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Changelog

Source: NEWS.md

hvtiRimputation 0.1.3

  • DESCRIPTION now declares the Quarto command line tool in SystemRequirements, which the new vignette needs to build.

  • New Overview vignette, vignette("hvtiRimputation"). It walks one small synthetic example from impute_mean() through the record, the row-level attrition columns, indicators, provenance and the inputs the package refuses, then shows the impute_multiple() prototype. On the pkgdown site, articles put the table of contents on the left and use the full window width, through pkgdown/extra.css. quarto and knitr join Suggests.

hvtiRimputation 0.1.2

  • impute_multiple() is now reproducible by default (breaking). seed is required, either as an argument or once per session through options(hvtiRimputation.seed = ); with neither, the call fails. The old default, seed = NA, left the draws to whatever state R’s RNG happened to be in. The mice call now runs under withr::with_seed() with the RNG kind pinned to R’s defaults, so the same seed gives the same imputations after any set.seed() or RNGkind() change, and the caller’s own random stream is restored afterwards. A call that already passed a seed gets the same values as before under R’s default RNG kind. withr is a new import.
  • A new test parses R/ and fails on a call outside withr::with_seed() to any function in a defined audited set: every stats random generator (found from the installed stats exports, so new ones are covered), sample(), jitter(), simulate(), mice, randomForestSRC and varPro. It also fails on a randomForestSRC call without a negative seed =, and it self-tests on planted violations. A function that draws from the RNG internally without being in that set is outside the audit.
  • Now requires R 4.4.0 or newer, up from 4.1.0, to match the rest of the HVTI family. hvtiR::install() installs the members together, and several already required 4.4.0, so on an older R the install failed whatever this package declared.

hvtiRimputation 0.1.1

New features

  • impute_multiple() prototype. Multiple imputation by chained equations (mice), returning m completed datasets stacked long plus the same shape of record impute_mean() returns. Method defaults to mice’s own per-column dispatch (pmm/logreg/polyreg/polr) rather than one string forced across every column; a named-vector method argument overrides specific columns. A new class, hvti_imputation_multi, keeps impute_mean()’s own contract untouched. imputed_data() is now a real S3 generic; its hvti_imputation_multi method requires an imputation argument with no default, on purpose – see dev/specs/2026-09-23-impute-multiple-design.md for why. Pooling (Rubin’s rules) remains out of scope, per the design note and the README.

hvtiRimputation 0.1.0

First release. Single mean imputation, and the record that goes with it.

  • The impute_mean() help page no longer tells readers to call the designed but unimplemented impute_multiple() function.

  • Coverage uploads without a Codecov token are now best-effort, so a successful package test run is not marked failed solely because tokenless upload is unavailable.

  • impute_mean() ports PROC STANDARD ... REPLACE – the form with no MEAN=/STD=, which the imputsub macro wraps. Missing values are filled with the variable’s mean over its non-missing values; observed values are never touched.

  • The record is a logical matrix parallel to the data, one column per imputed variable, TRUE where a value was filled. Read it with imputed_matrix(). A count cannot answer was this patient’s value imputed?, so the matrix is the primary artifact and summary() is a view of it rather than a replacement for it.

  • imputed_any(), complete_case_pass() and analysis_pass() are the row-level columns the attrition record consumes. complete_case_pass() is read from the input, before anything is filled – evaluated on the completed data every row over vars would pass, which is the opposite of what the column means. analysis_pass() is the same question after imputing.

  • kept_by_imputation() counts the rows in an analysis only because a covariate was filled in – the number that catches a port which imputed the wrong variable list yet still reached the right row count.

    ⚠️ It is analysis_pass & !complete_case_pass, not imputed_any & !complete_case_pass. The latter overcounts: it is TRUE for a row that had a value filled but is still incomplete because a column outside vars is missing, and such a row never enters the analysis at all. The two agree only when imputation completes every row, which is the case the design was written against – so the wrong formula gave the right answer there and the error was invisible.

  • imputation_indicators() generates missingness indicators from the record, with a prefix the caller chooses. The SAS ms_* spelling is available (prefix = "ms_") and is deliberately not the default: a convention adopted for parity becomes permanent.

  • imputation_provenance() records the method, m, the fill values, the package version and the seed. An imputed dataset that does not say how it was imputed cannot be reproduced.

Divergences from SAS, stated once

  • vars is required. PROC STANDARD with no VAR statement processes every numeric variable in the dataset; that default is not inherited. An imputation that silently chose its own variable list is the failure the record exists to catch.
  • An all-missing variable is an error, not a pass-through. There is no mean to impute from, and leaving the column as NA while reporting it as imputed would be a lie in the record.
  • A non-finite mean is an error. mean(c(Inf, -Inf), na.rm = TRUE) is NaN; filling with it leaves the cell missing while the record marks it imputed. An infinite mean is refused in the other direction, as not a plausible measurement. The completed column is then asserted to hold no missing values, so the invariant is proved rather than assumed.
  • A classed numeric is an error, even though it passes is.numeric(). bit64::integer64 packs a 64-bit integer into a double’s bits, so taking its mean into an ordinary double reinterprets the bit pattern – a column of 1 and 3 completes as 0, with the provenance recording 9.88e-324 rather than 2. Wrong data and wrong provenance, no error. The guard keys on the class attribute rather than a list of known classes, so it also catches whatever the next one is called. Convert with as.numeric() yourself, so the conversion is a choice you made.
  • Duplicated column names in data are an error. data[["age"]] returns the first age, so naming it fills one column, leaves the other missing, and emits a single record column that cannot say which. An audit cannot read that.
  • A non-numeric variable is an error. A mean of a factor or a character column is not defined, and guessing one would be a method choice made silently.

Not in this release

impute_multiple() (PROC MI, mult_imput) is designed and not built. Pooling by Rubin’s rules is deferred: multiple imputation is only multiple imputation if the results are pooled, and that is separate work with its own verification. BY-group means are unmeasured in the corpus and unimplemented. PROC STANDARD MEAN=0 STD=1 REPLACE, which standardises and fills, is a different operation and is not what impute_mean() does.