Recently, allometric was updated to v2.0.0. The move from v1 to v2 does not necessarily indicate the delivery of new features. Rather, it indicates that substantial changes were made to the package that prevent back-compatibility with v1. Hence, we encourage all readers to update their allometric installations and models by running:


install_models(redownload = TRUE)

Largely, these updates only effect the back-end of allometric, e.g., those functions that allow users to contribute models to our repository. The one exception are the Taxa and Taxon classes, described below.

Introducing the Taxon and Taxa Classes

Prior versions of allometric tracked species information for a model using the family, genus, and species descriptor fields. These were insufficient for several reasons. First, a model can belong to multiple taxonomic specifications (e.g., Douglas-fir and Ponderosa pine together). In allometric v1, this was achieved by duplicating the model and assigning the one species to each copy, which is ineffecient for storage purposes. Second, there were no validity checks used to ensure that the taxonomic specification was valid. This means a model could just have a species field defined, but no genus or family, which causes data quality issues.

In allometric v2, taxonomic specifications must use the Taxa and Taxon classes, which enforce stricter checks for taxonomic validity. Taxa is essentially a special type of list that only contains Taxon objects. Taxon is an object that contains one taxonomic hierarchy.

For example:

pp <- Taxon(family = "Pinaceae", genus = "Pinus", species = "ponderosa")
wwp <- Taxon(family = "Pinaceae", genus = "Pinus", species = "monticola")

my_taxa <- Taxa(pp, wwp)

specifies a Taxa containing two types of trees, Ponderosa pine and Western white pine. Some useful methods have been implemented for Taxa and Taxon. For example, we can do common set operations on Taxa:

"Pinus" %in% my_taxa
## [1] TRUE
pp %in% my_taxa
## [1] TRUE

We can do the same for Taxon. A few examples:

"Pinus" %in% pp
## [1] TRUE
"ponderosa" %in% wwp
## [1] FALSE
pp == pp
## [1] TRUE
pp == wwp
## [1] FALSE

These methods facilitate searching, described in the next section.

Searching Taxa in a Model Table

One of the most common operations users will perform is searching through a model table to find models of a given family, genus, or species. Using the above operations, filtering on the taxa column is similar to working with any other hierarchical column in a model table. Readers should already be familiar with the purrr examples on this page.

ponderosa_models <- models %>%
    purrr::map_lgl( # A map over each model's taxa
      ~ pp %in% .

The structure of this example is exactly the same as any other hierarchical search done in allometric. We filter the set of models using dplyr::filter() on the taxa column. For each Taxa we encounter, represented by ., we check if our Ponderosa pine Taxon is inside. If yes, the function ~ pp %in% . returns true, and the row is retained. Please refer to this page for further examples.

Usage when Writing Models

The taxa descriptor is a list of Taxon elements. Here is an example for a height-diameter model that applies to both Douglas-fir and Western hemlock.

my_model <- FixedEffectsModel(
  response = list(hst = units::as_units("ft")),
  covariates = list(dsob = units::as_units("in")),
  parameters = list(a = 1),
  predict_fn = function(dsob) {a * dsob},
  descriptors = list(
    taxa = list(
        family = "Pinaceae",
        genus = "Pseudotsuga",
        species = "menziesii"
        family = "Pinaceae",
        genus = "Tsuga",
        species = "heterophylla"
    country = "US",
    region = "US-OR"

We can see that the value of the taxa descriptor is a list of an arbitrary number of Taxon calls. Calls to Taxon need not specify all three values of the hierarchy. Instead, we can specify shallower layers only:

Taxon(family = "Pinaceae")
## An object of class "Taxon"
## Slot "family":
## [1] "Pinaceae"
## Slot "genus":
## [1] NA
## Slot "species":
## [1] NA

but we cannot specify deeper layers without specifying the shallower layers first:

Taxon(genus = "Pinus")
## Error in validObject(.Object): invalid class "Taxon" object: Invalid taxonomic hierarchy

Usage when Writing Model Sets

For model sets we have added a helper function aggregate_taxa(), which will aggregate a row of a parameter CSV file across the family, genus and species columns into a new column called taxa. For example:

my_parameter_frame <- tibble::tibble(
  family = c("Pinaceae", "Betulacae"),
  parameter_1 = c(1, 2)

my_parameter_frame %>% aggregate_taxa()
## # A tibble: 2 × 2
##   parameter_1 taxa  
##         <dbl> <list>
## 1           1 <Taxa>
## 2           2 <Taxa>

This allows back-compatibility with previously written parameter files from v1, while enforcing the taxa structure.

JSON Representations of Models

Models can now be converted to and from JSON. Of all of the new updates, this is the one I am most excited about, because it enables models to be stored in a remotely hosted database. The to/from JSON conversions are a necessary pre-requisite for this database, which will yield many forthcoming features including:

  1. A publicly available API for searching/requesting model information.
  2. An interactive web viewer for the models
  3. Entrypoints for other languages, such as Python, to access models and use them in analysis.

In short, the remotely hosted database will become the centralized location of the models, and users will interact with this database exclusively in the future. For now, users must still install models locally. We expect to deliver the database in 2024.

Point 3 is especially exciting, and savvy Python package developers are encouraged to email me at for some colloboration ideas.

covariate_units and response_unit Refactoring

The covariate_units and response_unit arguments were refactored to covariates and response, respectively. Only users that contribute models to allometric will be impacted by this change. For example, a FixedEffectsModel is now made by the following:

  response = list(
    vsia = units::as_units("ft^3")
  covariates = list(
    dsob = units::as_units("in")

This is easier to write and remember.