typos

Author: bgctw

src/AbstractHybridProblem.jl

@@ -17,7 +17,7 @@ optionally
 - `get_hybridproblem_cor_ends` (defaults to include all correlations: `(P=(1,), M=(1,))`)
 
 The initial value of parameters to estimate is spread
-- `ϕg`: paramter of the MLapplicator: returned by `get_hybridproblem_MLapplicator`
+- `ϕg`: parameter of the MLapplicator: returned by `get_hybridproblem_MLapplicator`
 - `ζP`: mean of the PBmodel parameters: returned by `get_hybridproblem_par_templates`
 - `ϕunc`: additional parameters of the approximte posterior: returned by `get_hybridproblem_ϕunc`
 """

src/cholesky.jl

@@ -288,7 +288,7 @@ end
     get_cor_count(n_par::Integer)
 
 Return number of correlation coefficients for a correlation matrix of size `(npar x npar)`
-With blocks starting a postions given with tuple `cor_ends`.
+With blocks starting a positions given with tuple `cor_ends`.
 """
 function get_cor_count(cor_ends::AbstractVector)
     sum(get_cor_counts(cor_ends))

src/init_hybrid_params.jl

@@ -20,7 +20,7 @@ Returns a NamedTuple of
     parameters, e.g. `Stacked(elementwise(identity), elementwise(exp), elementwise(exp))`.
     Its the transformation froing from unconstrained to constrained space: θ = Tinv(ζ),
     because this direction is used much more often.
-- `ϕunc0` initial uncertainty parameters, ComponentVector wiht format of `init_hybrid_ϕunc.`
+- `ϕunc0` initial uncertainty parameters, ComponentVector with format of `init_hybrid_ϕunc.`
 """
 function init_hybrid_params(θP::AbstractVector{FT}, θM::AbstractVector{FT},
         cor_ends::NamedTuple, ϕg::AbstractVector{FT}, n_batch;