Fix typos and grammar in documentation

- Fix "identifiabuility" -> "identifiability" in identifiable_functions.md
- Fix "inverstigate" -> "investigate" in identifiability.md
- Fix "inidcate" -> "indicate" in identifiability.md and identifiable_functions.md
- Fix "a parameter or a states" -> "a parameter or a state" in identifiability.md and discrete_time.md
- Fix "what does non-identifiability...looks like" -> "what non-identifiability...looks like" in identifiable_functions.md
- Fix missing comma after year=2023 in BibTeX citation in index.md
- Fix incorrectly concatenated author names in README.md reference [2]

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <[email protected]>

Author: ChrisRackauckas

README.md

@@ -183,8 +183,7 @@ Ruiwen Dong, Christian Goodbrake, Heather Harrington, and Gleb Pogudin,
 SIAM Journal on Applied Algebra and Geometry, 2023.
 
 <a id="epidem">[2]</a>
-Yuganthi R. Liyanage, Omar Saucedo, Necibe Tuncer, and Gerardo Chowell
-Hongyu Miao, Xiaohua Xia, Alan S. Perelson, and Hulin Wu,
+Yuganthi R. Liyanage, Omar Saucedo, Necibe Tuncer, and Gerardo Chowell,
 [*A Tutorial on Structural Identifiability of Epidemic Models Using StructuralIdentifiability.jl*](https://arxiv.org/abs/2505.10517),
 preprint, 2025.
 

docs/src/index.md

@@ -19,7 +19,7 @@ Pkg.add("StructuralIdentifiability")
   title   = {Differential Elimination for Dynamical Models via Projections with Applications to Structural Identifiability},
   journal = {SIAM Journal on Applied Algebra and Geometry},
   url     = {https://doi.org/10.1137/22M1469067},
-  year    = {2023}
+  year    = {2023},
   volume  = {7},
   number  = {1},
   pages   = {194-235}

docs/src/tutorials/discrete_time.md

@@ -9,7 +9,7 @@ $\begin{cases}
 
 where $\mathbf{x}(t), \mathbf{y}(t)$, and $\mathbf{u}(t)$ are time-dependent states, outputs, and inputs, respectively,
 and $\mathbf{p}$ are scalar parameters.
-As in the ODE case, we will call that a parameter or a states (or a function of them) is **identifiable** if its value can be recovered from
+As in the ODE case, we will call a parameter or a state (or a function of them) **identifiable** if its value can be recovered from
 time series for inputs and outputs (in the generic case, see Definition 3 in [^1] for details).
 Again, we will distinguish two types of identifiability
 

docs/src/tutorials/identifiability.md

@@ -9,7 +9,7 @@ $\begin{cases}
 
 where $\mathbf{x}(t), \mathbf{y}(t)$, and $\mathbf{u}(t)$ are time-dependent states, outputs, and inputs, respectively,
 and $\mathbf{p}$ are scalar parameters.
-We will call that a parameter or a states (or a function of them) is **identifiable** if its value can be recovered from
+We will call a parameter or a state (or a function of them) **identifiable** if its value can be recovered from
 time series for inputs and outputs.
 Typically, two types of identifiability are distinguished
 
@@ -112,7 +112,7 @@ Similarly to `assess_local_identifiability`, this function has optional paramete
   - `loglevel` (default `Logging.Info`). The minimal level of logging messages to be displayed. Available options: `Logging.Debug`,
     `Logging.Info`, `Logging.Warn`, and `Logging.Error`.
 
-Using `funcs_to_check` parameter, one can further inverstigate the nature of the lack of identifiability in the model at hand.
+Using `funcs_to_check` parameter, one can further investigate the nature of the lack of identifiability in the model at hand.
 For example, for the Goodwin oscillator, we can check if `beta + delta` and `beta * delta` are identifiable:
 
 ```@example global
@@ -125,7 +125,7 @@ can be exchanged. One may wonder how could we guess these functions `beta + delt
 
 ## Assuming known initial conditions
 
-An experimental feature allows to provide an additional keyword argument `known_ic` to inidcate functions of states and parameters for which the
+An experimental feature allows to provide an additional keyword argument `known_ic` to indicate functions of states and parameters for which the
 initial conditions are assumed to be known (while the initial conditions of the system are still assumed to be generic). In this case,
 the identifiability will be assessed for parameters and all the initial conditions or for the initial conditions of `funcs_to_check`.
 Let us add an assumption that the initial conditions $x_2(0)$ and $x_3(0)$ are known:

docs/src/tutorials/identifiable_functions.md

@@ -1,9 +1,9 @@
 # Globally Identifiable Functions
 
-In addition to assessing identifiabuility of given functions of parameters or states, `StructuralIdentifiability.jl`
+In addition to assessing identifiability of given functions of parameters or states, `StructuralIdentifiability.jl`
 provides the function `find_identifiable_functions` which finds a set of identifiable functions such that any other
 identifiable function can be expressed using them.
-This allows to find out what actually is identifiable and what does non-identifiability in the model at hand looks like.
+This allows to find out what actually is identifiable and what non-identifiability in the model at hand looks like.
 
 For example, consider the following model[^1].
 
@@ -43,7 +43,7 @@ the degree of simplification. The default value is `:standard` but one could use
 As `assess_identifiability` and `assess_local_identifiability`, `find_identifiable_functions` accepts an optional parameter `loglevel` (default: `Logging.Info`)
 to adjust the verbosity of logging.
 
-Finally, as for `assess_identifiability`, an experimental feature allows to provide an additional keyword argument `known_ic` to inidcate functions of states and parameters for which the
+Finally, as for `assess_identifiability`, an experimental feature allows to provide an additional keyword argument `known_ic` to indicate functions of states and parameters for which the
 initial conditions are assumed to be known (while the initial conditions of the system are still assumed to be generic).
 In this case, the function will find identifiable functions of parameters and initial conditions rather than of parameters and states.
 Let us add an assumption that the initial condition `x1(0)` is known: