spell checked docs

docs/src/examples.md

@@ -37,7 +37,7 @@ The [Data](https://github.com/m3g/ComplexMixturesExamples/tree/main/Protein_in_G
 
 The sample trajectory is provided so that the first example can be run, yet do not expect that the results are the same, as the sampling is much lower in this case. The complete trajectory can be retrieved from [this link](https://drive.google.com/file/d/14M30jDHRwUM77hzbDphgbu8mcWFBcQrX/view?usp=sharing) (3GB file). 
 
-## Minimum-Distance Distribuion function
+## Minimum-Distance Distribution function
 
 Here we compute the minimum-distance distribution function, the Kirkwood-Buff integral, and the atomic contributions of the solvent to the density.
 
@@ -261,7 +261,7 @@ Set a matrix that will store the results, with a number of lines corresponding t
 rescontrib = zeros(length(R.mddf),length(residues))
 ```
 
-Now, collect the contribution of each residue as a column of the above matrix. The notation `pairs(residues)` returns tuples containg the index `ires` and the corresponding residue. The `.=` symbol sets each element of the corresponding column of the  `rescontrib` matrix to the output of `contributions` (by broadcasting).  
+Now, collect the contribution of each residue as a column of the above matrix. The notation `pairs(residues)` returns tuples containing the index `ires` and the corresponding residue. The `.=` symbol sets each element of the corresponding column of the  `rescontrib` matrix to the output of `contributions` (by broadcasting).  
 ```julia
 for (ires,residue) in pairs(residues)
   rescontrib[:,ires] .= contributions(solute,R.solute_atom,residue)

docs/src/index.md

@@ -71,7 +71,7 @@ Kirkwood-Buff integrals carry the information of the total accumulation or deple
 </center>
 ```
 
-From differences in KB integrals among cossolvents, the Preferential Solvation parameter can be computed. This is an important parameter because it can be measured experimentally and is ultimately associated with the equilibrium thermodynamics of the solvation. In the following figure, we show that, for example, the preferential solvation of a protein in different folding states is dependent in a non-trivial way on the concentration of an ionic liquid in aqueous solutions. 
+From differences in KB integrals among cosolvents, the Preferential Solvation parameter can be computed. This is an important parameter because it can be measured experimentally and is ultimately associated with the equilibrium thermodynamics of the solvation. In the following figure, we show that, for example, the preferential solvation of a protein in different folding states is dependent in a non-trivial way on the concentration of an ionic liquid in aqueous solutions. 
 
 ```@raw html
 <center>

docs/src/installation.md

@@ -4,7 +4,7 @@
     This is a package written in Julia. We invite you to experiment with the language, but if you want to just call this package 
     from Python, read the [From Python](@ref python) section of the manual. Understanding all the features of the package 
     requires reading the manual as whole. The syntaxes of using this package from Julia or Python are almost identical, and the
-    motivation for using Python should be mostly the familiarity with further analsys tools, as the plotting packages. 
+    motivation for using Python should be mostly the familiarity with further analysis tools, as the plotting packages. 
 
 ## Install Julia
 
@@ -85,7 +85,7 @@ And the script can be run with `julia -t auto script.jl` (where `-t auto` allows
     import ComplexMixtures
     ```
     Then, the functions of the package are called, for example, using `ComplexMixtures.mddf(...)`.
-    To avoid having to write `ComplexMixtures` all the time, define an accronym. For example:
+    To avoid having to write `ComplexMixtures` all the time, define an acronym. For example:
     ```julia
     import ComplexMixtures as CM
     CM.mddf(...)

docs/src/parallel.md

@@ -15,7 +15,7 @@ The computation will use a number of parallel processes equal to `N`.
     Many computers allow hyperthreading, and not necessarily this this beneficial for the execution
     of this package. The optimal number of threads may vary.
 
-    Independently of the number of threads initalized with the `-t` command-line
+    Independently of the number of threads initialized with the `-t` command-line
     parameter, the number of processes launched by `ComplexMixtures` in any 
     given computation can be adjusted by the `Options(nthreads=N)` option. This
     won't provide any speedup if the optional number of threads is greater than

docs/src/python.md

@@ -1,7 +1,7 @@
 # [From Python](@id python)
 
 !!! note
-    Most features of the package are available through this Python interface. However, some flexibility may be reduced and, also, the tunning of the plot appearance is left to the user, as it is expected that he/she is fluent with some tools within Python if chosing this interface.
+    Most features of the package are available through this Python interface. However, some flexibility may be reduced and, also, the tunning of the plot appearance is left to the user, as it is expected that he/she is fluent with some tools within Python if choosing this interface.
 
     Python 3 or greater is required.
 
@@ -15,7 +15,7 @@ The following examples consider a system composed a protein solvated by a mixtur
 </center>
 ```
 
-Image of the system of the example: a protein solvated by a mixture of glycreol (green) and water, at a concentration of 50%vv. The complete
+Image of the system of the example: a protein solvated by a mixture of glycerol (green) and water, at a concentration of 50%vv. The complete
 example is available at [this repository](https://github.com/m3g/ComplexMixturesExamples/tree/main/Protein_in_Glycerol).
 
 ## Loading the `ComplexMixtures.py` file
@@ -57,7 +57,7 @@ export JULIA_NUM_THREADS=8
 ```
 The last line will allow Julia to execute multi-threaded, which will improve a lot the performance on most machines. Set the number of threads to the number of cores of your computer.
 
-## Minimum-Distance Distribuion function
+## Minimum-Distance Distribution function
 
 Note that the example here follows an identical syntax to the Julia example, except that we qualify the name of the loaded module and implicitly load the `PDBTools` package.
 

docs/src/quickguide.md

@@ -94,7 +94,7 @@ The fastest way to understand how to use this package is through an
 example.  
 
 Let us consider a system of three components: a protein,
-water, a cossolvent: TMAO (trimetylamine-N-oxyde), which is a common
+water, a cosolvent: TMAO (trimetylamine-N-oxyde), which is a common
 osmolyte known to stabilize protein structures. A picture of this system
 is shown below, with the protein in blue, water, and TMAO molecules. The
 system was constructed with [Packmol](http://m3g.iqm.unicamp.br/packmol)

docs/src/references.md

@@ -30,7 +30,7 @@ If this package was useful to you, please cite the following papers:
 
 * [Packmol](http://m3g.iqm.unicamp.br/packmol): A package for building initial configurations for molecular dynamics simulations.
 
-* [CellListMap.jl](https://m3g.github.io/CellListMap.jl/stable/): Efficient and customizable implementation of cell lists, which allows the computation of general properties dependent on distances of particles within a cutoff, for example short-range potentials, forces, neighbour lists, etc.
+* [CellListMap.jl](https://m3g.github.io/CellListMap.jl/stable/): Efficient and customizable implementation of cell lists, which allows the computation of general properties dependent on distances of particles within a cutoff, for example short-range potentials, forces, neighbor lists, etc.
 
 * [MDLovoFit](http://m3g.iqm.unicamp.br/mdlovofit/home.shtml): Automatic identification of mobile and rigid substructures in molecular dynamics simulations and fractional structural fluctuation analysis.