remove stereographic slice sampling

Author: Red-Portal

Project.toml

@@ -10,7 +10,6 @@ FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 LogDensityProblems = "6fdf6af0-433a-55f7-b3ed-c6c6e0b8df7c"
 LogDensityProblemsAD = "996a588d-648d-4e1f-a8f0-a84b347e47b1"
-LogExpFunctions = "2ab3a3ac-af41-5b50-aa03-7779005ae688"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 
@@ -28,7 +27,6 @@ FillArrays = "1"
 LinearAlgebra = "1"
 LogDensityProblems = "2"
 LogDensityProblemsAD = "1"
-LogExpFunctions = "0.3"
 Random = "1"
 Requires = "1"
 Turing = "0.36"

README.md

@@ -22,7 +22,6 @@ For general usage, please refer to [here](https://turinglang.org/SliceSampling.j
 ### Multivariate Slice Sampling Algorithms
 - Latent slice sampling ([LSS](https://turinglang.org/SliceSampling.jl/dev/latent_slice/)) by Li and Walker[^LW2023] (`LatentSlice`)
 - Gibbsian polar slice sampling ([GPSS](https://turinglang.org/SliceSampling.jl/dev/gibbs_polar/)) by P. Schär, M. Habeck, and D. Rudolf[^SHR2023] (`GibbsPolarSlice`)
-- Stereographic slice sampling[^BLR2024] (`StereographicSlice`)
 
 ## Example with Turing Models
 This package supports the [Turing](https://github.com/TuringLang/Turing.jl) probabilistic programming framework:
@@ -47,7 +46,6 @@ The following slice samplers can also be used as a conditional sampler in `Turin
 * For multidimensional variables: 
   * `RandPermGibbs`
   * `HitAndRun`
-  * `StereographicSlice`
 * For unidimensional variables: 
   * `Slice`
   * `SliceSteppingOut`
@@ -86,4 +84,3 @@ sample(model, sampler, n_samples)
 [^SHR2023]: Schär, P., Habeck, M., & Rudolf, D. (2023, July). Gibbsian polar slice sampling. In International Conference on Machine Learning.
 [^GG1984]: Geman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, (6).
 [^BRS1993]: Bélisle, C. J., Romeijn, H. E., & Smith, R. L. (1993). Hit-and-run algorithms for generating multivariate distributions. Mathematics of Operations Research, 18(2), 255-266.
-[^BLR2024]: Bell, Cameron, Krzystof Łatuszyński, and Gareth O. Roberts. "Adaptive stereographic MCMC." arXiv preprint arXiv:2408.11780 (2024)

ext/SliceSamplingTuringExt.jl

@@ -29,7 +29,6 @@ Turing.Inference.isgibbscomponent(::SliceSampling.HitAndRun) = true
 Turing.Inference.isgibbscomponent(::SliceSampling.Slice) = true
 Turing.Inference.isgibbscomponent(::SliceSampling.SliceSteppingOut) = true
 Turing.Inference.isgibbscomponent(::SliceSampling.SliceDoublingOut) = true
-Turing.Inference.isgibbscomponent(::SliceSampling.StereographicSlice) = true
 
 function Turing.Inference.getparams(
     ::Turing.DynamicPPL.Model, sample::SliceSampling.UnivariateSliceState

src/SliceSampling.jl

@@ -7,7 +7,6 @@ using Distributions
 using FillArrays
 using LinearAlgebra
 using LogDensityProblems
-using LogExpFunctions
 using Random
 
 # The following is necessary because Turing wraps all models with
@@ -117,10 +116,6 @@ include("multivariate/latent.jl")
 export GibbsPolarSlice
 include("multivariate/gibbspolar.jl")
 
-# Stereographic Slice Sampling
-export StereographicSlice
-include("multivariate/stereographic.jl")
-
 # Turing Compatibility
 
 if !isdefined(Base, :get_extension)

src/multivariate/stereographic.jl

@@ -77,7 +77,6 @@ end
         # Multivariate slice samplers
         LatentSlice(5),
         GibbsPolarSlice(100),
-        StereographicSlice(1),
     ]
         @testset "initial_params" begin
             model  = MultiModel(3.0, 3.0, [0.0])

test/multivariate.jl

@@ -20,7 +20,6 @@
         HitAndRun(SliceDoublingOut(1)),
         LatentSlice(5),
         GibbsPolarSlice(5),
-        StereographicSlice(10),
     ]
         chain = sample(
             model,