), and filter_moving_average() should gain a serious performance improvement when calculating rolling means, at the cost of negligible loss of precision on the order of ± ~1e-11.
Lists of data frames exported from core functions now contain class = "mnirs" and should now plot() properly.

mnirs 0.7.0

CRAN release: 2026-08-01

Highlights

This minor version update includes mostly internal refactoring, but enough user-facing changes and a few breaking deprecations, so that it’s more than just a patch. This version lays the foundation for the incoming (hopefully soon) mnirs analyse_kinetics suite of functions.

Working with lists and grouped data frames

Core processing functions (resample_mnirs(), replace_mnirs(), filter_mnirs(), shift_mnirs(), and rescale_mnirs()) now accept as the data input either a single data frame, a list of data frames, or a grouped data frame (requires dplyr). Single data frames are processed and returned directly, as previously. Listed and grouped data frames are each processed independently and returned as a named list.
extract_intervals() now also accepts a list of data frames or a grouped data frame and returns one flattened, named list of extracted intervals.

replace_mnirs(
    data = list(df1, df2),
    outlier_cutoff = 3,
    span = 5
)
#> $interval_1
#> # A tibble:
#>     time  smo2    o2hb
#>    <dbl> <dbl>   <dbl>
#>  1   0    42.8 -0.0289
#>  2   0.1  42.8 -0.0524
#>  3   0.2  42.8 -0.0916
#>  4   0.3  42.9 -0.138 
#>  5   0.4  43.2 -0.205 
#> 
#> $interval_2
#> # A tibble:
#>     time  smo2  o2hb
#>    <dbl> <dbl> <dbl>
#>  1   9.9  51.7 -2.29
#>  2  10    51.7 -2.32
#>  3  10.1  51.8 -2.31
#>  4  10.2  52.2 -2.22
#>  5  10.3  52.4 -2.12

Channel grouping and processing

BREAKING CHANGE: extract_intervals(), shift_mnirs(), and rescale_mnirs() gain argument group_channels, which separates channel selection from channel grouping. nirs_channels selects channels; the new group_channels argument channels specifies “distinct”, “ensemble”, or custom channel groupp construction.

shift_mnirs(
    data,
    nirs_channels = c(smo2, o2hb),
    group_channels = "ensemble",
    to = 0,
    span = 5
)

filter_mnirs() and replace_mnirs() now accept named lists for channel-specific processing arguments, in addition to a single global value as previously. rescale_mnirs() and shift_mnirs() accept processing arguments specified by channel or group. This way each nirs_channel can be processed with unique parameters.

filter_mnirs(
    data,
    nirs_channels = c(smo2, o2hb),
    method = list(smo2 = "moving_average", o2hb = "butterworth"),
    span = 5,     ## only used by "moving_average" channels
    order = 2,    ## only used by "butterworth" channels (and below args)
    W = 0.02,
    type = "low",
    na.rm = TRUE
)

Function and argument renaming

filter_butterworth() and filter_moving_average() are now the renamed canonical functions. filter_butter() and filter_ma() remain available as aliases.
BREAKING CHANGE: extract_intervals() new argument group_intervals replaces deprecated event_groups. This is where interval grouping is specified for “distinct” or “ensemble”-averaged, or custom group list construction.

interval_list <- extract_intervals(
    data,
    nirs_channels = c(smo2_left, smo2_right),
    group_intervals = "ensemble",
    start = by_time(368, 1084),
    span = c(-20, 90)
)
#> $ensemble
#> # A tibble:
#>     time smo2_left smo2_right
#>    <dbl>     <dbl>      <dbl>
#>  1 -20        56.3       59.2
#>  2 -19.9      56.1       59.2
#>  3 -19.8      56.1       59.2
#>  4 -19.7      56.2       58.9
#>  5 -19.6      56.4       58.9

Performance improvements & bug fixes

`read_mnirs()`

Improved reading of delimited files containing extra spaces around quoted values.
Documented support for .txt files.
Made automatic device and channel detection more efficient and improved related messages.
nirs_channel now identified before type conversion, ensuring coercion to numeric.
Added a warning when all values in nirs_channels become missing during numeric coercion.
Improved handling of timestamp values; absolute date-times, time-only values, header timestamps, and POSIXct-type time_channel column.
Refined sample_rate detection and warnings for irregular samples.

`create_mnirs_data()`

Grouped data now remain grouped when mnirs metadata is added or refreshed.

`resample_mnirs()`

data arg now accepts a list or grouped data frame and returns one processed data frame per data frame.
Clarified documentation that downsampling numeric columns uses averages based on linear interpolation, rather than time-weighted averages.

`replace_mnirs()`

data arg now accepts a list or grouped data frame and returns one processed data frame per data frame.
Arguments can be specified per-nirs_channels as named lists.
Improved performance via local-median calculations when values are missing.
Made fixed-width outlier detection faster by calculating rolling medians across many windows together.
Added clearer checks for invalid window settings and unsorted or missing time values.

`filter_mnirs()`

data arg now accepts a list or grouped data frame and returns one processed data frame per data frame.
Arguments can be specified per-nirs_channels as named lists.
Filter settings such as spar, W, fc, width, and span are now explicit function arguments, making available options easier to discover.
Simplified filter selection so each channel is sent directly to its chosen filter method.
filter_butterworth() and filter_moving_average() are now the main worker functions; filter_butter() and filter_ma() remain available as aliases.
Improved checks and messages for missing values, cutoff frequencies, window sizes, and unsupported settings.

`shift_mnirs()`

data arg now accepts a list or grouped data frame and returns one processed data frame per data frame.
Arguments can be specified per-nirs_channels as named lists, or per-named group via group_channels (see below).
BREAKING CHANGE: Channel grouping through a list to process channels together is now supplied to group_channels. Supplying a list to nirs_channels is deprecated.
nirs_channels now defaults to NULL, allowing channels to be taken from mnirs metadata as elsewhere in the package.

`rescale_mnirs()`

data arg now accepts a list or grouped data frame and returns one processed data frame per data frame.
Arguments can be specified per-nirs_channels as named lists, or per-named group via group_channels (see below).
BREAKING CHANGE: Channel grouping through a list to process channels together is now supplied to group_channels. Supplying a list to nirs_channels is deprecated.
nirs_channels now defaults to NULL, allowing channels to be taken from mnirs metadata as elsewhere in the package.

`extract_intervals()`

data arg now accepts a list or grouped data frame and returns one named list with interval names that identify their source data frame and interval sequence number.
Arguments can be specified per-nirs_channels as named lists.
BREAKING CHANGE: Interval grouping for ensemble-averaging is now supplied to group_intervals, renamed from event_groups. event_groups is now deprecated.
BREAKING CHANGE: Channel grouping through a list to process channels together is now supplied to group_channels. Supplying a list to nirs_channels is deprecated.
Added checks for interval groups and their selected channels, with clearer errors for invalid group numbers, unknown channels, and invalid time boundaries.
Updated ensemble averaging so each interval group can use its own per-channel argument selection.
Improved naming, zero-time handling, and preservation of mnirs metadata in extracted intervals.

`plot.mnirs()`

data arg accepts a list or grouped data frame and returns facetted plots per data frame (existing functionality since mnirs 0.6.3, documenting for clarity).
Time (x-) axis now displays units mm:ss for data lasting less than one hour and h:mm:ss for longer data.
Facets now follow the order of intervals in the input instead of being reordered alphabetically.
Improved error messages for empty or invalid lists of data frames.

Internal validation & shared functions

Improved errors and warnings package-wide so they point to the function called by the user rather than an internal checking function.
Added as_data_list.R containing shared support for processing data argument from one data frame, a list of data frames, or a grouped data frame. Existing mnirs metadata is kept for each interval.
Added channel_args.R containing shared support for setting processing options separately for each nirs_channel or per-group (for shift_mnirs(), replace_mnirs(), and extract_intervals()) as a list named by nirs_channels. One (vector) value can still be supplied to all channels by default.
Added clear warnings for unknown channel names and errors for conflicting values within a group.

mnirs 0.6.5

CRAN release: 2026-06-02

`read_mnirs()`

read_mnirs() can now handle c(".txt", ".tsv") files via the same data.table::fread() pathway as ".csv". There are occasionally still odd file formats where columns will not be properly detetcted, usually where files are designed to be human-readible not machine-readable.
read_mnirs(nirs_channels = NULL) now automatically returns all channels starting with “SmO2” (case insensitive), which is the most common NIRS channel name for wearable mNIRS devices. Previously, only the first detected nirs channel was returned.

## read an mNIRS file with two "smo2" channels
df <- read_mnirs(file_path = example_mnirs("moxy_ramp"))

attr(df, "nirs_channels")
#> [1] "SmO2 Live"    "SmO2 Live(2)"

Core function updates

extract_intervals() helper function by_label() matches character strings as regular expressions (regex) by default. Now more clearly documents this, and accepts two additional arguments for better handling character strings, which are both FALSE be default and must be explicitly opted into (see ?grep):
- by_label(ignore_case = TRUE) ignore case when evaluating strings.
- by_label(fixed = TRUE) treat labels as fixed strings rather than regular expressions. Useful when labels contain regex metacharacters (., *, (, etc.).
plot.mnirs() internal update to pass additional arguments n.breaks to y-axis breaks, and breaks to x-axis breaks.
format_hmmss() can now display fractional seconds values.
replace_mnirs(), resample_mnirs(), and some other functions return more informative error message when time_channel has irregularly sampled values (time series must be monotonically increasing, non NA).

mnirs 0.6.4

CRAN release: 2026-05-15

Bug fix

Fix a memory allocation bug with resample_mnirs. Downsampling 100k rows now allocates ~11MB rather than ~4.5 GB.

mnirs 0.6.3

CRAN release: 2026-05-07

Plotting improvements

Generic plot.mnirs() ncan now be called on a list of “mnirs” data frames. Each data frame will be printed as a facet. This is primarily useful for printing a list of interval data frames exported from extract_intervals():

read_mnirs() |> 
    extract_intervals() |> 
    plot()
## returns a plot with a facet for each interval

To faciliate this, extract_intervals() now returns a list of data frames with class = "mnirs". Otherwise, to manually plot a list of data frames, it will need to have class(list) <- c("mnirs", class(list)) edited manually.

read_mnirs() |> 
    extract_intervals() |> 
    class()
#> [1] "mnirs" "list"

Backend improvement: print.mnirs() generic created to avoid displaying extra NextMethod() calls when printing lists with class = "mnirs".

Modified lap extraction behaviour

extract_intervals():
- When specifying start and end with by_lap(), start will only refer to the first sample of the lap, and end the last sample.
- With previous behaviour, start = by_lap() would include the entire specified lap(s). But this resulted in less control over displaying only parts of a lap.
- Updated behaviour is now more consistent with by_time, by_label, and by_sample() methods, which reference start and end from a single sample. This allows extraction of e.g. only the first 60-sec of lap:

read_mnirs() |> 
    extract_intervals(
        start = by_lap(1, 3),
        span = c(0, 60),
    )
## returns a list of two intervals with the first 60-sec of laps 1 and 3, respectively.

Updated core functions

read_mnirs(): Fix detection issue with “PerfPro” file formats, and another small bug fix to improve timestamp parsing.
create_mnirs_data() now properly accepts tidy evaluation for nirs_channels, time_channel, and event_channel:

create_mnirs_data(df, nirs_channels = c(o2hb, hhb))

resample_mnirs():
- Now resamples to an inclusive time range around existing data, rounded to the nearest resampled rate. Better handles edge cases where the last sample was being dropped in certain rounding conditions.
- Fix an edge case error when sample_rate was mis-specified higher than the actual sample_rate of the data. Now more robustly fills non-numeric columns.

mnirs 0.6.2

CRAN release: 2026-04-18

Core updates

read_mnirs():
- Now properly parses time_channel as fractional unix values; i.e. timestamp (e.g. “hh:mm:ss”) values are saved by Excel in all its infinite wisdom as numeric fractional Unix timestamps. Will now be properly coerced to numeric and POSIXct timestamp values can be returned.
- Timestamps should now be returned in the user’s local time zone.

Core function argument changes

resample_mnirs(): Update default method = "none". Less opinionated default to force users to explicitly opt-in to specifying either “linear” or “locf” methods to fill/interpolate across new samples. Updated package documentation.
replace_invalid(), replace_outliers(), replace_missing(): Remove bypass_checks arg intended for internal use only, to bypass redundant checks when calling from replace_mnirs().

Small edits

plot.mnirs(): No longer coerces to long format data behind the scenes.
plot.mnirs(): y-axis title changed from “signal” to “mNIRS”.
Fix lap marker inconsistency in train.red_intervals.csv. Updated relevant interval times in documentation.
README.md & “reading-mnirs-data.qmd” vignette updates.
- Update recommended core processing sequence: read_mnirs() -> resample_mnirs() -> replace_mnirs() -> …
- Update extract_intervals() vignette to train.red_intervals.csv end-interval reoxygenation events.
Small documentation changes.

mnirs 0.6.1

Bug fixes

Core functions updated to accept nirs_channel args as a list.
- An info message will be displayed when a list is not required, instead of erroring.
- Additional info messages will be displayed for shift_mnirs(), rescale_mnirs(), and extract_intervals() when nirs_channels has not been specified as a list, nor retrieved from metadata. As a reminder of the grouping requirements in those functions.
Core functions now properly update nirs_channels metadata when re-specified.
- Previously, specifying nirs_channels in a function would only add any additional column name strings to the existing metadata rather than overwrite it. Meaning nirs_channels could only be removed from metadata by using create_mnirs_data(nirs_channels = "..."). The updated behaviour should mean channels need to be re-specified less often.
filter_mnirs(): Fixed an error matching method arguments when left blank.
README and “Reading and Cleaning Data with {mnirs}” vignette updated to fix typos and small edits.

mnirs 0.6.0

CRAN release: 2026-03-30

Prepare for CRAN submission

NEWS.md is truncated to the most recent relevant release updates. The full NEWS.md remains in the dev branch.
Some development functions have been omitted from the package build in expectation of CRAN review. They are still present in dev branch and can be installed with pak::pak("jemarnold/mnirs").

Updated core functions

filter_mnirs(): Method-specific arguments (e.g. order for method = "butterworth", or width for method = "moving_average") removed from the generic function call. Continue to be passed to the appropriate method via .... Documentation and info/warning/abort messages updated.
filter_ma() better separates effects of partial and na.rm args:
- partial = FALSE by default returns NA at edges where insufficient number of samples are available compared to the specified width or span.
- partial = TRUE calculates mean values at edges, as long as one valid non-NA sample is available.
- na.rm = FALSE by default behaves as expected with mean(na.rm = FALSE), propagating any NAs in the local window to the calculated mean with a warning.
  - NOTE This differs from the behaviour of na.rm = FALSE in filter_butter(), which errors if there are any internal NAs present. This has not been changed.
- na.rm = TRUE ignores NAs and calculates local means as long as one valid sample is present.
plot.mnirs()
- Add new arg: points = TRUE will plot points in addition to lines, as a useful quick shortcut.
- Update na.omit now omits non-valid c(NA, NaN, Inf, -Inf) values from plotting, not just NA.
palette_mnirs() can now accept either a single numeric value specifying the number of colours to return, or any number of (valid) character colour names.

mnirs 0.5.2

Shiny app

Update online shiny app hosted at https://jemarnold-mnirs-app.share.connect.posit.cloud/ with basic reading and pre-processing functionality.

mnirs 0.5.1

Updated core functions

read_mnirs() now has better automated channel detection logic for “Artinis” / “Oxysoft” file formats. nirs_channels and time_channel can be left blank to automatically “sample”, “time” (from sample_rate), and nirs channel “2”.
replace_outliers() now accepts numeric outlier_cutoff values for more precise outlier detection thresholds (previously was integers only). Documentation also updated.

mnirs 0.5.0

Updated core functions

read_mnirs() expands event_channel to work with integer “lap” numbers, or character event label as previous.
- Should now work on more .csv file formats; previously read errors may have occured where the file contained header rows above the data table, resulting in improper detection of columns.
- event_channel can now be specified as an integer lap column, in addition to a character column as previous.
- Other {mnirs} functions may expect event_channel to be either character or integer-ish.
extract_intervals():
- Function arguments start and end are used to specify one or both of a start and end point to the target interval.
- Specify start/end values with helper functions by_time(), by_label(), by_lap(), and by_sample().
- Numeric values are automatically coerced to “time” values; Explicit integer values (e.g. 2L) are coerced to “lap”; Character strings are coerced to event “label”.

Package resources

A rough draft {mnirs} hex icon and package cheatsheet have been added.

mnirs 0.4.2 and prior

Full previous changelog is available on the github dev branch: https://github.com/jemarnold/mnirs/blob/dev/NEWS.md

mnirs 0.8.0

analyse_kinetics()

Vector-level and model functions

mV̇O2 recovery kinetics and muscle Oxidative Capacity assessment

Correcting for blood volume changes

Articles

mnirs 0.7.2

read_mnirs()

Core processing functions

Package accessories

mnirs 0.7.1

mnirs 0.7.0

Highlights

Working with lists and grouped data frames

Channel grouping and processing

Function and argument renaming

Performance improvements & bug fixes

read_mnirs()

create_mnirs_data()

resample_mnirs()

replace_mnirs()

filter_mnirs()

shift_mnirs()

rescale_mnirs()

extract_intervals()

plot.mnirs()

Internal validation & shared functions

mnirs 0.6.5

read_mnirs()

Core function updates

mnirs 0.6.4

Bug fix

mnirs 0.6.3

Plotting improvements

Modified lap extraction behaviour

Updated core functions

mnirs 0.6.2

Core updates

Core function argument changes

Small edits

mnirs 0.6.1

Bug fixes

mnirs 0.6.0

Prepare for CRAN submission

Updated core functions

mnirs 0.5.2

Shiny app

mnirs 0.5.1

Updated core functions

mnirs 0.5.0

Updated core functions

Package resources

mnirs 0.4.2 and prior

`analyse_kinetics()`

mV̇O₂ recovery kinetics and muscle Oxidative Capacity assessment

`read_mnirs()`

`read_mnirs()`

`create_mnirs_data()`

`resample_mnirs()`

`replace_mnirs()`

`filter_mnirs()`

`shift_mnirs()`

`rescale_mnirs()`

`extract_intervals()`

`plot.mnirs()`

`read_mnirs()`