Doc: spelling fixes

docsrc/api.rst

@@ -5,7 +5,7 @@ API Reference
 #############
 
 The following documents the public API of CmdStanPy. It is expected to be stable between versions,
-with backwards compatibility between minor versions and deprecation warnings preceeding breaking changes.
+with backwards compatibility between minor versions and deprecation warnings preceding breaking changes.
 There is also the `internal API <internal_api.rst>`__, which is makes no such guarantees.
 
 .. toctree::

docsrc/index.rst

@@ -10,7 +10,7 @@
 .. module:: cmdstanpy
    :synopsis: A lightweight pure-Python interface to CmdStan which provides access to the Stan compiler and all inference algorithms.
 
-.. moduleauthor:: Stan Developement Team
+.. moduleauthor:: Stan Development Team
 
 CmdStanPy is a lightweight interface to Stan for Python users which
 provides the necessary objects and functions to do Bayesian inference

docsrc/installation.rst

@@ -158,7 +158,7 @@ To install the Xcode command line tools, run the shell command: ``xcode-select -
 
 **Windows**  We recommend using the `RTools 4.0 <https://cran.r-project.org/bin/windows/Rtools/rtools40.html>`_ toolchain
 which contains a ``g++ 8`` compiler and ``Mingw``, the native Windows equivalent of the GNU-Make utility.
-This can be installed allong with CmdStan when you invoke the function :meth:`cmdstanpy.install_cmdstan`
+This can be installed along with CmdStan when you invoke the function :meth:`cmdstanpy.install_cmdstan`
 with argument ``compiler=True``.
 
 

docsrc/users-guide/examples/Run Generated Quantities.ipynb

@@ -133,7 +133,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We run the `generate_quantities` method on `bernoulli_ppc` using existing sample `fit` as input.  The `generate_quantities` method takes the values of `theta` in the `fit` sample as the set of draws from the posterior used to generate the corresponsing `y_rep` quantities of interest.\n",
+    "We run the `generate_quantities` method on `bernoulli_ppc` using existing sample `fit` as input.  The `generate_quantities` method takes the values of `theta` in the `fit` sample as the set of draws from the posterior used to generate the corresponding `y_rep` quantities of interest.\n",
     "\n",
     "The arguments to the `generate_quantities` method are:\n",
     " + `data`  - the data used to fit the model\n",
@@ -153,7 +153,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The `generate_quantities` method returns a `CmdStanGQ` object which contains the values for all variables in the generated quantitites block of the program ``bernoulli_ppc.stan``.  Unlike the output from the ``sample`` method, it doesn't contain any information on the joint log probability density, sampler state, or parameters or transformed parameter values.\n",
+    "The `generate_quantities` method returns a `CmdStanGQ` object which contains the values for all variables in the generated quantities block of the program ``bernoulli_ppc.stan``.  Unlike the output from the ``sample`` method, it doesn't contain any information on the joint log probability density, sampler state, or parameters or transformed parameter values.\n",
     "\n",
     "In this example, each draw consists of the N-length array of replicate of the `bernoulli` model's input variable  `y`, which is an N-length array of Bernoulli outcomes."
    ]

docsrc/users-guide/examples/VI as Sampler Inits.ipynb

@@ -128,7 +128,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Using the default random parameter initializations, we need to run more warmup iteratons. If we only run 75 warmup iterations with random inits, the result fails to estimate `sigma` correctly.  It is necessary to run the model with at least 150 warmup iterations to produce a good set of estimates."
+    "Using the default random parameter initializations, we need to run more warmup iterations. If we only run 75 warmup iterations with random inits, the result fails to estimate `sigma` correctly.  It is necessary to run the model with at least 150 warmup iterations to produce a good set of estimates."
    ]
   },
   {

docsrc/users-guide/overview.rst

@@ -31,7 +31,7 @@ CmdStanPy is designed to support the development, testing, and deployment of a S
 CmdStanPy manages the Stan program files, data files, and CmdStan output files.
 By default, output files are written to a temporary filesystem which persists
 throughout the session.  This is appropriate behavior during model development
-because it allows the user to test many models without filsystem clutter or worse.
+because it allows the user to test many models without filesystem clutter or worse.
 Once deployed into production, 
 the user can specify the output directory for the CmdStan outputs.