minor changes to docs

docs/Credits to those who helped.rst

@@ -15,4 +15,5 @@ During the process of writing *reliability* there have been many problems that I
 - `Thomas Enzinger <https://github.com/TEFEdotCC>`_ for help in improving the method of finding the area in stress-strength interference between any two distributions. Previously this was done using a monte-carlo method, but Thomas' method is much more accurate and always consistent. This is incorporated in Version 0.5.0.
 - `Karthick Mani <https://www.linkedin.com/in/manikarthick/>`_ for help implementing the Loglogistic and Gumbel Distributions including implementation of these distributions in Fitters and Probability_plotting.
 - Jake Sadie for identifying an error in the formula used for stress-strength interference of any two distributions. This error has been corrected in version 0.5.7.
-- Ed Burrows for x10 speed improvement to optimal_replacement_time by using numpy.vectorize.
+- Ed Burrows for x10 speed improvement to optimal_replacement_time by using numpy.vectorize.
+- Adam Gary for help incorporating the Crow-AMSAA model into the reliability_growth function.

docs/Reliability growth.rst

@@ -34,7 +34,7 @@ The algorithm to fit the model is as follows:
 
 This gives us the model parameters of :math:`b` and :math:`\alpha`. The formulas for the other reported values are:
 
-:math:`DMTBF_C = b.(T^{\alpha})`. This is the demonstrated MTBF (cumulative) and is reported in the results as DMTBF_C.
+:math:`DMTBF_C = b.T^{\alpha}`. This is the demonstrated MTBF (cumulative) and is reported in the results as DMTBF_C.
 
 :math:`DFI_C = \frac{1}{DMTBF_C}`. This is the demonstrated failure intensity (cumulative) and is reported in the results as DFI_C.
 
@@ -71,7 +71,7 @@ This gives us the model parameters :math:`\beta` and :math:`\lambda`. The formul
 
 :math:`DFI_C = \frac{1}{DMTBF_C}`. This is the demonstrated failure intensity (cumulative) and is reported in the results as DFI_C.
 
-The time to reach the target MTBF is calculated as :math:`t_{target} = \left(\frac{1}{\lambda . \textrm{target_MTBF}} \right)^ \frac{1}{\beta - 1}`
+The time to reach the target MTBF is calculated as :math:`t_{target} = \left(\frac{1}{\lambda . (\textrm{target MTBF}}) \right)^ \frac{1}{\beta - 1}`
 
 For more information see `reliawiki <http://reliawiki.org/index.php/Crow-AMSAA_(NHPP)>`_.
 
@@ -82,7 +82,7 @@ For more information see `reliawiki <http://reliawiki.org/index.php/Crow-AMSAA_(
 Example 1
 """""""""
 
-In this first example, we import a dataset and fit the Duane model. Log_scale is set to True (which only affects the plot). The target MTBF is 35 which will give us the time to reach the target MTBF based on the model.
+In this first example, we import a dataset and fit the Duane model. For the plot log_scale is set to True. The target MTBF is 35 which will give us the time to reach the target MTBF based on the model.
 
 .. code:: python
 

reliability/Repairable_systems.py

@@ -84,8 +84,7 @@ class reliability_growth:
 
     Notes
     -----
-    For more information see http://reliawiki.org/index.php/Crow-AMSAA_(NHPP)
-    and http://reliawiki.org/index.php/Duane_Model
+    For more information see the `documentation <https://reliability.readthedocs.io/en/latest/Reliability%20growth.html>`_.
     """
 
     def __init__(
@@ -163,7 +162,7 @@ def __init__(
         if print_results is True:
             if model == "Crow-AMSAA":
                 colorprint(
-                    "Crow-AMSAA Reliability growth model parameters:",
+                    "Crow-AMSAA reliability growth model parameters:",
                     bold=True,
                     underline=True,
                 )
@@ -172,7 +171,7 @@ def __init__(
                 print("Growth rate:", round_to_decimals(self.growth_rate))
             else:  # Duane
                 colorprint(
-                    "Duane Reliability growth model parameters:",
+                    "Duane reliability growth model parameters:",
                     bold=True,
                     underline=True,
                 )