From 59074820938b1b29a494e906960511db0c86d235 Mon Sep 17 00:00:00 2001
From: Phillip Alday <me@phillipalday.com>
Date: Thu, 21 Aug 2025 16:33:14 -0500
Subject: [PATCH 01/17] remove prfit!, place NLopt support in submodule

* Placing NLopt support into a submodule will make it easier to move NLopt to an extension and promote PRIMA to the default backend.
* Unfortunately, the profiling code still has been explicit assumptions around NLopt.
---
 NEWS.md                    |  3 ++-
 ext/MixedModelsPRIMAExt.jl | 29 +++++++++++------------------
 src/MixedModels.jl         |  7 ++++---
 src/nlopt.jl               | 34 +++++++++++++++++++++++++++++-----
 src/optsummary.jl          | 13 +++++++++++++
 src/prima.jl               | 21 ---------------------
 src/profile/thetapr.jl     |  9 +++++----
 src/profile/vcpr.jl        |  4 ++--
 test/prima.jl              |  7 +++++--
 9 files changed, 71 insertions(+), 56 deletions(-)
 delete mode 100644 src/prima.jl

diff --git a/NEWS.md b/NEWS.md
index 3971b3405..717042cef 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -3,7 +3,8 @@ MixedModels v5.0.0 Release Notes
 - Optimization is now performed _without constraints_. In a post-fitting step, the the Cholesky factor is canonicalized to have non-negative diagonal elements. [#840]
 - The default optimizer has changed to NLopt's implementation of NEWUOA where possible. NLopt's implementation fails on 1-dimensional problems, so in the case A single, scalar random effect, BOBYQA is used instead. In the future, the default optimizer backend will likely change to PRIMA and NLopt support will be moved to an extension. Blocking this change in backend is an issue with PRIMA.jl when running in VSCode's built-in REPL on Linux. [#840]
 - [BREAKING] Support for constrained optimization has been completely removed, i.e. the field `lowerbd` has been removed from `OptSummary`.
-
+- Internal code around the default optimizer has been restructured. In particular, the NLopt backend has been moved to a submodule, which will make it easier to move it to an extension if we promote another backend to the default.
+- The `prfit!` convenience function has been removed.
 
 MixedModels v4.38.0 Release Notes
 ==============================
diff --git a/ext/MixedModelsPRIMAExt.jl b/ext/MixedModelsPRIMAExt.jl
index 39dd3a4ef..6f177464e 100644
--- a/ext/MixedModelsPRIMAExt.jl
+++ b/ext/MixedModelsPRIMAExt.jl
@@ -2,7 +2,8 @@ module MixedModelsPRIMAExt
 
 using MixedModels
 using MixedModels: Statistics
-using MixedModels: ProgressMeter, ProgressUnknown, objective!, _objective!
+using MixedModels.ProgressMeter: ProgressMeter, ProgressUnknown
+using MixedModels: objective!, _objective!
 using LinearAlgebra: PosDefException
 using PRIMA: PRIMA
 
@@ -13,19 +14,10 @@ end
 
 const PRIMABackend = Val{:prima}
 
-function MixedModels.prfit!(m::LinearMixedModel;
-    kwargs...)
-    MixedModels.unfit!(m)
-    m.optsum.optimizer = :bobyqa
-    m.optsum.backend = :prima
-
-    return fit!(m; kwargs...)
-end
-
-prima_optimizer!(::Val{:bobyqa}, args...; kwargs...) = PRIMA.bobyqa!(args...; kwargs...)
-prima_optimizer!(::Val{:cobyla}, args...; kwargs...) = PRIMA.cobyla!(args...; kwargs...)
-prima_optimizer!(::Val{:lincoa}, args...; kwargs...) = PRIMA.lincoa!(args...; kwargs...)
-prima_optimizer!(::Val{:newuoa}, args...; kwargs...) = PRIMA.newuoa!(args...; kwargs...)
+_optimizer!(::Val{:bobyqa}, args...; kwargs...) = PRIMA.bobyqa!(args...; kwargs...)
+_optimizer!(::Val{:cobyla}, args...; kwargs...) = PRIMA.cobyla!(args...; kwargs...)
+_optimizer!(::Val{:lincoa}, args...; kwargs...) = PRIMA.lincoa!(args...; kwargs...)
+_optimizer!(::Val{:newuoa}, args...; kwargs...) = PRIMA.newuoa!(args...; kwargs...)
 
 function MixedModels.optimize!(m::LinearMixedModel, ::PRIMABackend;
     progress::Bool=true, fitlog::Bool=false, kwargs...)
@@ -56,8 +48,8 @@ function MixedModels.optimize!(m::LinearMixedModel, ::PRIMABackend;
     end
 
     maxfun = optsum.maxfeval > 0 ? optsum.maxfeval : 500 * length(optsum.initial)
-    info = prima_optimizer!(Val(optsum.optimizer), obj, optsum.final;
-        #        xl=optsum.lowerbd, 
+    info = _optimizer!(Val(optsum.optimizer), obj, optsum.final;
+        #        xl=optsum.lowerbd,
         maxfun,
         optsum.rhoend, optsum.rhobeg)
     ProgressMeter.finish!(prog)
@@ -108,7 +100,7 @@ function MixedModels.optimize!(m::GeneralizedLinearMixedModel, ::PRIMABackend;
         end
         sc
     end
-    info = prima_optimizer!(Val(optsum.optimizer), obj, optsum.final;
+    info = _optimizer!(Val(optsum.optimizer), obj, optsum.final;
         xl=optsum.lowerbd, maxfun,
         optsum.rhoend, optsum.rhobeg,
         scale)
@@ -130,6 +122,7 @@ function _check_prima_return(info::PRIMA.Info)
     return nothing
 end
 
-MixedModels.opt_params(::PRIMABackend) = (:rhobeg, :rhoend, :maxfeval)
+MixedModels.opt_params(::PRIMABackend) = [:rhobeg, :rhoend, :maxfeval]
+MixedModels.optimizers(::PRIMABackend) = [:bobyqa, :cobyla, :lincoa, :newuoa]
 
 end # module
diff --git a/src/MixedModels.jl b/src/MixedModels.jl
index 180a99490..f42143ded 100644
--- a/src/MixedModels.jl
+++ b/src/MixedModels.jl
@@ -20,10 +20,10 @@ using LinearAlgebra: ldiv!, lmul!, logdet, mul!, norm, normalize, normalize!, qr
 using LinearAlgebra: rank, rdiv!, rmul!, svd, tril!
 using Markdown: Markdown
 using MixedModelsDatasets: dataset, datasets
-using NLopt: NLopt, Opt
 using PooledArrays: PooledArrays, PooledArray
+using NLopt: NLopt
 using PrecompileTools: PrecompileTools, @setup_workload, @compile_workload
-using ProgressMeter: ProgressMeter, Progress, ProgressUnknown, finish!, next!
+using ProgressMeter: ProgressMeter, Progress, finish!, next!
 using Random: Random, AbstractRNG, randn!
 using SparseArrays: SparseArrays, SparseMatrixCSC, SparseVector, dropzeros!, nnz
 using SparseArrays: nonzeros, nzrange, rowvals, sparse
@@ -214,7 +214,8 @@ include("mimeshow.jl")
 include("serialization.jl")
 include("profile/profile.jl")
 include("nlopt.jl")
-include("prima.jl")
+using .MixedModelsNLoptExt
+
 include("derivatives.jl")
 
 # aliases with non-unicode function names
diff --git a/src/nlopt.jl b/src/nlopt.jl
index 323b2ec23..60d860b40 100644
--- a/src/nlopt.jl
+++ b/src/nlopt.jl
@@ -1,8 +1,25 @@
-push!(OPTIMIZATION_BACKENDS, :nlopt)
+module MixedModelsNLoptExt # not actually an extension at the moment
+
+using ..MixedModels
+using ..MixedModels: objective!, _objective!
+# are part of the package's dependencies and will not be part
+# of the extension's dependencies
+using ..MixedModels.ProgressMeter: ProgressMeter, ProgressUnknown
+
+
+# stdlib
+using LinearAlgebra: PosDefException
+# will be a weakdep when this is moved to an extension
+using NLopt: NLopt, Opt
+
+function __init__()
+    push!(MixedModels.OPTIMIZATION_BACKENDS, :nlopt)
+    return nothing
+end
 
 const NLoptBackend = Val{:nlopt}
 
-function optimize!(m::LinearMixedModel, ::NLoptBackend;
+function MixedModels.optimize!(m::LinearMixedModel, ::NLoptBackend;
     progress::Bool=true, fitlog::Bool=false,
     kwargs...)
     optsum = m.optsum
@@ -42,7 +59,7 @@ function optimize!(m::LinearMixedModel, ::NLoptBackend;
     return xmin, fmin
 end
 
-function optimize!(m::GeneralizedLinearMixedModel, ::NLoptBackend;
+function MixedModels.optimize!(m::GeneralizedLinearMixedModel, ::NLoptBackend;
     progress::Bool=true, fitlog::Bool=false,
     fast::Bool=false, verbose::Bool=false, nAGQ=1,
     kwargs...)
@@ -121,6 +138,13 @@ function _check_nlopt_return(ret, failure_modes=_NLOPT_FAILURE_MODES)
     end
 end
 
-function opt_params(::NLoptBackend)
-    return (:ftol_rel, :ftol_abs, :xtol_rel, :xtol_abs, :initial_step, :maxfeval, :maxtime)
+function MixedModels.opt_params(::NLoptBackend)
+    return [:ftol_rel, :ftol_abs, :xtol_rel, :xtol_abs, :initial_step, :maxfeval, :maxtime]
 end
+
+function MixedModels.optimizers(::NLoptBackend)
+    return [:LN_NEWUOA, :LN_BOBYQA, :LN_COBYLA, :LN_NELDERMEAD, :LN_PRAXIS]
+end
+
+
+end # module
diff --git a/src/optsummary.jl b/src/optsummary.jl
index 9be1e3db3..b3012ee6e 100644
--- a/src/optsummary.jl
+++ b/src/optsummary.jl
@@ -185,3 +185,16 @@ Return a collection of the fields of [`OptSummary`](@ref) used by backend.
 they are used _after_ optimization and are thus shared across backends.
 """
 function opt_params end
+
+"""
+    optimizers(::Val{backend})
+
+Return a collection of the algorithms supported by the backend.
+
+!!! note
+    The names of the algorithms are not necessarily consistent across backends.
+    For example, NLopt has `:LN_BOBYQA` and PRIMA has `:bobyqa` for Powell's
+    BOBYQA algorithm. In other words, we have not yet abstracted over the
+    backends' differing naming conventions for algorithms.
+"""
+function optimizers end
diff --git a/src/prima.jl b/src/prima.jl
deleted file mode 100644
index 0d9cbe823..000000000
--- a/src/prima.jl
+++ /dev/null
@@ -1,21 +0,0 @@
-
-"""
-    prfit!(m::LinearMixedModel; kwargs...)
-
-Fit a mixed model using the [PRIMA](https://github.com/libprima/PRIMA.jl) implementation
-of the BOBYQA optimizer.
-
-!!! warning "Experimental feature"
-    This function is an experimental feature that will go away in the future.
-    Do **not** rely on it, unless you are willing to pin the precise MixedModels.jl
-    version. The purpose of the function is to provide the MixedModels developers
-    a chance to explore the performance of the PRIMA implementation without the large
-    and potentially breaking changes it would take to fully replace the current NLopt
-    backend with a PRIMA backend or a backend supporting a range of optimizers.
-
-!!! note "Package extension"
-    In order to reduce the dependency burden, all methods of this function are
-    implemented in a package extension and are only defined when PRIMA.jl is loaded
-    by the user.
-"""
-function prfit! end
diff --git a/src/profile/thetapr.jl b/src/profile/thetapr.jl
index 6f3b357b5..9708122bd 100644
--- a/src/profile/thetapr.jl
+++ b/src/profile/thetapr.jl
@@ -15,11 +15,12 @@ function optsumj(os::OptSummary, j::Integer)
 end
 
 function profileobj!(
-    m::LinearMixedModel{T}, θ::AbstractVector{T}, opt::Opt, osj::OptSummary
+    m::LinearMixedModel{T}, θ::AbstractVector{T}, opt::NLopt.Opt, osj::OptSummary
 ) where {T}
     isone(length(θ)) && return objective!(m, θ)
+    # XXX NLopt assumption
     fmin, xmin, ret = NLopt.optimize(opt, copyto!(osj.final, osj.initial))
-    _check_nlopt_return(ret)
+    MixedModelsNLoptExt._check_nlopt_return(ret)
     return fmin
 end
 
@@ -32,13 +33,13 @@ function profileθj!(
     j = parsej(sym)
     θ = copy(final)
     osj = optsum
-    opt = Opt(osj)
+    opt = NLopt.Opt(osj)
     pmj = m.parmap[j]
     lbj = pmj[2] == pmj[3] ? zero(T) : T(-Inf)
     if length(θ) > 1      # set up the conditional optimization problem
         notj = deleteat!(collect(axes(final, 1)), j)
         osj = optsumj(optsum, j)
-        opt = Opt(osj)               # create an NLopt optimizer object for the reduced problem
+        opt = NLopt.Opt(osj)               # create an NLopt optimizer object for the reduced problem
         function obj(x, g)
             isempty(g) ||
                 throw(ArgumentError("gradients are not evaluated by this objective"))
diff --git a/src/profile/vcpr.jl b/src/profile/vcpr.jl
index 6457c2fcc..908f57e88 100644
--- a/src/profile/vcpr.jl
+++ b/src/profile/vcpr.jl
@@ -17,10 +17,10 @@ function profilevc(m::LinearMixedModel{T}, val::T, rowj::AbstractVector{T}) wher
         objctv = objective(m)
         return objctv
     end
-    opt = Opt(optsum)
+    opt = NLopt.Opt(optsum)
     NLopt.min_objective!(opt, obj)
     fmin, xmin, ret = NLopt.optimize!(opt, copyto!(optsum.final, optsum.initial))
-    _check_nlopt_return(ret)
+    MixedModelsNLoptExt._check_nlopt_return(ret)
     return fmin, xmin
 end
 
diff --git a/test/prima.jl b/test/prima.jl
index 714f603b1..f310ce639 100644
--- a/test/prima.jl
+++ b/test/prima.jl
@@ -1,10 +1,13 @@
 using PRIMA
-using MixedModels: prfit!, unfit!, dataset
+using MixedModels: unfit!, dataset
 
 include("modelcache.jl")
 
 @testset "formula($model)" for model in models(:sleepstudy)
-    prmodel = prfit!(LinearMixedModel(formula(model), dataset(:sleepstudy)); progress=false)
+    prmodel = LinearMixedModel(formula(model), dataset(:sleepstudy))
+    prmodel.backend = :prima
+    prmodel.optimizer = :bobyqa
+    fit!(prmodel; progress=false)
 
     @test isapprox(loglikelihood(model), loglikelihood(prmodel))
     @test prmodel.optsum.optimizer == :bobyqa

From 6124e0279750fedfa54170acf310efc2225b9480 Mon Sep 17 00:00:00 2001
From: Phillip Alday <me@phillipalday.com>
Date: Thu, 21 Aug 2025 16:35:44 -0500
Subject: [PATCH 02/17] NEWS refs

---
 NEWS.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/NEWS.md b/NEWS.md
index 717042cef..741754816 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -3,8 +3,8 @@ MixedModels v5.0.0 Release Notes
 - Optimization is now performed _without constraints_. In a post-fitting step, the the Cholesky factor is canonicalized to have non-negative diagonal elements. [#840]
 - The default optimizer has changed to NLopt's implementation of NEWUOA where possible. NLopt's implementation fails on 1-dimensional problems, so in the case A single, scalar random effect, BOBYQA is used instead. In the future, the default optimizer backend will likely change to PRIMA and NLopt support will be moved to an extension. Blocking this change in backend is an issue with PRIMA.jl when running in VSCode's built-in REPL on Linux. [#840]
 - [BREAKING] Support for constrained optimization has been completely removed, i.e. the field `lowerbd` has been removed from `OptSummary`.
-- Internal code around the default optimizer has been restructured. In particular, the NLopt backend has been moved to a submodule, which will make it easier to move it to an extension if we promote another backend to the default.
-- The `prfit!` convenience function has been removed.
+- Internal code around the default optimizer has been restructured. In particular, the NLopt backend has been moved to a submodule, which will make it easier to move it to an extension if we promote another backend to the default. [#853]
+- The `prfit!` convenience function has been removed. [#853]
 
 MixedModels v4.38.0 Release Notes
 ==============================

From 740193472dfad3fdb1d320fcfaf63d3b1d14255a Mon Sep 17 00:00:00 2001
From: Phillip Alday <me@phillipalday.com>
Date: Thu, 21 Aug 2025 16:36:18 -0500
Subject: [PATCH 03/17] oops

---
 test/prima.jl | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/test/prima.jl b/test/prima.jl
index f310ce639..5942be4b5 100644
--- a/test/prima.jl
+++ b/test/prima.jl
@@ -5,8 +5,8 @@ include("modelcache.jl")
 
 @testset "formula($model)" for model in models(:sleepstudy)
     prmodel = LinearMixedModel(formula(model), dataset(:sleepstudy))
-    prmodel.backend = :prima
-    prmodel.optimizer = :bobyqa
+    prmodel.optsum.backend = :prima
+    prmodel.optsum.optimizer = :bobyqa
     fit!(prmodel; progress=false)
 
     @test isapprox(loglikelihood(model), loglikelihood(prmodel))

From 303945c1b6fcde2e9958bcae33cdde65cd710e31 Mon Sep 17 00:00:00 2001
From: Phillip Alday <me@phillipalday.com>
Date: Thu, 21 Aug 2025 17:00:45 -0500
Subject: [PATCH 04/17] Update src/nlopt.jl

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
---
 src/nlopt.jl | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/nlopt.jl b/src/nlopt.jl
index 60d860b40..02fb17623 100644
--- a/src/nlopt.jl
+++ b/src/nlopt.jl
@@ -6,7 +6,6 @@ using ..MixedModels: objective!, _objective!
 # of the extension's dependencies
 using ..MixedModels.ProgressMeter: ProgressMeter, ProgressUnknown
 
-
 # stdlib
 using LinearAlgebra: PosDefException
 # will be a weakdep when this is moved to an extension

From 9d31ff4fa5d1381aabe604d0e4ae2409542d0a02 Mon Sep 17 00:00:00 2001
From: Phillip Alday <me@phillipalday.com>
Date: Thu, 21 Aug 2025 17:01:04 -0500
Subject: [PATCH 05/17] Update src/nlopt.jl

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
---
 src/nlopt.jl | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/nlopt.jl b/src/nlopt.jl
index 02fb17623..4dd24103f 100644
--- a/src/nlopt.jl
+++ b/src/nlopt.jl
@@ -145,5 +145,4 @@ function MixedModels.optimizers(::NLoptBackend)
     return [:LN_NEWUOA, :LN_BOBYQA, :LN_COBYLA, :LN_NELDERMEAD, :LN_PRAXIS]
 end
 
-
 end # module

From b4a204fd016eb127a1a08a5a6637841c4cb1ee4b Mon Sep 17 00:00:00 2001
From: Phillip Alday <me@phillipalday.com>
Date: Thu, 21 Aug 2025 22:04:49 -0500
Subject: [PATCH 06/17] rename file

---
 src/MixedModels.jl                       | 2 +-
 src/{nlopt.jl => MixedModelsNLoptExt.jl} | 0
 2 files changed, 1 insertion(+), 1 deletion(-)
 rename src/{nlopt.jl => MixedModelsNLoptExt.jl} (100%)

diff --git a/src/MixedModels.jl b/src/MixedModels.jl
index f42143ded..852be8fdd 100644
--- a/src/MixedModels.jl
+++ b/src/MixedModels.jl
@@ -213,7 +213,7 @@ include("grouping.jl")
 include("mimeshow.jl")
 include("serialization.jl")
 include("profile/profile.jl")
-include("nlopt.jl")
+include("MixedModelsNLoptExt.jl")
 using .MixedModelsNLoptExt
 
 include("derivatives.jl")
diff --git a/src/nlopt.jl b/src/MixedModelsNLoptExt.jl
similarity index 100%
rename from src/nlopt.jl
rename to src/MixedModelsNLoptExt.jl

From c818742f41cfba61f5715ee8185cbf436a4b107b Mon Sep 17 00:00:00 2001
From: Phillip Alday <me@phillipalday.com>
Date: Thu, 21 Aug 2025 23:24:46 -0500
Subject: [PATCH 07/17] refactor profiling to be optimizer independent

---
 ext/MixedModelsPRIMAExt.jl | 38 +++++++++++++++++++++++++++++++++++---
 src/MixedModelsNLoptExt.jl | 25 +++++++++++++++++++++++--
 src/profile/thetapr.jl     | 26 +++++++++++---------------
 src/profile/vcpr.jl        | 11 +++++------
 test/prima.jl              |  8 +++++++-
 5 files changed, 81 insertions(+), 27 deletions(-)

diff --git a/ext/MixedModelsPRIMAExt.jl b/ext/MixedModelsPRIMAExt.jl
index 6f177464e..8529dc843 100644
--- a/ext/MixedModelsPRIMAExt.jl
+++ b/ext/MixedModelsPRIMAExt.jl
@@ -3,8 +3,8 @@ module MixedModelsPRIMAExt
 using MixedModels
 using MixedModels: Statistics
 using MixedModels.ProgressMeter: ProgressMeter, ProgressUnknown
-using MixedModels: objective!, _objective!
-using LinearAlgebra: PosDefException
+using MixedModels: objective!, _objective!, rectify!
+using LinearAlgebra: PosDefException, norm
 using PRIMA: PRIMA
 
 function __init__()
@@ -14,6 +14,12 @@ end
 
 const PRIMABackend = Val{:prima}
 
+function _optimizer(o::Val{O}, f, x0::Vector, args...; kwargs...) where {O}
+    x0 = copy(x0)
+    info = _optimizer!(o, f, x0, args...; kwargs...)
+    return x0, info
+end
+
 _optimizer!(::Val{:bobyqa}, args...; kwargs...) = PRIMA.bobyqa!(args...; kwargs...)
 _optimizer!(::Val{:cobyla}, args...; kwargs...) = PRIMA.cobyla!(args...; kwargs...)
 _optimizer!(::Val{:lincoa}, args...; kwargs...) = PRIMA.lincoa!(args...; kwargs...)
@@ -49,7 +55,6 @@ function MixedModels.optimize!(m::LinearMixedModel, ::PRIMABackend;
 
     maxfun = optsum.maxfeval > 0 ? optsum.maxfeval : 500 * length(optsum.initial)
     info = _optimizer!(Val(optsum.optimizer), obj, optsum.final;
-        #        xl=optsum.lowerbd,
         maxfun,
         optsum.rhoend, optsum.rhobeg)
     ProgressMeter.finish!(prog)
@@ -125,4 +130,31 @@ end
 MixedModels.opt_params(::PRIMABackend) = [:rhobeg, :rhoend, :maxfeval]
 MixedModels.optimizers(::PRIMABackend) = [:bobyqa, :cobyla, :lincoa, :newuoa]
 
+function MixedModels.profilevc(obj, optsum::OptSummary, ::PRIMABackend; kwargs...)
+    maxfun = optsum.maxfeval > 0 ? optsum.maxfeval : 500 * length(optsum.initial)
+    xmin, info = _optimizer(Val(optsum.optimizer), obj, copyto!(optsum.final, optsum.initial);
+        maxfun,
+        optsum.rhoend, optsum.rhobeg,
+        scale=nothing) # will need to scale for GLMM
+    _check_prima_return(info)
+    fmin = info.fx
+    return fmin, xmin
+end
+
+function MixedModels.profileobj!(obj,
+    m::LinearMixedModel{T}, θ::AbstractVector{T}, osj::OptSummary, ::PRIMABackend;
+    kwargs...) where {T}
+
+    maxfun = osj.maxfeval > 0 ? osj.maxfeval : 500 * length(osj.initial)
+    xmin = copyto!(osj.final, osj.initial)
+    info = _optimizer!(Val(osj.optimizer), obj, xmin;
+        maxfun,
+        osj.rhoend, osj.rhobeg,
+        scale=nothing) # will need to scale for GLMM
+    fmin = info.fx
+    _check_prima_return(info)
+    rectify!(m)
+    return fmin
+end
+
 end # module
diff --git a/src/MixedModelsNLoptExt.jl b/src/MixedModelsNLoptExt.jl
index 4dd24103f..0feca0cda 100644
--- a/src/MixedModelsNLoptExt.jl
+++ b/src/MixedModelsNLoptExt.jl
@@ -1,13 +1,13 @@
 module MixedModelsNLoptExt # not actually an extension at the moment
 
 using ..MixedModels
-using ..MixedModels: objective!, _objective!
+using ..MixedModels: objective!, _objective!, rectify!
 # are part of the package's dependencies and will not be part
 # of the extension's dependencies
 using ..MixedModels.ProgressMeter: ProgressMeter, ProgressUnknown
 
 # stdlib
-using LinearAlgebra: PosDefException
+using LinearAlgebra: PosDefException, norm
 # will be a weakdep when this is moved to an extension
 using NLopt: NLopt, Opt
 
@@ -145,4 +145,25 @@ function MixedModels.optimizers(::NLoptBackend)
     return [:LN_NEWUOA, :LN_BOBYQA, :LN_COBYLA, :LN_NELDERMEAD, :LN_PRAXIS]
 end
 
+function MixedModels.profilevc(obj, optsum::OptSummary, ::NLoptBackend; kwargs...)
+    opt = NLopt.Opt(optsum)
+    NLopt.min_objective!(opt, obj)
+    fmin, xmin, ret = NLopt.optimize!(opt, copyto!(optsum.final, optsum.initial))
+    _check_nlopt_return(ret)
+
+    return fmin, xmin
+end
+
+function MixedModels.profileobj!(obj,
+    m::LinearMixedModel{T}, θ::AbstractVector{T}, osj::OptSummary, ::NLoptBackend;
+    kwargs...) where {T}
+
+    opt = NLopt.Opt(osj)
+    NLopt.min_objective!(opt, obj)
+    fmin, xmin, ret = NLopt.optimize(opt, copyto!(osj.final, osj.initial))
+    _check_nlopt_return(ret)
+    rectify!(m)
+    return fmin
+end
+
 end # module
diff --git a/src/profile/thetapr.jl b/src/profile/thetapr.jl
index 9708122bd..2596e4554 100644
--- a/src/profile/thetapr.jl
+++ b/src/profile/thetapr.jl
@@ -10,18 +10,15 @@ function optsumj(os::OptSummary, j::Integer)
     return OptSummary(
         deleteat!(copy(os.final), j),
         deleteat!(copy(os.lowerbd), j),
-        os.optimizer,
+        os.optimizer;
+        os.backend
     )
 end
 
-function profileobj!(
-    m::LinearMixedModel{T}, θ::AbstractVector{T}, opt::NLopt.Opt, osj::OptSummary
-) where {T}
+function profileobj!(obj,
+    m::LinearMixedModel{T}, θ::AbstractVector{T}, osj::OptSummary) where {T}
     isone(length(θ)) && return objective!(m, θ)
-    # XXX NLopt assumption
-    fmin, xmin, ret = NLopt.optimize(opt, copyto!(osj.final, osj.initial))
-    MixedModelsNLoptExt._check_nlopt_return(ret)
-    return fmin
+    return profileobj!(obj, m, θ, osj, Val(osj.backend))
 end
 
 function profileθj!(
@@ -33,14 +30,12 @@ function profileθj!(
     j = parsej(sym)
     θ = copy(final)
     osj = optsum
-    opt = NLopt.Opt(osj)
     pmj = m.parmap[j]
     lbj = pmj[2] == pmj[3] ? zero(T) : T(-Inf)
     if length(θ) > 1      # set up the conditional optimization problem
         notj = deleteat!(collect(axes(final, 1)), j)
         osj = optsumj(optsum, j)
-        opt = NLopt.Opt(osj)               # create an NLopt optimizer object for the reduced problem
-        function obj(x, g)
+        function obj(x, g=T[])
             isempty(g) ||
                 throw(ArgumentError("gradients are not evaluated by this objective"))
             for i in eachindex(notj, x)
@@ -48,7 +43,8 @@ function profileθj!(
             end
             return objective!(m, θ)
         end
-        NLopt.min_objective!(opt, obj)
+    else
+        obj = nothing
     end
     pnm = (; p=sym)
     ζold = zero(T)
@@ -57,7 +53,7 @@ function profileθj!(
     θj = final[j]
     θ[j] = θj - δj
     while (abs(ζold) < threshold) && θ[j] ≥ lbj && length(tbl) < 100  # decreasing values of θ[j]
-        ζ = sign(θ[j] - θj) * sqrt(profileobj!(m, θ, opt, osj) - fmin)
+        ζ = sign(θ[j] - θj) * sqrt(profileobj!(obj, m, θ, osj) - fmin)
         push!(tbl, merge(pnm, mkrow!(tc, m, ζ)))
         θ[j] == lbj && break
         δj /= (4 * abs(ζ - ζold))   # take smaller steps when evaluating negative zeta
@@ -81,12 +77,12 @@ function profileθj!(
     copyto!(θ, final)
     θ[j] += δj
     while (ζold < threshold) && (length(tbl) < 120)
-        fval = profileobj!(m, θ, opt, osj)
+        fval = profileobj!(obj, m, θ, osj)
         if fval < fmin
             @warn "Negative difference ", fval - fmin, " for ", sym, " at ", θ[j]
             ζ = zero(T)
         else
-            ζ = sqrt(profileobj!(m, θ, opt, osj) - fmin)
+            ζ = sqrt(profileobj!(obj, m, θ, osj) - fmin)
         end
         push!(tbl, merge(pnm, mkrow!(tc, m, ζ)))
         δj /= (2 * abs(ζ - ζold))
diff --git a/src/profile/vcpr.jl b/src/profile/vcpr.jl
index 908f57e88..087996ecb 100644
--- a/src/profile/vcpr.jl
+++ b/src/profile/vcpr.jl
@@ -10,18 +10,17 @@ Profile an element of the variance components.
 """
 function profilevc(m::LinearMixedModel{T}, val::T, rowj::AbstractVector{T}) where {T}
     optsum = m.optsum
-    function obj(x, g)
+    # by giving g a default we can also
+    # work with backends which don't have a gradient slot
+    function obj(x, g=T[])
         isempty(g) || throw(ArgumentError("g must be empty"))
         updateL!(setθ!(m, x))
         optsum.sigma = val / norm(rowj)
         objctv = objective(m)
         return objctv
     end
-    opt = NLopt.Opt(optsum)
-    NLopt.min_objective!(opt, obj)
-    fmin, xmin, ret = NLopt.optimize!(opt, copyto!(optsum.final, optsum.initial))
-    MixedModelsNLoptExt._check_nlopt_return(ret)
-    return fmin, xmin
+
+    return profilevc(obj, optsum, Val(m.optsum.backend))
 end
 
 """
diff --git a/test/prima.jl b/test/prima.jl
index 5942be4b5..01149a141 100644
--- a/test/prima.jl
+++ b/test/prima.jl
@@ -3,7 +3,7 @@ using MixedModels: unfit!, dataset
 
 include("modelcache.jl")
 
-@testset "formula($model)" for model in models(:sleepstudy)
+@testset "$(formula(model))" for model in models(:sleepstudy)
     prmodel = LinearMixedModel(formula(model), dataset(:sleepstudy))
     prmodel.optsum.backend = :prima
     prmodel.optsum.optimizer = :bobyqa
@@ -12,6 +12,12 @@ include("modelcache.jl")
     @test isapprox(loglikelihood(model), loglikelihood(prmodel))
     @test prmodel.optsum.optimizer == :bobyqa
     @test prmodel.optsum.backend == :prima
+
+    @testset "profile" begin
+        profile_prima = profile(prmodel)
+        profile_nlopt = profile(model)
+        @test isapprox(profile_prima.tbl.ζ, profile_nlopt.tbl.ζ; rtol=0.0001)
+    end
 end
 
 model = first(models(:sleepstudy))

From 685ddd0cfc19a4f2011465796526e165f1196ec8 Mon Sep 17 00:00:00 2001
From: Phillip Alday <me@phillipalday.com>
Date: Thu, 21 Aug 2025 23:26:10 -0500
Subject: [PATCH 08/17] update imports

---
 ext/MixedModelsPRIMAExt.jl | 2 +-
 src/MixedModelsNLoptExt.jl | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/ext/MixedModelsPRIMAExt.jl b/ext/MixedModelsPRIMAExt.jl
index 8529dc843..02805d960 100644
--- a/ext/MixedModelsPRIMAExt.jl
+++ b/ext/MixedModelsPRIMAExt.jl
@@ -4,7 +4,7 @@ using MixedModels
 using MixedModels: Statistics
 using MixedModels.ProgressMeter: ProgressMeter, ProgressUnknown
 using MixedModels: objective!, _objective!, rectify!
-using LinearAlgebra: PosDefException, norm
+using LinearAlgebra: PosDefException
 using PRIMA: PRIMA
 
 function __init__()
diff --git a/src/MixedModelsNLoptExt.jl b/src/MixedModelsNLoptExt.jl
index 0feca0cda..1251cea12 100644
--- a/src/MixedModelsNLoptExt.jl
+++ b/src/MixedModelsNLoptExt.jl
@@ -7,7 +7,7 @@ using ..MixedModels: objective!, _objective!, rectify!
 using ..MixedModels.ProgressMeter: ProgressMeter, ProgressUnknown
 
 # stdlib
-using LinearAlgebra: PosDefException, norm
+using LinearAlgebra: PosDefException
 # will be a weakdep when this is moved to an extension
 using NLopt: NLopt, Opt
 

From 8893cf6199006815a2c67c3befdacbb6cc2e21b3 Mon Sep 17 00:00:00 2001
From: Phillip Alday <me@phillipalday.com>
Date: Fri, 22 Aug 2025 04:26:41 +0000
Subject: [PATCH 09/17] style

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
---
 ext/MixedModelsPRIMAExt.jl | 4 ++--
 src/MixedModelsNLoptExt.jl | 1 -
 src/profile/thetapr.jl     | 2 +-
 3 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/ext/MixedModelsPRIMAExt.jl b/ext/MixedModelsPRIMAExt.jl
index 8529dc843..b32cc1347 100644
--- a/ext/MixedModelsPRIMAExt.jl
+++ b/ext/MixedModelsPRIMAExt.jl
@@ -132,7 +132,8 @@ MixedModels.optimizers(::PRIMABackend) = [:bobyqa, :cobyla, :lincoa, :newuoa]
 
 function MixedModels.profilevc(obj, optsum::OptSummary, ::PRIMABackend; kwargs...)
     maxfun = optsum.maxfeval > 0 ? optsum.maxfeval : 500 * length(optsum.initial)
-    xmin, info = _optimizer(Val(optsum.optimizer), obj, copyto!(optsum.final, optsum.initial);
+    xmin, info = _optimizer(Val(optsum.optimizer), obj,
+        copyto!(optsum.final, optsum.initial);
         maxfun,
         optsum.rhoend, optsum.rhobeg,
         scale=nothing) # will need to scale for GLMM
@@ -144,7 +145,6 @@ end
 function MixedModels.profileobj!(obj,
     m::LinearMixedModel{T}, θ::AbstractVector{T}, osj::OptSummary, ::PRIMABackend;
     kwargs...) where {T}
-
     maxfun = osj.maxfeval > 0 ? osj.maxfeval : 500 * length(osj.initial)
     xmin = copyto!(osj.final, osj.initial)
     info = _optimizer!(Val(osj.optimizer), obj, xmin;
diff --git a/src/MixedModelsNLoptExt.jl b/src/MixedModelsNLoptExt.jl
index 0feca0cda..d3e2d821a 100644
--- a/src/MixedModelsNLoptExt.jl
+++ b/src/MixedModelsNLoptExt.jl
@@ -157,7 +157,6 @@ end
 function MixedModels.profileobj!(obj,
     m::LinearMixedModel{T}, θ::AbstractVector{T}, osj::OptSummary, ::NLoptBackend;
     kwargs...) where {T}
-
     opt = NLopt.Opt(osj)
     NLopt.min_objective!(opt, obj)
     fmin, xmin, ret = NLopt.optimize(opt, copyto!(osj.final, osj.initial))
diff --git a/src/profile/thetapr.jl b/src/profile/thetapr.jl
index 2596e4554..a89902504 100644
--- a/src/profile/thetapr.jl
+++ b/src/profile/thetapr.jl
@@ -11,7 +11,7 @@ function optsumj(os::OptSummary, j::Integer)
         deleteat!(copy(os.final), j),
         deleteat!(copy(os.lowerbd), j),
         os.optimizer;
-        os.backend
+        os.backend,
     )
 end
 

From 8295bb7d6dec2e129ae478844a7a90aefa6d0a2e Mon Sep 17 00:00:00 2001
From: Phillip Alday <me@phillipalday.com>
Date: Thu, 21 Aug 2025 23:35:00 -0500
Subject: [PATCH 10/17] suppression

---
 test/prima.jl | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/test/prima.jl b/test/prima.jl
index 01149a141..44145883e 100644
--- a/test/prima.jl
+++ b/test/prima.jl
@@ -1,5 +1,6 @@
 using PRIMA
 using MixedModels: unfit!, dataset
+using Suppressor
 
 include("modelcache.jl")
 
@@ -14,8 +15,8 @@ include("modelcache.jl")
     @test prmodel.optsum.backend == :prima
 
     @testset "profile" begin
-        profile_prima = profile(prmodel)
-        profile_nlopt = profile(model)
+        @suppress profile_prima = profile(prmodel)
+        @suppress profile_nlopt = profile(model)
         @test isapprox(profile_prima.tbl.ζ, profile_nlopt.tbl.ζ; rtol=0.0001)
     end
 end

From b74dbedb0d1b78712bf616a80f700cb19169eb21 Mon Sep 17 00:00:00 2001
From: Phillip Alday <me@phillipalday.com>
Date: Fri, 22 Aug 2025 00:18:50 -0500
Subject: [PATCH 11/17] docs

---
 docs/make.jl             |  1 +
 docs/src/api.md          |  8 ++++++--
 docs/src/optimization.md | 25 +++++++++++++++++++++----
 src/optsummary.jl        | 21 ++++++++++++++++++++-
 4 files changed, 48 insertions(+), 7 deletions(-)

diff --git a/docs/make.jl b/docs/make.jl
index 1e2dd0aff..1ee57b3c4 100644
--- a/docs/make.jl
+++ b/docs/make.jl
@@ -11,6 +11,7 @@ makedocs(;
     doctest=true,
     # pagesonly=true,
     # warnonly=true,
+    # warnonly=[:cross_references],
     pages=[
         "index.md",
         "constructors.md",
diff --git a/docs/src/api.md b/docs/src/api.md
index 42b840280..1deee7aab 100644
--- a/docs/src/api.md
+++ b/docs/src/api.md
@@ -79,9 +79,9 @@ stderrror!
 varest
 ```
 
-## Non-Exported Functions
+## Non-Exported Functions and Constants
 
-Note that unless discussed elsewhere in the online documentation, non-exported functions should be considered implementation details.
+Note that unless discussed elsewhere in the online documentation, non-exported functions and constants should be considered implementation details.
 
 ```@autodocs
 Modules = [MixedModels]
@@ -89,3 +89,7 @@ Public  = false
 Order   = [:function]
 Filter = f -> !startswith(string(f), "_")
 ```
+
+```@docs
+MixedModels.OPTIMIZATION_BACKENDS
+```
diff --git a/docs/src/optimization.md b/docs/src/optimization.md
index f73a8e887..e906762ae 100644
--- a/docs/src/optimization.md
+++ b/docs/src/optimization.md
@@ -222,10 +222,29 @@ Typically, the (1,1) block is the largest block in `A` and `L` and it has a spec
 `UniformBlockDiagonal`
 providing a compact representation and fast matrix multiplication or solutions of linear systems of equations.
 
-### Modifying the optimization process
+## Modifying the optimization process
 
-The `OptSummary` object contains both input and output fields for the optimizer.
+The [`OptSummary`](@ref) object contains both input and output fields for the optimizer.
 To modify the optimization process the input fields can be changed after constructing the model but before fitting it.
+In addition to various tolerances, which we will not discuss further here, users can specify the choice of `backend` (i.e., the non-linear optimization library used) and `optimizer` (i.e., the implementation of an algorithm provided by the backend).
+
+The current default backend is [NLopt](https://github.com/JuliaOpt/NLopt.jl), which is a direct dependency of MixedModels.jl.
+A [PRIMA](https://github.com/libprima/PRIMA.jl/) backend is also provided as a package extension and thus only
+available when the library PRIMA is loaded.
+The list of currently loaded backends is available as [`MixedModels.OPTIMIZATION_BACKENDS`](@ref).
+For each individual backend, the list of available optimizers can be inspected with the function [`MixedModels.optimizers`](@ref).
+```@example Main
+MixedModels.optimizers(:nlopt)
+```
+Similarly, the list of applicable optimization parameters can be inspected with the function [`MixedModels.opt_params`](@ref).
+```@example Main
+MixedModels.opt_params(:nlopt)
+```
+
+!!! note "Optimizer defaults subject to change"
+    The choice of default backend and optimizer is subject to change without being considered a breaking change.
+    If you want to guarantee a particular backend and optimizer, then you should
+    explicitly load the associated backend's package (e.g. NLopt or PRIMA) and manually set the `optimizer` and `backend` fields.
 
 Suppose, for example, that the user wishes to try a [Nelder-Mead](https://en.wikipedia.org/wiki/Nelder%E2%80%93Mead_method) optimization method instead of the default [`BOBYQA`](https://en.wikipedia.org/wiki/BOBYQA) (Bounded Optimization BY Quadratic Approximation) method.
 ```@example Main
@@ -252,8 +271,6 @@ plot(convdf, x=:step, y=:objective, color=:algorithm, Geom.line)
 
 Run time can be constrained with  `maxfeval` and `maxtime`.
 
-See the documentation for the [`NLopt`](https://github.com/JuliaOpt/NLopt.jl) package for details about the various settings.
-
 ### Convergence to singular covariance matrices
 
 To ensure identifiability of $\Sigma_\theta=\sigma^2\Lambda_\theta \Lambda_\theta$, the elements of $\theta$ corresponding to diagonal elements of $\Lambda_\theta$ are constrained to be non-negative.
diff --git a/src/optsummary.jl b/src/optsummary.jl
index b3012ee6e..b35e4c3f8 100644
--- a/src/optsummary.jl
+++ b/src/optsummary.jl
@@ -20,7 +20,21 @@ Summary of an optimization
 
 ## Choice of optimizer and backend
 * `optimizer`: the name of the optimizer used, as a `Symbol`
-* `backend`: the optimization library providing the optimizer, default is `NLoptBackend`.
+* `backend`: the optimization library providing the optimizer, stored as a symbol.
+   The current default is `:nlopt`.
+
+The current default backend is NLopt, which is a direct dependency of MixedModels.jl.
+A PRIMA backend is also provided as a package extension and thus only
+available when the library PRIMA is loaded.
+The list of currently loaded backends is available as [`MixedModels.OPTIMIZATION_BACKENDS`](@ref).
+For each individual backend, the list of available optimizers can be inspected with the function [`MixedModels.optimizers`](@ref).
+Similarly, the list of applicable optimization parameters can be inspected with the function [`MixedModels.opt_params`](@ref).
+
+!!! note "Optimizer defaults subject to change"
+    The choice of backend and optimizer is subject to change without being considered a breaking
+    change. If you want to guarantee a particular backend and optimizer, then you should
+    explicitly load the associated backend's package (e.g. NLopt or PRIMA) and manually
+    set the `optimizer` and `backend` fields.
 
 ## Backend-specific fields
 * `ftol_rel`: as in NLopt, not used in PRIMA
@@ -186,6 +200,9 @@ they are used _after_ optimization and are thus shared across backends.
 """
 function opt_params end
 
+opt_params(s::Symbol) = opt_params(Val(s))
+
+
 """
     optimizers(::Val{backend})
 
@@ -198,3 +215,5 @@ Return a collection of the algorithms supported by the backend.
     backends' differing naming conventions for algorithms.
 """
 function optimizers end
+
+optimizers(s::Symbol) = optimizers(Val(s))

From 30df1975e13b0c4836b9ad333a8dda109fdf83e3 Mon Sep 17 00:00:00 2001
From: Phillip Alday <me@phillipalday.com>
Date: Fri, 22 Aug 2025 00:21:44 -0500
Subject: [PATCH 12/17] fix figure rendering

---
 docs/src/optimization.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/src/optimization.md b/docs/src/optimization.md
index e906762ae..486068cb4 100644
--- a/docs/src/optimization.md
+++ b/docs/src/optimization.md
@@ -250,7 +250,7 @@ Suppose, for example, that the user wishes to try a [Nelder-Mead](https://en.wik
 ```@example Main
 fm2nm = LinearMixedModel(@formula(reaction ~ 1+days+(1+days|subj)), sleepstudy);
 fm2nm.optsum.optimizer = :LN_NELDERMEAD;
-fit!(fm2nm; thin=1)
+fit!(fm2nm; fitlog=true)
 fm2nm.optsum
 DisplayAs.Text(ans) # hide
 ```

From fd30bf4bb6ae171f8f90895d8c445e32e335fc3e Mon Sep 17 00:00:00 2001
From: Phillip Alday <me@phillipalday.com>
Date: Fri, 22 Aug 2025 00:22:45 -0500
Subject: [PATCH 13/17] suppression tweak

---
 test/prima.jl | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/test/prima.jl b/test/prima.jl
index 44145883e..110ed78a4 100644
--- a/test/prima.jl
+++ b/test/prima.jl
@@ -15,8 +15,8 @@ include("modelcache.jl")
     @test prmodel.optsum.backend == :prima
 
     @testset "profile" begin
-        @suppress profile_prima = profile(prmodel)
-        @suppress profile_nlopt = profile(model)
+        profile_prima = @suppress profile(prmodel)
+        profile_nlopt = @suppress profile(model)
         @test isapprox(profile_prima.tbl.ζ, profile_nlopt.tbl.ζ; rtol=0.0001)
     end
 end

From 4f758cad041da1c4e4cc18240b2014100d804d2d Mon Sep 17 00:00:00 2001
From: Phillip Alday <me@phillipalday.com>
Date: Fri, 22 Aug 2025 00:24:25 -0500
Subject: [PATCH 14/17] NEWS tweak

---
 NEWS.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/NEWS.md b/NEWS.md
index 741754816..41d6853e6 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -4,6 +4,7 @@ MixedModels v5.0.0 Release Notes
 - The default optimizer has changed to NLopt's implementation of NEWUOA where possible. NLopt's implementation fails on 1-dimensional problems, so in the case A single, scalar random effect, BOBYQA is used instead. In the future, the default optimizer backend will likely change to PRIMA and NLopt support will be moved to an extension. Blocking this change in backend is an issue with PRIMA.jl when running in VSCode's built-in REPL on Linux. [#840]
 - [BREAKING] Support for constrained optimization has been completely removed, i.e. the field `lowerbd` has been removed from `OptSummary`.
 - Internal code around the default optimizer has been restructured. In particular, the NLopt backend has been moved to a submodule, which will make it easier to move it to an extension if we promote another backend to the default. [#853]
+- Internal code around optimization in profiling has been restructuring so that fitting done during calls to `profile` respect the `backend` and `optimizer` settings. [#853]
 - The `prfit!` convenience function has been removed. [#853]
 
 MixedModels v4.38.0 Release Notes

From ac47b33616c7a496b67fff6f7a5ee76afdb07fb4 Mon Sep 17 00:00:00 2001
From: Phillip Alday <me@phillipalday.com>
Date: Fri, 22 Aug 2025 05:24:37 +0000
Subject: [PATCH 15/17] style

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
---
 src/optsummary.jl | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/optsummary.jl b/src/optsummary.jl
index b35e4c3f8..de361894b 100644
--- a/src/optsummary.jl
+++ b/src/optsummary.jl
@@ -202,7 +202,6 @@ function opt_params end
 
 opt_params(s::Symbol) = opt_params(Val(s))
 
-
 """
     optimizers(::Val{backend})
 

From 3c31e1e270ce79f8977809ac0dac45daf75576a3 Mon Sep 17 00:00:00 2001
From: Douglas Bates <dmbates@gmail.com>
Date: Fri, 22 Aug 2025 09:58:47 -0500
Subject: [PATCH 16/17] Update NEWS.md

Minor changes in grammar.
---
 NEWS.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/NEWS.md b/NEWS.md
index 41d6853e6..b0920e6cc 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,7 +1,7 @@
 MixedModels v5.0.0 Release Notes
 ==============================
-- Optimization is now performed _without constraints_. In a post-fitting step, the the Cholesky factor is canonicalized to have non-negative diagonal elements. [#840]
-- The default optimizer has changed to NLopt's implementation of NEWUOA where possible. NLopt's implementation fails on 1-dimensional problems, so in the case A single, scalar random effect, BOBYQA is used instead. In the future, the default optimizer backend will likely change to PRIMA and NLopt support will be moved to an extension. Blocking this change in backend is an issue with PRIMA.jl when running in VSCode's built-in REPL on Linux. [#840]
+- Optimization is now performed _without constraints_. In a post-fitting step, the Cholesky factor is canonicalized to have non-negative diagonal elements. [#840]
+- The default optimizer has changed to NLopt's implementation of NEWUOA where possible. NLopt's implementation fails on 1-dimensional problems, so in the case of a single, scalar random effect, BOBYQA is used instead. In the future, the default optimizer backend will likely change to PRIMA and NLopt support will be moved to an extension. Blocking this change in backend is an issue with PRIMA.jl when running in VSCode's built-in REPL on Linux. [#840]
 - [BREAKING] Support for constrained optimization has been completely removed, i.e. the field `lowerbd` has been removed from `OptSummary`.
 - Internal code around the default optimizer has been restructured. In particular, the NLopt backend has been moved to a submodule, which will make it easier to move it to an extension if we promote another backend to the default. [#853]
 - Internal code around optimization in profiling has been restructuring so that fitting done during calls to `profile` respect the `backend` and `optimizer` settings. [#853]

From f722e30c795f1081b3e8ad91638f404752868108 Mon Sep 17 00:00:00 2001
From: Phillip Alday <me@phillipalday.com>
Date: Fri, 22 Aug 2025 10:43:15 -0500
Subject: [PATCH 17/17] Update docs/src/optimization.md

---
 docs/src/optimization.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/src/optimization.md b/docs/src/optimization.md
index 486068cb4..65c6f8756 100644
--- a/docs/src/optimization.md
+++ b/docs/src/optimization.md
@@ -230,7 +230,7 @@ In addition to various tolerances, which we will not discuss further here, users
 
 The current default backend is [NLopt](https://github.com/JuliaOpt/NLopt.jl), which is a direct dependency of MixedModels.jl.
 A [PRIMA](https://github.com/libprima/PRIMA.jl/) backend is also provided as a package extension and thus only
-available when the library PRIMA is loaded.
+available when the PRIMA package is loaded.
 The list of currently loaded backends is available as [`MixedModels.OPTIMIZATION_BACKENDS`](@ref).
 For each individual backend, the list of available optimizers can be inspected with the function [`MixedModels.optimizers`](@ref).
 ```@example Main