diff --git a/docs/source/manuals/evaluation.md b/docs/source/manuals/evaluation.md
index d9c1b79..e5404a3 100644
--- a/docs/source/manuals/evaluation.md
+++ b/docs/source/manuals/evaluation.md
@@ -72,8 +72,8 @@ one specific _sample generation procotol_ to genereate many
 samples, typically characterized by widely varying amounts of 
 _shift_ with respect to the original distribution, that are then
 used to evaluate the performance of a (trained) quantifier. 
-These protocols are explained in more detail in a dedicated [entry 
-in the wiki](Protocols.md). For the moment being, let us assume we already have
+These protocols are explained in more detail in a dedicated [manual](./protocols.md).
+For the moment being, let us assume we already have
 chosen and instantiated one specific such protocol, that we here
 simply call _prot_. Let also assume our model is called
 _quantifier_ and that our evaluatio measure of choice is 
diff --git a/docs/source/manuals/explicit-loss-minimization.md b/docs/source/manuals/explicit-loss-minimization.md
index 23a07ea..f80c434 100644
--- a/docs/source/manuals/explicit-loss-minimization.md
+++ b/docs/source/manuals/explicit-loss-minimization.md
@@ -5,14 +5,14 @@ SVM(Q), SVM(KLD), SVM(NKLD), SVM(AE), or SVM(RAE).
 These methods require to first download the 
 [svmperf](http://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html) 
 package, apply the patch 
-[svm-perf-quantification-ext.patch](./svm-perf-quantification-ext.patch), and compile the sources.
-The script [prepare_svmperf.sh](prepare_svmperf.sh) does all the job. Simply run:
+[svm-perf-quantification-ext.patch](https://github.com/HLT-ISTI/QuaPy/blob/master/svm-perf-quantification-ext.patch), and compile the sources.
+The script [prepare_svmperf.sh](https://github.com/HLT-ISTI/QuaPy/blob/master/prepare_svmperf.sh) does all the job. Simply run:
 
 ```
 ./prepare_svmperf.sh
 ```
 
-The resulting directory [svm_perf_quantification](./svm_perf_quantification) contains the
+The resulting directory `svm_perf_quantification/` contains the
 patched version of _svmperf_ with quantification-oriented losses. 
 
 The [svm-perf-quantification-ext.patch](https://github.com/HLT-ISTI/QuaPy/blob/master/prepare_svmperf.sh) is an extension of the patch made available by
@@ -22,5 +22,5 @@ the _Q_ measure as proposed by [Barranquero et al. 2015](https://www.sciencedire
 and for the _KLD_ and _NKLD_ measures as proposed by [Esuli et al. 2015](https://dl.acm.org/doi/abs/10.1145/2700406?casa_token=8D2fHsGCVn0AAAAA:ZfThYOvrzWxMGfZYlQW_y8Cagg-o_l6X_PcF09mdETQ4Tu7jK98mxFbGSXp9ZSO14JkUIYuDGFG0).
 This patch extends the above one by also allowing SVMperf to optimize for 
 _AE_ and _RAE_.
-See [Methods.md](Methods.md) for more details and code examples.
+See the [](./methods) manual for more details and code examples.
 
diff --git a/docs/source/manuals/methods.md b/docs/source/manuals/methods.md
index 03c5c2a..9536820 100644
--- a/docs/source/manuals/methods.md
+++ b/docs/source/manuals/methods.md
@@ -414,8 +414,8 @@ model.fit(dataset.training)
 estim_prevalence = model.quantify(dataset.test.instances)
 ```
 
-Check the examples _[explicit_loss_minimization.py](..%2Fexamples%2Fexplicit_loss_minimization.py)_
-and [one_vs_all.py](..%2Fexamples%2Fone_vs_all.py) for more details.
+Check the examples on [explicit_loss_minimization](https://github.com/HLT-ISTI/QuaPy/blob/devel/examples/5.explicit_loss_minimization.py)
+and on [one versus all quantification](https://github.com/HLT-ISTI/QuaPy/blob/devel/examples/10.one_vs_all.py) for more details.
 
 ### Kernel Density Estimation methods (KDEy)