Preparing Your Data for PACTS

This vignette covers getting data into the shape pacts_scaling() expects, and choosing which of its output variables to model. For what PACTS is and why continuous cycle time outperforms day counting, see vignette("menstrualcycleR-overview") or the visual explainer.

library(menstrualcycleR)
library(dplyr)

Input format

pacts_scaling() takes one long-format table with four required columns.

Column	Contents	Notes
`id`	Person identifier	Any type. One person may contribute many cycles.
`date`	Date of the observation	Class `Date`, not character.
`menses`	`1` on the first day of bleeding, `0` otherwise	Not every bleeding day. See Section 3.
`ovtoday`	`1` on the estimated day of ovulation, `0` otherwise	Required as a column even without an ovulation biomarker. See Section 4.

Outcome columns sit alongside these. pacts_scaling() does not modify them; it appends 18 new columns, of which four are the cycle-time variables covered in Section 7.

head(cycledata, 4)
#>   id menses ovtoday symptom  daterated
#> 1  1      1       0       5 2024-01-20
#> 2  1      0       0       5 2024-01-21
#> 3  1      0       0       3 2024-01-22
#> 4  1      0       0       2 2024-01-24

One row per person per day

No duplicate id-date pairs. Where a participant submitted two diary entries on one day, resolve them to a single row before scaling. Two rows for one date means that day is counted twice in every downstream model.

dups <- cycledata %>%
  count(id, daterated) %>%
  filter(n > 1)
nrow(dups)
#> [1] 0

Gaps need no filling. pacts_scaling() fills the calendar internally so that cycle position is defined for every date inside a cycle. It therefore returns more rows than it was given, and the added rows carry a date and a cycle-time value but no outcome. cycledata goes in with 619 rows and comes out with 744, so 125 rows (17% of the output) have no symptom value. Count coverage over rows where the outcome is observed, not over all rows.

scaled <- pacts_scaling(cycledata, id = id, date = daterated,
                        menses = menses, ovtoday = ovtoday)

c(all_rows             = nrow(scaled),
  rows_with_an_outcome = sum(!is.na(scaled$symptom)))
#>             all_rows rows_with_an_outcome 
#>                  744                  619

Any per-person day count or sparsity threshold should use the second number.

The lower_cyclength_bound / upper_cyclength_bound arguments default to 21 and 35 days and gate ovulation imputation only. A cycle with a confirmed ovulation is scaled whatever its length, subject to its phase lengths instead. Nagpal et al. (2025) §2.1.1 describes the bounds as deciding which cycles are scaled at all; the package applies the carve-out named in that section’s next sentence, and does so by default rather than on request. summary_ovulation() reports how many cycles fall outside 21-35 days. To exclude them, filter on mcyclength_complete after scaling.

Menses onset

menses == 1 marks the first day of a period; every later bleeding day is 0. Coding all bleeding days as 1 makes the function read a four-day period as four cycle onsets, and nothing errors.

Exclude periovulatory spotting, which is not a menses onset and will start a phantom cycle. Reclassify post-midnight entries to the previous day before scaling, so the diary and any sleep or wearable data agree on which day is which.

Ovulation

ovtoday marks the estimated day of ovulation. Ovulation biomarkers are preferred, and the coding convention differs by method.

Method	Code `ovtoday = 1` on	Report
Urinary LH test	The day after the first positive test (LH+1)	Brand and threshold, e.g. 40 mIU/ml
Basal body temperature	The day after the temperature nadir	How temperature was measured
Daily hormone assay	See Nagpal et al. (2025)	Assay and analyte
None available	Pass `ovtoday = NULL`	The imputation rate (Section 7)

With only period-tracker dates, pass ovtoday = NULL. The column is created for you as all-NA, pacts_scaling() places ovulation 15 days before each next menses onset, records it in ovtoday_impute, and covers those cycles in the *_impute cycle-time columns. A message reports that this happened. Supplying your own column of all 0s or NAs does the same thing.

Omitting the argument altogether is an error rather than a silent fallback, so that forgetting it when you do have ovulation data cannot quietly produce an all-imputed analysis. Against 33 hormone-confirmed cycles the imputed estimate was off by a mean absolute 0.97 days (Section 7.2).

From a period-tracker export to the PACTS table

The common starting point is one table of period start dates and a separate table of daily measurements.

period_log <- data.frame(
  id           = c(1, 1, 1, 2, 2, 2),
  period_start = as.Date(c("2024-01-03", "2024-01-31", "2024-02-27",
                           "2024-01-08", "2024-02-10", "2024-03-09"))
)

set.seed(1)
wearable <- data.frame(
  id   = rep(c(1, 2), each = 75),
  date = c(seq(as.Date("2024-01-01"), by = "day", length.out = 75),
           seq(as.Date("2024-01-05"), by = "day", length.out = 75)),
  resting_hr = round(c(rnorm(75, 58, 2), rnorm(75, 62, 2)), 1)
)

head(period_log, 3)
#>   id period_start
#> 1  1   2024-01-03
#> 2  1   2024-01-31
#> 3  1   2024-02-27
head(wearable, 3)
#>   id       date resting_hr
#> 1  1 2024-01-01       56.7
#> 2  1 2024-01-02       58.4
#> 3  1 2024-01-03       56.3

A left join turns the period log into a daily flag.

daily <- wearable %>%
  left_join(mutate(period_log, menses = 1L),
            by = c("id", "date" = "period_start")) %>%
  mutate(
    menses  = coalesce(menses, 0L),
    ovtoday = 0L          # no ovulation biomarker in this example
  )

daily %>% filter(menses == 1) %>% head(3)
#>   id       date resting_hr menses ovtoday
#> 1  1 2024-01-03       56.3      1       0
#> 2  1 2024-01-31       60.7      1       0
#> 3  1 2024-02-27       55.9      1       0

daily_scaled <- pacts_scaling(daily, id = id, date = date,
                              menses = menses, ovtoday = ovtoday)

daily_scaled %>%
  select(id, date, resting_hr, cyclic_time_impute, ovtoday_impute) %>%
  filter(!is.na(cyclic_time_impute)) %>%
  head(4)
#> # A tibble: 4 × 5
#>      id date       resting_hr cyclic_time_impute ovtoday_impute
#>   <dbl> <date>          <dbl>              <dbl>          <int>
#> 1     1 2024-01-03       56.3             0                   0
#> 2     1 2024-01-04       61.2             0.0769              0
#> 3     1 2024-01-05       58.7             0.154               0
#> 4     1 2024-01-06       56.4             0.231               0

Common device exports

Reshape each source into id, date, value form before running the join above.

Oura. Daily sleep and readiness endpoints already give one row per day. Use the sleep day field rather than the timestamp, so nights are attributed to the day the person woke.
Apple Health. Exports are per-sample. Aggregate to a daily value first, choosing explicitly between a daily mean, a nightly minimum, or a single reading. Apple Health also records MenstrualFlow, which can supply menses if reduced to the first day of each run of flow days.
Fitbit. Daily summaries arrive as one row per day per metric, in separate files. Join them on date before joining to the period log.
Period-tracker apps. Most export one row per bleeding day rather than one per period. Reduce to onsets first by keeping a day only when the previous calendar day was not also a bleeding day.

Checks before scaling

checks <- c(
  date_is_Date        = inherits(daily$date, "Date"),
  no_duplicate_rows   = !any(duplicated(daily[c("id", "date")])),
  menses_is_binary    = all(daily$menses %in% c(0, 1)),
  has_ovtoday_column  = "ovtoday" %in% names(daily),
  every_id_has_onset  = all(tapply(daily$menses, daily$id, sum) > 0)
)
checks
#>       date_is_Date  no_duplicate_rows   menses_is_binary has_ovtoday_column 
#>               TRUE               TRUE               TRUE               TRUE 
#> every_id_has_onset 
#>               TRUE

A FALSE on the last check usually means a person’s period dates failed to join, most often because the identifier is a factor in one table and a character in the other.

After scaling, cycledata_check() reports how much usable outcome data each person has in each phase, which feeds any inclusion rule.

It imposes no threshold. How much coverage a person needs before their scaled data support an estimate is an open question, along with power, cyclical clustering, and the reliability of per-person smooths. See “Outstanding Questions” in vignette("menstrualcycleR-overview").

Choosing a cycle-time variable

pacts_scaling() returns four cycle-time variables, crossing the anchor with whether imputed ovulation is included.

	Confirmed ovulation only	Imputed ovulation included
Centered on menses onset	`cyclic_time`	`cyclic_time_impute`
Centered on ovulation	`cyclic_time_ov`	`cyclic_time_imp_ov`

Switching anchor changes no row’s inclusion. Switching between confirmed-only and imputation-inclusive changed coverage from 358 rows to 735 in cycledata, and from 0 rows to 744 in a copy of it with ovtoday zeroed.

Anchor

Center on the event the outcome is tied to. Perimenstrual outcomes such as dysmenorrhea belong on the menses-centered variables, periovulatory outcomes such as reward sensitivity on the ovulation-centered ones. Nagpal et al. (2025) recommend fitting both in separate models where the expected timing is unclear.

Each variable places one anchor at zero and the other at the endpoints, -1 and +1. On cyclic_time, menses onset is at zero and ovulation is at the wrap; on cyclic_time_ov, the reverse. A feature sitting at the wrap is split across the two ends of the plotted axis, and any summary of where it falls has to be circular rather than linear, since an arithmetic mean of positions straddling -1 and +1 is meaningless. Nagpal et al. (2025) additionally flag, as an open limitation rather than a settled result, that centering on a hormonal transition may reduce sensitivity to change at the endpoints, and suggest alternative centering such as the mid-follicular phase as future work.

Fit both models when the outcome has features at both anchors, such as a perimenstrual rise and a periovulatory dip, and read each feature from the fit that centers it.

cyclic_time and cyclic_time_ov cover the same rows as each other, and so do cyclic_time_impute and cyclic_time_imp_ov. This held on cycledata and on a copy chopped to create a left-censored opening tail and an open trailing cycle.

Having no ovulation biomarker does not change which anchor to pick, but it does put a measured event at zero in one case and an estimated one at zero in the other. The imputed ovulation day is off by a mean absolute 0.97 days (Section 7.2). Spread over a follicular phase averaging 14.8 days, that is 0.066 of the 0-to-1 follicular span, so a feature narrower than about 0.07 on the axis cannot be located reliably in an ovulation-centered fit. State in the caption that zero is an estimated day.

Confirmed or imputed ovulation

In cycledata, 14 of 25 cycles have a confirmed ovulation. cyclic_time scales 358 of 744 rows and cyclic_time_impute scales 735. Zeroing ovtoday throughout drops cyclic_time to 0 rows and raises cyclic_time_impute to 744, because every cycle then takes the imputed path.

Run this on your own data before choosing.

vars <- c("cyclic_time", "cyclic_time_impute", "cyclic_time_ov", "cyclic_time_imp_ov")
sapply(vars, function(v) sum(!is.na(scaled[[v]])))
#>        cyclic_time cyclic_time_impute     cyclic_time_ov cyclic_time_imp_ov 
#>                358                735                358                735

Model cyclic_time or cyclic_time_ov when the confirmed-only coverage is enough for the analysis, and report the confirmation rate either way. When it is not, use the *_impute variables rather than dropping the unconfirmed cycles: the alternative to an imputed ovulation is a forward day count, which Nagpal et al. (2025) found had the highest within-person residual variance of the five time variables they compared.

Against 33 hormone-confirmed cycles, the day −15 backward count differed from the hormone-confirmed day of ovulation by a mean absolute 0.97 days (SD 0.88), with a mean signed difference of 0.30 days (SD 1.29). The error grew with cycle length in the same sample (r = 0.395, p = .023), so the estimate is least accurate in the longest cycles. PACTS has not been validated in samples with highly variable cycle lengths, frequent anovulation, or conditions that alter hormone dynamics.

Sensitivity to imputed ovulation

No confirmation rate has been established below which ovulation-anchored questions become unanswerable. The published guidance is to acknowledge the imprecision and report the proportion of cycles in which ovulation was estimated rather than measured.

Fit the model twice whenever any cycle in the analysis used an imputed ovulation: once on confirmed cycles only, once on the full set. Nagpal et al. (2025) report that their confirmed-only analyses agreed with the combined dataset, which is what justified pooling those 44 cycles. That agreement is a property of a particular sample, not of the method.

# The confirmed-only subset is the rows cyclic_time covers.
c(rows_confirmed_only = sum(!is.na(scaled$cyclic_time)),
  rows_incl_imputed   = sum(!is.na(scaled$cyclic_time_impute)))
#> rows_confirmed_only   rows_incl_imputed 
#>                 358                 735

Report both fits in the methods, including when they agree.

Extended-phase rows

This filter is package behavior rather than published method. When a cycle’s luteal or follicular phase falls outside the plausible bounds, the *_impute variables still cover it, using phase fractions computed without the upper phase-length cap. Those rows carry a flag.

strict <- subset(scaled, cyclic_time_impute_extended_phase == 0)

In cycledata this flag is set on 5.4% of scaled rows. The fallback only adds coverage and has no argument that disables it, so filter after scaling if you do not want those rows.

When your data is awkward

Everything above assumes a diary that behaves: a first period onset before the first rating, a closing onset after the last one, every cycle of a plausible length. Real diaries break all three, and pacts_scaling() has a setting for most of the ways they break. The package ships a second practice dataset for exactly this, in which every person is one awkward case, named in a case column.

cases <- unique(cycledata_special[, c("id", "case")])
writeLines(sprintf("%2d  %s", cases$id, cases$case))
#>  1  ordinary: three complete cycles, ovulation confirmed
#>  2  8 rated days before the first recorded menses onset
#>  3  30 rated days before the first onset: how many scale is a setting, not a cap
#>  4  confirmed ovulation among the leading days: scales with no setting
#>  5  confirmed ovulation at the end, no closing menses onset
#>  6  an 18-day cycle, ovulation not confirmed: under lower_cyclength_bound
#>  7  a 42-day cycle, ovulation not confirmed: over upper_cyclength_bound
#>  8  confirmed ovulation 22 days before the next onset: past luteal_phase_max_days
#>  9  confirmed ovulation 26 days after the onset: past follicular_phase_max_days
#> 10  confirmed ovulation 5 days before the next onset: under luteal_phase_min_days
#> 11  an 11-day hole in the diary: rows come back with no rating
#> 12  ovulation confirmed on no day: imputed columns only
#> 13  no menses onset and no confirmed ovulation: nothing can scale
#> 14  confirmed ovulation but no onset ever: one day scales, or a whole luteal phase
#> 15  both opt-in rules at once, at opposite ends of one diary

Run your own settings against it before switching them on for your data. The point is not that these people are unusual – in the CLEAR lab’s merged research data, 57% of participants have rated days before their first recorded period – but that in cycledata none of them occur, so nothing there shows a setting working or failing.

A setting recovers rows and data at different rates

An imputed anchor can fall earlier than a participant’s first rating, and the package then fills the calendar up to it. Those extra rows are output rows with no outcome on them, so “days recovered” and “data recovered” are two numbers, and the smaller one is the one to report.

off <- pacts_scaling(cycledata_special, id = id, date = daterated,
                     menses = menses, ovtoday = ovtoday)

on <- pacts_scaling(cycledata_special, id = id, date = daterated,
                    menses = menses, ovtoday = ovtoday,
                    impute_leading_ovulation = TRUE)

covered <- function(x) !is.na(x$cyclic_time_impute)

c(days_gained     = sum(covered(on)) - sum(covered(off)),
  with_a_rating   = sum(covered(on) & !is.na(on$symptom)) -
                    sum(covered(off) & !is.na(off$symptom)),
  rows_fabricated = nrow(on) - nrow(off))
#>     days_gained   with_a_rating rows_fabricated 
#>              45              31              12

The same holds for impute_next_menses, which closes a cycle forward from a confirmed ovulation that never got a closing period.

`leading_ovulation_luteal_days` is a placement, not a cap

The 15 is where the ovulation is put by default. It is not a ceiling on how far back the rule reaches, and nothing else bounds it either: the cycle-length bounds cannot, because a tail with no opening onset has no cycle length to test, and the phase-length caps are not consulted on this path at all.

sapply(c(5, 15, 25, 40), function(k) {
  x <- pacts_scaling(cycledata_special, id = id, date = daterated,
                     menses = menses, ovtoday = ovtoday,
                     impute_leading_ovulation = TRUE,
                     leading_ovulation_luteal_days = k)
  sum(covered(x)) - sum(covered(off))
})
#> [1]  15  45  75 120

Every extra day you ask for is scaled, in a straight line, with no gate anywhere. Treat a value above the default as a methods decision to write down, not a coverage dial to turn up.

Some people cannot be recovered, and it is worth knowing which

Turn on both opt-in rules, widen the cycle-length bounds to the CLEAR lab’s [20, 43], and drop the luteal floor, and one person in fifteen still has no cycle time at all.

most <- pacts_scaling(cycledata_special, id = id, date = daterated,
                      menses = menses, ovtoday = ovtoday,
                      impute_leading_ovulation = TRUE, impute_next_menses = TRUE,
                      lower_cyclength_bound = 20, upper_cyclength_bound = 43,
                      luteal_phase_min_days = 4)

unique(most$case[!is.na(most$id) & is.na(most$cyclic_time_impute) &
                   !most$id %in% most$id[covered(most)]])
#> [1] "no menses onset and no confirmed ovulation: nothing can scale"

That person has no menses onset and no confirmed ovulation, so there is nothing to anchor to and no setting can invent one. Empty cycle-time columns for a participant like this are the correct answer, not a bug – and ?cycledata_special lists which of the other fourteen are recoverable, by which setting, and at what cost.

The `case` column disappears on fabricated rows, deliberately

case is a per-person label, so it reads NA on exactly the rows the package added – calendar-filled gaps and imputed anchors. That makes it the cheapest possible check of which rows in your own output nobody rated. Carry it across those rows when you want it for grouping.

library(dplyr)
library(tidyr)
most |> group_by(id) |> fill(case, .direction = "downup") |> ungroup()

What to report

How ovulation was determined. summary_ovulation() splits cycles into biomarker-confirmed and imputed.

summary_ovulation(scaled)$ovstatus_total
#>          Total Confirmed Ovulation
#> N cycles                        14
#>          Total Estimated Ovulation via 15day Backward Count
#> N cycles                                                 11

What share of coverage came from the phase-length fallback.

mean(scaled$cyclic_time_impute_extended_phase == 1, na.rm = TRUE)
#> [1] 0.05376344

References

Nagpal, A., Schmalenberger, K. M., Barone, J. C., Mulligan, E., Stumper, A., Knol, L., Failenschmid, J., Kiesner, J., Peters, J. R., & Eisenlohr-Moul, T. A. (2025). Studying the menstrual cycle as a continuous variable: Implementing Phase-Aligned Cycle Time Scaling (PACTS) with the menstrualcycleR package. Psychoneuroendocrinology, 107584. https://doi.org/10.1016/j.psyneuen.2025.107584

Schmalenberger, K. M., et al. (2021). How to study the menstrual cycle: Practical tools and recommendations. Psychoneuroendocrinology, 123, 104895. https://doi.org/10.1016/j.psyneuen.2020.104895

This vignette was drafted with Claude Code and reviewed by the package authors, who are responsible for its content. See the README for the package’s full statement on AI tool use.

Tory Eisenlohr-Moul