diff --git a/README.md b/README.md
index 61c731e..928e454 100644
--- a/README.md
+++ b/README.md
@@ -1,11 +1,10 @@
 # SciMLExpectations.jl: Expectated Values of Simulations and Uncertainty Quantification
 
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 [![Global Docs](https://img.shields.io/badge/docs-SciML-blue.svg)](https://docs.sciml.ai/SciMLExpectations/stable/)
 
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diff --git a/docs/src/tutorials/gpu_bayesian.md b/docs/src/tutorials/gpu_bayesian.md
index a28c026..aa5f42b 100644
--- a/docs/src/tutorials/gpu_bayesian.md
+++ b/docs/src/tutorials/gpu_bayesian.md
@@ -130,8 +130,7 @@ This is the Monte Carlo approach and it converges to the correct answer by
 
 However, the [Koopman expectation](https://arxiv.org/abs/2008.08737) can converge
 with much fewer points, allowing the use of higher order quadrature methods to
-converge exponentially faster in many cases. To use the Koopman expectation
-functionality provided by [SciMLExpectations.jl](https://github.com/SciML/SciMLExpectations.jl),
+converge exponentially faster in many cases. To use the Koopman expectation,
 we first need to define our observable function `g`. This function designates the
 thing about the solution we wish to calculate the expectation of. Thus for our
 question "what is the expected value of `x`at time `t=10`?", we would simply use:
@@ -172,7 +171,7 @@ Are we done? No, we need to add some GPUs! As mentioned earlier, probability
 calculations can take quite a bit of ODE solves, so let's parallelize across
 the parameters. [DiffEqGPU.jl](https://github.com/SciML/DiffEqGPU.jl) allows you
 to GPU-parallelize across parameters by using the
-[Ensemble interface](https://diffeq.sciml.ai/stable/features/ensemble/). Note that
+[Ensemble interface](https://docs.sciml.ai/DiffEqDocs/stable/features/ensemble/). Note that
 you do not have to do any of the heavy lifting: all of the conversion to GPU
 kernels is done automaticaly by simply specifying `EnsembleGPUArray` as the
 ensembling method. For example:
diff --git a/docs/src/tutorials/introduction.md b/docs/src/tutorials/introduction.md
index ed5953d..3cc1878 100644
--- a/docs/src/tutorials/introduction.md
+++ b/docs/src/tutorials/introduction.md
@@ -279,12 +279,12 @@ centralmoment(5, g, prob, u0_dist, p, Koopman(), Tsit5(),
 ```
 
 ## Batch-Mode
-It is also possible to solve the various simulations in parallel by using the `batch` kwarg and a batch-mode supported quadrature algorithm via the `quadalg` kwarg. To view the list of batch compatible quadrature algorithms, refer to [Quadrature.jl](https://github.com/SciML/Quadrature.jl). Note: Batch-mode operation is built on top of DifferentialEquation.jl's `EnsembleProblem`. See the [EnsembleProblem documentation](https://diffeq.sciml.ai/stable/features/ensemble/) for additional options.
+It is also possible to solve the various simulations in parallel by using the `batch` kwarg and a batch-mode supported quadrature algorithm via the `quadalg` kwarg. To view the list of batch compatible quadrature algorithms, refer to [Integrals.jl](https://docs.sciml.ai/Integrals/stable/). Note: Batch-mode operation is built on top of DifferentialEquation.jl's `EnsembleProblem`. See the [EnsembleProblem documentation](https://docs.sciml.ai/DiffEqDocs/stable/features/ensemble/) for additional options.
 
 The default quadtrature algorithm used by `expectation()` does not support batch-mode evaluation. So, we first load dependencies for additional quadrature algorithms
 
 ```julia
-using Quadrature, Cuba
+using Integrals, Cuba
 ```
 
 We then solve our expectation as before using a `batch=10` multi-thread parallelization via `EnsembleThreads()` of Cuba's SUAVE algorithm. However, in this case we introduce additional uncertainty in the model parameter.