Merge remote-tracking branch 'origin/master'

docs/src/tutorials/ode_example.md

@@ -73,7 +73,7 @@ sol = solve(prob)
 ```
 
 The solvers can be controlled using the available options are described on the
-[Common Solver Options manual page](../basics/common_solver_opts.html). For example,
+[Common Solver Options manual page](../../basics/common_solver_opts.html). For example,
 we can lower the relative tolerance (in order to get a more correct result, at
 the cost of more timesteps) by using the command `reltol`:
 
@@ -144,7 +144,7 @@ In DifferentialEquations.jl, some good "go-to" choices for ODEs are:
 - `CVODE_BDF()` for stiff equations on `Vector{Float64}`.
 
 For a comprehensive list of the available algorithms and detailed recommendations,
-[Please see the solver documentation](../solvers/ode_solve.html). Every problem
+[Please see the solver documentation](../../solvers/ode_solve.html). Every problem
 type has an associated page detailing all of the solvers associated with the problem.
 
 ### Step 3: Analyzing the Solution
@@ -191,11 +191,11 @@ step, while `(t)` is an interpolation at time `t`!
 If in the solver `dense=true` (this is the default unless `saveat` is used), then
 this interpolation is a high order interpolation and thus usually matches the
 error of the solution time points. The interpolations associated with each solver
-is [detailed at the solver algorithm page](../solvers/ode_solve.html). If `dense=false`
+is [detailed at the solver algorithm page](../../solvers/ode_solve.html). If `dense=false`
 (unless specifically set, this only occurs when `save_everystep=false` or `saveat`
 is used) then this defaults to giving a linear interpolation.
 
-For details on more handling the output, see [the solution handling page](../basics/solution.html).
+For details on more handling the output, see [the solution handling page](../../basics/solution.html).
 
 #### Plotting Solutions
 
@@ -220,7 +220,7 @@ gui()
 ```
 
 The plot function can be formatted using [the attributes available in Plots.jl](https://juliaplots.github.io/).
-Additional DiffEq-specific controls are documented [at the plotting page](../basics/plot.html).
+Additional DiffEq-specific controls are documented [at the plotting page](../../basics/plot.html).
 
 For example, from the Plots.jl attribute page we see that the line width can be
 set via the argument `linewidth`. Additionally, a title can be set with `title`.
@@ -463,11 +463,11 @@ In many cases, the common workflow only starts with solving the differential equ
 Many common setups have built-in solutions in DifferentialEquations.jl. For example,
 check out the features for:
 
-- [Handling, parallelizing, and analyzing large Monte Carlo experiments](../features/monte_carlo.html)
-- [Saving the output to tabular formats like DataFrames and CSVs](../features/io.html)
-- [Event handling](../features/callback_functions.html)
-- [Parameter estimation (inverse problems)](../analysis/parameter_estimation.html)
-- [Quantification of numerical uncertainty and error](../analysis/uncertainty_quantification.html)
+- [Handling, parallelizing, and analyzing large Monte Carlo experiments](../../features/monte_carlo.html)
+- [Saving the output to tabular formats like DataFrames and CSVs](../../features/io.html)
+- [Event handling](../../features/callback_functions.html)
+- [Parameter estimation (inverse problems)](../../analysis/parameter_estimation.html)
+- [Quantification of numerical uncertainty and error](../../analysis/uncertainty_quantification.html)
 
 Many more are defined in the relevant sections of the docs. Please explore the rest
 of the documentation, including tutorials for getting started with other types

docs/src/tutorials/rode_example.md

@@ -28,7 +28,7 @@ sol = solve(prob,RandomEM(),dt=1/100)
 
 The random process defaults to a Gaussian/Wiener process, so there is nothing
 else required here! See the documentation on
-[`NoiseProcess`es](../features/noise_process.html) for details on how to define
+[`NoiseProcess`es](../../features/noise_process.html) for details on how to define
 other noise proceses.
 
 ## Example 2: Systems of RODEs

docs/src/tutorials/sde_example.md

@@ -108,7 +108,7 @@ constructor:
 monte_prob = MonteCarloProblem(prob)
 ```
 
-The solver commands are defined [at the Monte Carlo page](../features/monte_carlo.html).
+The solver commands are defined [at the Monte Carlo page](../../features/monte_carlo.html).
 For example we can choose to have 1000 trajectories via `num_monte=1000`. In addition,
 this will automatically parallelize using Julia native parallelism if extra processes
 are added via `addprocs()`, but we can change this to use multithreading via
@@ -254,7 +254,7 @@ multiplication.
 
 ## Example 4: Colored Noise
 
-Colored noise can be defined [using the Noise Process interface](../features/noise_process.html).
+Colored noise can be defined [using the Noise Process interface](../../features/noise_process.html).
 In that portion of the docs, it is shown how to define your own noise process
 `my_noise`, which can be passed to the SDEProblem
 
@@ -307,4 +307,4 @@ SDEProblem(f,g,u0,tspan,noise=heston_noise)
 Of course, to fully define this problem we need to define our constants. Constructors
 for making common models like this easier to define can be found in the modeling
 toolkits. For example, the `HestonProblem` is pre-defined as part of the
-[financial modeling tools](../models/financial.html).
+[financial modeling tools](../../models/financial.html).

docs/src/types/rode_types.md

@@ -27,7 +27,7 @@ Defines the RODE with the specified functions. The default noise is `WHITE_NOISE
 * `tspan`: The timespan for the problem.
 * `noise`: The noise process applied to the noise upon generation. Defaults to
   Gaussian white noise. For information on defining different noise processes,
-  see [the noise process documentation page](../features/noise_process.html)
+  see [the noise process documentation page](../../features/noise_process.html)
 * `noise_prototype`: A prototype type instance for the noise vector. It defaults
   to `nothing`, which means the problem should be interpreted as having a noise
   vector whose size matches `u0`.

docs/src/types/sde_types.md

@@ -38,7 +38,7 @@ Defines the SDE with the specified functions. The default noise is `WHITE_NOISE`
 * `tspan`: The timespan for the problem.
 * `noise`: The noise process applied to the noise upon generation. Defaults to
   Gaussian white noise. For information on defining different noise processes,
-  see [the noise process documentation page](../features/noise_process.html)
+  see [the noise process documentation page](../../features/noise_process.html)
 * `noise_rate_prototype`: A prototype type instance for the noise rates, that
   is the output `g`. It can be any type which overloads `A_mul_B!` with itself
   being the middle argument. Commonly, this is a matrix or sparse matrix. If