remove dash between url and citations

paper.md

@@ -30,11 +30,11 @@ The package supports programmatic model definition. Once the model is defined, t
 
 Due to the complexity of DSGE models, efficient numerical tools are required, as analytical solutions are often unavailable. `MacroModelling.jl` serves as a tool for handling the complexities involved, such as forward-looking expectations, nonlinearity, and high dimensionality.
 
-`MacroModelling.jl` differentiates itself among macroeconomic modelling packages by offering a unique blend of capabilities and conveniences, such as automatic declaration of variables and parameters, automatic differentiation with respect to parameters, and support for perturbation solution up to 3rd order. While it operates within the Julia environment, it presents an alternative to the MATLAB-dominated field, which includes [dynare](https://www.dynare.org) - [@dynare], [RISE](https://github.com/jmaih/RISE_toolbox) - [@rise], [Taylor Projection](https://sites.google.com/site/orenlevintal/taylor-projection) - [@taylorprojection], [NBTOOLBOX](https://github.com/Coksp1/NBTOOLBOX/tree/main/Documentation), and [IRIS](https://iris.igpmn.org), the latter two being capable of providing only 1st order perturbation solutions.
+`MacroModelling.jl` differentiates itself among macroeconomic modelling packages by offering a unique blend of capabilities and conveniences, such as automatic declaration of variables and parameters, automatic differentiation with respect to parameters, and support for perturbation solution up to 3rd order. While it operates within the Julia environment, it presents an alternative to the MATLAB-dominated field, which includes [dynare](https://www.dynare.org) [@dynare], [RISE](https://github.com/jmaih/RISE_toolbox) [@rise], [Taylor Projection](https://sites.google.com/site/orenlevintal/taylor-projection) [@taylorprojection], [NBTOOLBOX](https://github.com/Coksp1/NBTOOLBOX/tree/main/Documentation), and [IRIS](https://iris.igpmn.org), the latter two being capable of providing only 1st order perturbation solutions.
 
-Other Julia-based packages such as [DSGE.jl](https://github.com/FRBNY-DSGE/DSGE.jl), [StateSpaceEcon.jl](https://bankofcanada.github.io/DocsEcon.jl/dev/), [SolveDSGE.jl](https://github.com/RJDennis/SolveDSGE.jl), and [DifferentiableStateSpaceModels.jl](https://github.com/HighDimensionalEconLab/DifferentiableStateSpaceModels.jl) - [@diffstatespace] have functionalities similar to those of `MacroModelling.jl`. However, the former are not as general and convenience-focused as the MATLAB packages and `MacroModelling.jl`. The Julia-based packages are missing convenience functionalities such as automatic creation of auxiliary variables for variables in lead and lag larger than 1, or programmatic model definition. These functionalities are convenient to the user but require significant effort to implement in the parser. Furthermore, the other Julia-based packages do not possess the unique feature set of `MacroModelling.jl` regarding variable declaration and automatic differentiation. Notably, the Python-based [dolo.py](https://www.econforge.org/dolo.py/) offers global solutions, but does not include estimation and balanced growth path features which are available in `MacroModelling.jl`.
+Other Julia-based packages such as [DSGE.jl](https://github.com/FRBNY-DSGE/DSGE.jl), [StateSpaceEcon.jl](https://bankofcanada.github.io/DocsEcon.jl/dev/), [SolveDSGE.jl](https://github.com/RJDennis/SolveDSGE.jl), and [DifferentiableStateSpaceModels.jl](https://github.com/HighDimensionalEconLab/DifferentiableStateSpaceModels.jl) [@diffstatespace] have functionalities similar to those of `MacroModelling.jl`. However, the former are not as general and convenience-focused as the MATLAB packages and `MacroModelling.jl`. The Julia-based packages are missing convenience functionalities such as automatic creation of auxiliary variables for variables in lead and lag larger than 1, or programmatic model definition. These functionalities are convenient to the user but require significant effort to implement in the parser. Furthermore, the other Julia-based packages do not possess the unique feature set of `MacroModelling.jl` regarding variable declaration and automatic differentiation. Notably, the Python-based [dolo.py](https://www.econforge.org/dolo.py/) offers global solutions, but does not include estimation and balanced growth path features which are available in `MacroModelling.jl`.
 
-`MacroModelling.jl` stands out as one of the few packages that can solve non-stochastic steady states symbolically, a feature shared only with [gEcon](http://gecon.r-forge.r-project.org) - [@gecon], an R-based package. When, as in most cases, symbolic solution is not possible `MacroModelling.jl` uses symbolic simplification, search space transformation, automatic domain restrictions, restarts with different initial values, warm starts using previous solutions, and a Levenberg-Marquardt-type optimizer. The combination of these elements makes it possible to solve all 16 models currently implemented in the examples out-of-the box. This is remarkable because all other packages rely either on analytical NSSS derivation by hand, or on a smaller subset of the features outlined above. In general this makes the other packages far less reliable in finding the NSSS without further information (e.g. a close enough initial guess). Furthermore, unlike many of its competitors, the domain-specific model language of `MacroModelling.jl` is integrated into the Julia language, which makes for convenient reading and coding, with the help of Julia macros.
+`MacroModelling.jl` stands out as one of the few packages that can solve non-stochastic steady states symbolically, a feature shared only with [gEcon](http://gecon.r-forge.r-project.org [@gecon], an R-based package. When, as in most cases, symbolic solution is not possible `MacroModelling.jl` uses symbolic simplification, search space transformation, automatic domain restrictions, restarts with different initial values, warm starts using previous solutions, and a Levenberg-Marquardt-type optimizer. The combination of these elements makes it possible to solve all 16 models currently implemented in the examples out-of-the box. This is remarkable because all other packages rely either on analytical NSSS derivation by hand, or on a smaller subset of the features outlined above. In general this makes the other packages far less reliable in finding the NSSS without further information (e.g. a close enough initial guess). Furthermore, unlike many of its competitors, the domain-specific model language of `MacroModelling.jl` is integrated into the Julia language, which makes for convenient reading and coding, with the help of Julia macros.
 
 # Example