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Changelog

Source: NEWS.md

cppally (development version)

Breaking changes

  • Sequences no longer abort on overflow, but instead silently return NA.

  • Removed r_vec::subset and r_factors::subset as they didn’t thematically fit in with the rest of the members. The free subset function is still available.

  • Renamed common_length to list_common_length. common_length now accepts variadic inputs.

  • Relocated various headers to and from the sugar folder.

  • r_factors member functions get_codes, set_codes, and new_codes have been removed. The functionality of these can be achieved with the existing members.

  • r_sexp::length has been fully deprecated and removed. Use cppally::length for returning object length.

  • as<r_str>(r_dbl::inf()) now returns title case r_str("Inf") instead of lower case r_str("inf"). Similarly as<r_str>( -r_dbl::inf() ) returns r_str("-Inf").

  • C++ char types (except const char* and unsigned char) no longer satisfy CastableToRScalar and therefore cannot participate in R/C++ function registration.

  • Many r_sexp-based free functions have been removed and there are a few reasons for doing so. The first being that cppally didn’t provide a complete set of r_sexp free functions anyway, and so providing an incomplete set felt lackluster. Another reason is that removing these methods speeds up compilation and makes the API more lightweight and predictable. Users are now encouraged to write their own r_sexp methods as and when they require them.

Bug fixes

  • Fixed a bug where matching on integer vectors with a non NA nomatch value would still return NA.

  • Fixed a bug where getting r_df rows would return CHARSXP instead of STRSXP.

  • Fixed a bug where r_vec::apply would throw a compiler error when called on lists and character vectors.

  • Fixed a bug where the wrong OMP pragma was being used for non-SIMD parallel for loops.

  • Fixed a bug where calling set_attr() on r_df or r_factors would produce a compiler error.

r_function

  • New class r_function to safely call R functions from C++.

  • New helper pkg_env to return the environment of a package, allowing users to easily retrieve functions from specific packages, e.g. r_function("foo", pkg_env<"bar">()).

random_stream

  • A new class random_stream, allowing one to use C++ random number generators, while preserving reproducibility from R’s seed. This means that numbers drawn from random_stream are reproducible via set.seed().

  • random_stream is driven by xoshiro256++ (Blackman & Vigna) via the bundled Xoshiro-cpp library (Ryo Suzuki, MIT), rather than a <random> engine. It still models std::uniform_random_bit_generator, so it can be handed to <random> distributions and algorithms such as std::shuffle.

  • Default-constructing a random_stream seeds it from R’s RNG (via two unif_rand() draws), so set.seed() still determines every draw, while R’s RNG state is only touched once per stream rather than once per draw.

  • random_stream can also be constructed directly from a custom uint64_t seed, allowing the generation of random numbers without affecting R’s RNG state. This can be useful if you do not need reproducibility from R’s set.seed().

  • random_stream::split() returns an independent child stream. Parallel code should build its streams up front via split() before dispatching work.

  • random_stream::index samples random indices in [a, b], allowing for fast sampling with replacement. It uses Lemire’s divisionless method (source: arXiv:1805.10941) along with a portable 128-bit multiply, provided by the ankerl library. This makes it fast and platform-agnostic, therefore results should be reproducible across platforms.

  • draw_from_r must be used if one wishes to call scalar RNG functions from the R C API header ‘Random.h’.

r_date & r_psxct

A new complete set of fast and parallelisable thread-safe member and free functions for r_date and r_psxct, which respectively represent R dates and date-times. Currently, all r_date and r_psxct member functions are thread-safe except for r_date::date_str() and r_psxct::datetime_str().

  • New field accessors like year(), month(), week(), day(), hour(), minute(), second(), iso_week() and iso_year().

  • New date arithmetic member template function add<>, allowing flexible addition with time units such as “years”, “months”, “weeks”, “days”, “hours”, “minutes”, and “seconds”.

  • New date rounding member functions floor, ceiling and round.

  • New static member functions r_date::today() and r_psxct::now() to get the current date or current time. r_psxct::now() returns the current time in fractional seconds to microsecond precision.

  • New template function time_diff<> to calculate time differences between two dates or date-times.

Date and date-time arithmetic

To perform time arithmetic with dates and date-times, use add<>, e.g.  x.add<"days">(1) adds 1 day to x, and x.add<"months">(-3) subtracts 3 months from x.

The on_impossible_date argument of add<> allows you to control how to handle impossible dates that arise when performing month-based arithmetic, e.g. 31 Jan + 1 month = ?

All roll options:

enum roll : uint8_t {
    none = 0,       // does not roll and impossible dates are returned as NA
    backward = 1,   // rolls backwards until the last day of the current month is reached
    forward = 2,    // rolls to first day of next month
    away = 3,       // rolls forward when adding and backward when subtracting
    nearest = 4     // rolls backward when adding and forward when subtracting
};

Sub-day units are accepted in both r_date and r_psxct arithmetic. For r_date specifically, fractional dates are produced when using sub-day units (such as “hours”) for date arithmetic. r_date::today().add<"hours">(12) will produce a fractional date representing noon of the current day and r_date::today().as_datetime().add<"hours">(12) will produce a date-time r_psxct also representing noon of the current day.

I would generally recommend against using sub-day units with r_date and to use r_psxct if you ever need sub-day granularity.

To add sub-second units such as milliseconds and microseconds, simply supply fractional seconds, e.g. x.add<"seconds">(1.0/1000.0) adds 1 millisecond to x. It’s worth noting that loss in floating-point precision can occur when working with small microseconds, though this is inherent in R’s POSIXct class as well.

Date and date-time rounding

To round dates and date-times, use member functions floor, ceiling and round.

For example, x.floor<"months">() floors x to the start of the current month. x.ceiling<"months">() advances x to the start of the next month, unless it is already at the start of the month.

Rounding functions accept a week_start argument, which is only relevant for rounding “weeks”. It follows the same convention as wday(), where 1 is Monday and 7 is Sunday (the default). For example, x.floor<"weeks">() floors x to the most recent past Sunday, whereas x.floor<"weeks">(1) floors x to the most recent past Monday.

When the date is exactly at the midpoint between boundaries, round() will floor the date, so for example if we are rounding a date-time to the nearest day and the time is at noon, the date-time gets floored to midnight on the same day.

Date and date-time differences

To calculate the time difference between two dates or date-times, use time_diff<>.

For example, time_diff<"months">(x, y) calculates the difference in months between x and y. If x and y do not share the same day-of-the-month, then a fractional difference is returned. Use cppally::floor() to floor the result if you always need a whole difference.

To return time differences using n time unit periods (the default is 1), just pass it to the n argument, e.g. time_diff<"months">(x, y, /*n=*/ 3) returns the difference in quarters between x and y, and similarly time_diff<"days">(x, y, /*n=*/ 7) is the same as time_diff<"weeks">(x, y).

For monthly time differences, sometimes impossible dates can arise when either x or y has a day-of-the-month that is larger than the other’s number of days in the month. While the default time_diff<"months">() would return NA, we can control the internal impossible date rolling with the on_impossible_date arg.

For example, assuming x is 31 Jan 2024 and y is 29 Feb 2024, time_diff<"months">(x, y, 1, roll::nearest) rolls the impossible 31 Feb date back to 29th February, resulting in a difference of exactly 1 month. If we specified roll::away the date becomes 1st March instead, giving a fractional answer that is slightly less than 1 month.

Improvements

  • cpp_source now generates OpenMP flags by default, so its compiled functions can use both OpenMP SIMD vectorisation and multi-threaded execution (threads set via set_threads()).

  • cpp_source gains the openmp argument.

  • Integer multiplication overflow handling is now more portable.

  • r_sym now constructs symbols from strings (const char*, r_str, r_str_view) under unwind protection, so R errors can no longer longjmp past C++ destructors.

  • Sorting speed has been improved for both character vectors and numeric vectors. Sorting is faster for character vectors when there are a relatively high proportion of unique strings. Sorting is also dramatically faster for double vectors when all values in the vector are exact whole numbers.

  • groups class members starts(), counts() now see substantial speed improvements when data are in group-sorted order. When sorted, starts() and counts() may perform a hybrid linear/exponential (or galloping) search. This new search strategy dramatically improves performance on large data with small numbers of groups. Time complexity now scales with the number of groups and not the size of data.

  • groups::order() also sees speed improvements for unsorted data, due to the fact that it unconditionally applies a counting sort, without checking for NA values (since group IDs do not have NA values) or scanning the group IDs for their range, like cppally::order() generally (and correctly) does for other integer vectors. When data are already sorted it simply returns the sequence [0, n - 1] for n data points (as it did before).

  • n_unique has been sped-up for integer vectors.

  • Some algorithms now benefit from a cardinality estimate. For large inputs, an extended Chao estimator (doi:10.1016/j.csda.2011.01.017) is applied to a random sample of the data to choose a better initial reserve size for the hash maps involved. This improves performance on high-cardinality data, where it avoids repeatedly resizing the map as new keys are added.

  • Algorithms that utilise hashing should now see speed improvements for strings in particular, as SEXP type checking is now skipped for hashing specifically.

  • use_cppally() now includes the “$(CXX_VISIBILITY)” Makevars flag, making dll symbol visibility hidden by default. This should have no effect on code written with cppally, though it may slightly improve compilation time. R/C++ registered functions are now registered with the R C API tag attribute_hidden.

  • r_vec<> gains an initializer_list constructor.

Other new features

  • New function scalar_coerce. Use this with allow_lossy = true if you want to return NA instead of an error on a completely lossy scalar coercion. For example, converting a letter string to a double (e.g. as<r_dbl>(r_str("a"))) always results in an error by default whereas scalar_coerce<r_dbl>(r_str("a"), /*allow_lossy=*/ true) returns na<r_dbl>().

  • New copy member for r_vec, r_factors and r_df. copy shallow copies the vector by creating a fresh copy of the atomic data, without deep copying lists or attributes, just like Rf_shallow_duplicate.

  • New by-group left-fold functional reduce_by_group, allowing for very efficient binary reductions by-group.

  • New function list_recycle to recycle vectors of a list.

  • New r_sexp visit helpers visit_as and view_as.

  • New r_str member function is_utf8 to check whether a string has valid UTF-8 encoding, which includes ASCII strings.

  • New r_str member function as_utf8 to convert a string to a UTF-8 string. If the string is already a UTF-8 string, it is a no-op and simply returns the same r_str.

  • New class string_literal to facilitate compile-time string literal NTTP programming.

  • R function cpp_eval gains a new argument, cppally_header, allowing one to compile expressions using the optional light header “cppally_light.hpp”.

  • r_factors gains a new member function, refactor, which creates a new r_factors object given a new set of levels.

  • New R function use_openmp() to set OpenMP-enabling Makevars flags.

  • A new optional feature where users can now restrict the set of candidates that participate in template dispatch for template-registered R/C++ functions via use_template_dispatch_candidates(). For example, if a user wishes to write a C++ algebra library using only integers and doubles, they can call use_template_dispatch_candidates(c("r_int", "r_dbl")), and any template they register to R will only ever accept at most those types, regardless of the concepts and constraints of the template. This can dramatically reduce compilation times for template-heavy code when specifying a small number of candidates. Generally the same effect can be achieved with concepts and constraints, but this acts as an additional layer to ensure faster compile-times in scenarios where it makes sense to use a small candidate set.

cppally 1.1.0 (2026-07-12)

CRAN release: 2026-07-12

Breaking changes

  • Improved type-safety regarding implicit coercion of RScalar types. They now can implicitly coerce only to their wrapped types, whereas before they could implicitly coerce to that as well as other C types.

  • RTimeType arithmetic has been deprecated and removed.

  • Arithmetic involving r_lgl is now always promoted to r_int, matching R’s own semantics.

  • common_math_t<T, U> now never returns r_lgl, effectively treating r_lgl as r_int.

Bug fixes

  • Fixed bug where exclusion of concepts header was affecting vignette creation on macos.

  • Fixed incorrect NA handling of r_date and r_psxct.

  • Fixed a bug where identical() would return false for two genuinely identical NA_real_ values.

  • NULL optional arguments are now correctly handled. Vector, factor and data frame arguments can now be NULL to allow for optional argument programming from R.

  • Fixed a bug where checking exact equality (via identical) for C/C++ types was not compiling due to template ordering issue.

r_lgl (logical) semantics

  • r_lgl can now implicitly coerce to int (and only int), consistent with the other RScalar types.

  • r_lgl values are now always normalised on construction internally to either 0, 1, or NA.

Arithmetic

  • Integer overflow is now explicitly handled in all cppally arithmetic, returning NA when detected. Integer addition and subtraction in particular are written using branchless or vectorisable code, enabling SIMD vectorisation.

  • Integer in-place division /= now matches R’s %/% (floored division), instead of C’s truncating division, e.g. -7L /= 2L now gives -4 instead of -3. Division by zero or NA now correctly returns NA instead of crashing the R session. Precision is also preserved for large r_int64 values that previously round-tripped through double.

  • Scalar in-place arithmetic that narrows a wider result back to the left-hand type (e.g. r_int += r_int64) now checks the value fits first, giving NA on overflow instead of a silently wrapped result.

  • % now accepts mixed integer/float operands (e.g. r_int(7) % 2.5), promoting and flooring consistent with R’s %%.

  • Vector in-place arithmetic (+=, -=, *=, %=) now routes through the scalar operators, fixing a bug where mixed-width operations (e.g.  r_vec<r_int> += r_vec<r_int64>) could silently truncate values, including turning NA into 0.

Improvements

  • Better errors when passing invalid types to template functions.

  • Constructing invalid dates and date-times now returns NA instead of an error.

  • New concept RNumber to represent number-based types like r_int and r_dbl.

  • New member is_na for RScalar types, in addition to the equivalent is_na free function.

  • New member function na() for RScalar classes.

  • Relational operators have been extended to cover all RNumericType classes which includes r_date and r_psxct.

cppally 1.0.0 (2026-07-02)

CRAN release: 2026-07-02

First major release. cppally’s public API is now considered stable. While there may be structural changes to the r_df and r_raw classes in the future, cppally’s vector and scalar classes are considered stable.

Breaking changes

  • r_sexp.length has been deprecated, in favour of the free function length

  • length(r_df) now returns the number of rows instead of the number of cols, marking a shift in how cppally treats data frames. They are now seen as row-wise vectors.

  • Setting attributes on plain SEXP is now unsupported, e.g. via cppally::attr::set_attr. Use cppally types such as r_vector, r_factors, r_df and in some cases r_sexp for attribute manipulation.

  • Various out-of-place or trivial to implement r_vec member functions have been removed.

  • visit_vector, visit_sexp and view_sexp have been deprecated in favour of the more flexible constrained r_sexp visitors: r_sexp_visit, r_sexp_view and r_sexp_mutate. These allow concepts and custom constraints to be applied directly on the lambda’s template parameter, e.g. r_sexp_visit(x, [&]<RVector T>(T vec){}) — here x is dispatched as its concrete vector type and aborts at runtime if the underlying type isn’t an RVector. r_sexp_view is the non-owning sibling: the wrapper handed to the lambda is a view (no extra protect), so it must not outlive x. r_sexp_mutate is for in-place mutation: it moves x into the typed wrapper (making it the sole owner), calls f, then writes the result back.

  • r_factors elements are now treated as r_str in member functions like get and set

  • r_sexp_visit now visits r_null as r_vec<r_sexp>(r_null), essentially treating NULL as an empty list but without changing the underlying data.

For example, in the below pseudo-code, when x is r_null of type r_sexp, r_sexp_visit will disambiguate it as r_vec<r_sexp>(r_null), preserving its data as R’s NULL but assigning its type as r_vec<r_sexp> (list).

 r_sexp_visit(x, [&]<RVector T>(const T& vec) -> bool {
  return vec.is_null();
 });

This preservation behaviour is not new, in fact all r_vec<T> vectors preserve r_null by design, allowing for efficient and easier attribute manipulation with vectors that may or may not be r_null. What is new is that previously r_null was not a visitable r_sexp object and now it is.

Data frames

  • r_df is now fully integrated into cppally.

  • New variadic function make_df to create in-line data frames.

  • Various r_df members have been added to allow easier data frame manipulation.

std::vector

  • Partial support for std::vector coercion. The following std::vector coercion directions are supported:
    • std::vector -> std::vector
    • std::vector -> cppally::r_vec
    • cppally::r_vec -> std::vector

Any coercion between std::vector and cppally::r_vec is possible so long as the element coercions are supported by cppally::as

Regular sequences

  • New function seq which behaves similarly to base::seq.

  • New function sequence which is similar to base::sequence but accepts only scalar inputs.

Named vector hash lookups

For named vectors, lookup by name has been dramatically improved in C++ by introducing a hashing approach. It works in the following way: the first time a lookup is requested, a linear scan is done to find the named value. The second time triggers the hash map of name-value pairs to be built and cached with the vector. That second lookup is completed using the cached hash map and all subsequent lookups also use the hash map. The rationale for hashing on second lookup is covered in the ‘Automatic Names Hashing’ vignette.

A similar hashing approach is also used for r_factors, making conversions of strings to and from factor codes fast and analytically viable.

Copy-on-modify

cppally now supports copy-on-modify as an opt-in feature. This feature prevents accidentally overwriting data between shared objects, just like R. To opt-in, run cppally::use_copy_on_modify or set the copy_on_modify to TRUE in cpp_source.

The major downside of this feature is significantly slower element setting as every set must verify the object is not referenced by another object. This check is single-threaded and thus nearly all parallel cppally code is disabled as a safety precaution. If using copy-on-modify, it is recommended to avoid writing cppally registered R functions that rely on in-place modification.

pmap

Inspired by purrr::pmap and base::mapply, cppally::pmap is a C++ variadic function that supports applying custom C++ lambda functions across corresponding elements of multiple vectors.

With pmap it is trivial to calculate parallel statistics like max, min, etc. Example of C++ version of base::pmax applied to two vectors.

template <RVector T, RVector U>
requires requires(typename T::data_type a, typename U::data_type b) { max(a, b); }
[[cppally::register]]
auto cpp_pmax2(T x, U y){
  return pmap([](auto a, auto b){ return max(a, b); }, x, y);
}

reduce

A left-fold reduction functional that successively applies a binary function along the elements of the vector (from left-to-right).

Example: maximum value across vector of doubles

[[cppally::register]]
r_dbl cpp_max(r_vec<r_dbl> x){
  return x.reduce([](auto acc, auto curr){ return max(acc, curr); });
}

Other changes

  • New alias of r_vec, r_vector.

  • Named-vector subsetting is now supported.

  • New C++ functions combine and flatten. combine is a variadic function that allows for combining multiple vectors into one, similar to base::c but always casts vectors to the common type among them. flatten allows one to flatten a list of vectors into one vector of a specified type, similar to unlist(recursive = FALSE).

  • Many functions that were originally r_vec-only members are now free functions that also work on r_sexp as well as RComposite types, allowing for easier manipulation of lists.

  • All C++ reference qualifiers (T&, T&&, const T&) are now supported for registered functions, including templated ones.

  • New concept RVectorisable which encompasses types that are OMP friendly.

  • New infix operator IS_IN, identical to R’s %in%.

  • New C++ function coalesce().

  • r_psxct.datetime_str() always appends “UTC” at the end to avoid time-zone ambiguity.

Bug fixes

  • When registering C++ functions, cppally.hpp is now included in the generated C++ code. Not including it caused issues when trying to compile functions that constructed factors.

  • Zero-length r_vec vectors can now be constructed unambiguously via r_vec<T>(0).

  • Math operations involving mixed types that included r_dbl are now correct when involving NA values.

cppally 0.1.0

CRAN release: 2026-04-28

  • Initial CRAN submission.