fix doc typos and inaccuracies

docs/src/api.md

@@ -72,13 +72,13 @@ AbstractMCMC.MCMCSerial
 
 Common keyword arguments for regular and parallel sampling are:
 - `progress` (default: `AbstractMCMC.PROGRESS[]` which is `true` initially): toggles progress logging. See the section on [Progress logging](#progress-logging) below for more details.
-- `chain_type` (default: `Any`): determines the type of the returned chain
+- `chain_type` (default: `Any`): determines the type of the returned chain.
 - `callback` (default: `nothing`): if `callback !== nothing`, then
   `callback(rng, model, sampler, sample, iteration; kwargs...)` is called after every sampling step,
-  where `sample` is the most recent sample of the Markov chain and `iteration` is the current iteration
+  where `sample` is the most recent sample of the Markov chain and `iteration` is the current iteration.
    - Keyword arguments `kwargs...` are passed down from the call to `sample(...)`. If you are performing multiple-chain sampling, then `kwargs` _additionally_ contains a `chain_number` keyword argument, which runs from 1 to the number of chains. This is not present when performing single-chain sampling.
-- `num_warmup` (default: `0`): number of "warm-up" steps to take before the first "regular" step, 
-   i.e. number of times to call [`AbstractMCMC.step_warmup`](@ref) before the first call to 
+- `num_warmup` (default: `0`): number of "warm-up" steps to take before the first "regular" step,
+   i.e. number of times to call [`AbstractMCMC.step_warmup`](@ref) before the first call to
    [`AbstractMCMC.step`](@ref).
 - `discard_initial` (default: `num_warmup`): number of initial samples that are discarded. Note that
   if `discard_initial < num_warmup`, warm-up samples will also be included in the resulting samples.
@@ -151,7 +151,7 @@ AbstractMCMC.to_samples
 AbstractMCMC.from_samples
 ```
 
-For chains of this type, AbstractMCMC defines the following two methods.
+For chains of this type, AbstractMCMC defines the following methods.
 
 ```@docs
 AbstractMCMC.chainscat

docs/src/design.md

@@ -89,21 +89,21 @@ In each step, the sample is saved in the container by `AbstractMCMC.save!!`. The
 follows the convention of the package [BangBang.jl](https://github.com/JuliaFolds/BangBang.jl)
 which is used in the default implementation of `AbstractMCMC.save!!`. It indicates that the
 sample is pushed to the container but a "widening" fallback is used if the container type
-does not allow to save the sample. Therefore `AbstractMCMC.save!!` *always has* to return the container.
+does not allow saving the sample. Therefore `AbstractMCMC.save!!` *always has* to return the container.
 
 ```@docs
 AbstractMCMC.save!!
 ```
 
 For most use cases the default implementation of `AbstractMCMC.samples` and `AbstractMCMC.save!!`
-should work out of the box and hence need not to be overloaded in downstream code.
+should work out of the box and hence need not be overloaded in downstream code.
 
 ## Creating chains
 
 !!! note
     This section does not apply to the iterator and transducer interface.
 
-At the end of the sampling procedure for regular and paralle sampling we transform
+At the end of the sampling procedure for regular and parallel sampling we transform
 the collection of samples to the desired output type by calling `AbstractMCMC.bundle_samples`.
 
 ```@docs

src/AbstractMCMC.jl

@@ -164,9 +164,9 @@ function getstats end
 """
     setparams!!([model::AbstractModel, ]state, params)
 
-Set the values of parameters in the sampler's `state` from a `Vector{<:Real}`. 
+Set the values of parameters in the sampler's `state` from a `Vector{<:Real}`.
 
-This function should follow the `BangBang` interface: mutate `state` in-place if possible and 
+This function should follow the `BangBang` interface: mutate `state` in-place if possible and
 return the mutated `state`. Otherwise, it should return a new `state` containing the updated parameters.
 
 Although not enforced, it should hold that `setparams!!(state, getparams(state)) == state`. In other

src/interface.jl

@@ -120,7 +120,7 @@ Save the `sample` of the MCMC `sampler` at the current `iteration` in the contai
 
 The function can be called with and without a predefined number `N` of samples. By default,
 AbstractMCMC uses `push!!` from the Julia package
-[BangBang](https://github.com/tkf/BangBang.jl) to append to the container, and widen its
+[BangBang](https://github.com/JuliaFolds/BangBang.jl) to append to the container, and widen its
 type if needed.
 """
 function save!!(

src/logdensityproblems.jl

@@ -1,10 +1,10 @@
 """
     LogDensityModel <: AbstractMCMC.AbstractModel
 
-Wrapper around something that implements the LogDensityProblem.jl interface.
+Wrapper around something that implements the LogDensityProblems.jl interface.
 
 Note that this does _not_ implement the LogDensityProblems.jl interface itself,
-but it simply useful for indicating to the `sample` and other `AbstractMCMC` methods
+but it is simply useful for indicating to the `sample` and other `AbstractMCMC` methods
 that the wrapped object implements the LogDensityProblems.jl interface.
 
 # Fields

src/sample.jl

@@ -27,7 +27,7 @@ end
 
 """
     sample(
-        rng::Random.AbatractRNG=Random.default_rng(),
+        rng::Random.AbstractRNG=Random.default_rng(),
         model::AbstractModel,
         sampler::AbstractSampler,
         N_or_isdone;