Clean docs (#1798)

* added links and formatting

* update docs via `make notebooks`

* Updated rstan build files (by running R CMD INSTALL .)

R/src/Makevars

@@ -3,13 +3,7 @@
 STANHEADERS_SRC = $(shell "$(R_HOME)/bin$(R_ARCH_BIN)/Rscript" -e "message()" -e "cat(system.file('include', 'src', package = 'StanHeaders', mustWork = TRUE))" -e "message()" | grep "StanHeaders")
 
 PKG_CPPFLAGS = -I"../inst/include" -I"$(STANHEADERS_SRC)" -DBOOST_DISABLE_ASSERTS -DEIGEN_NO_DEBUG -DBOOST_MATH_OVERFLOW_ERROR_POLICY=errno_on_error
+PKG_CXXFLAGS = $(shell "$(R_HOME)/bin$(R_ARCH_BIN)/Rscript" -e "RcppParallel::CxxFlags()") $(shell "$(R_HOME)/bin$(R_ARCH_BIN)/Rscript" -e "StanHeaders:::CxxFlags()")
+PKG_LIBS = $(shell "$(R_HOME)/bin$(R_ARCH_BIN)/Rscript" -e "RcppParallel::RcppParallelLibs()") $(shell "$(R_HOME)/bin$(R_ARCH_BIN)/Rscript" -e "StanHeaders:::LdFlags()")
 
 CXX_STD = CXX14
-
-all: $(SHLIB)
-		@if test -e "/usr/bin/install_name_tool" && test -e "/usr/local/clang4/lib/libc++.1.dylib" && test -e "/usr/lib/libc++.1.dylib"; then /usr/bin/install_name_tool -change /usr/local/clang4/lib/libc++.1.dylib /usr/lib/libc++.1.dylib $(SHLIB); fi
-
-clean:
-		rm -rf *.so *.o
-
-.phony: all clean

R/src/Makevars.win

@@ -2,13 +2,8 @@
 
 STANHEADERS_SRC = $(shell "$(R_HOME)/bin$(R_ARCH_BIN)/Rscript" -e "message()" -e "cat(system.file('include', 'src', package = 'StanHeaders', mustWork = TRUE))" -e "message()" | grep "StanHeaders")
 
-PKG_CPPFLAGS = -I"../inst/include" -I"$(STANHEADERS_SRC)" -DBOOST_DISABLE_ASSERTS -DEIGEN_NO_DEBUG
+PKG_CPPFLAGS = -I"../inst/include" -I"$(STANHEADERS_SRC)" -DBOOST_DISABLE_ASSERTS -DEIGEN_NO_DEBUG -DRCPP_PARALLEL_USE_TBB=1
+PKG_CXXFLAGS = $(shell "$(R_HOME)/bin$(R_ARCH_BIN)/Rscript" -e "RcppParallel::CxxFlags()") $(shell "$(R_HOME)/bin$(R_ARCH_BIN)/Rscript" -e "StanHeaders:::CxxFlags()")
+PKG_LIBS = $(shell "$(R_HOME)/bin$(R_ARCH_BIN)/Rscript" -e "RcppParallel::RcppParallelLibs()") $(shell "$(R_HOME)/bin$(R_ARCH_BIN)/Rscript" -e "StanHeaders:::LdFlags()")
 
 CXX_STD = CXX14
-
-all: $(SHLIB)
-
-clean:
-		RM -rf *.so *.o
-
-.phony: all clean

docs/_docs/additional_topics.md

@@ -55,11 +55,15 @@ The json file will be portable across systems, and deserialization is backwards
 For time series that exhibit strong seasonality patterns rather than trend changes, it may be useful to force the trend growth rate to be flat. This can be achieved simply by passing `growth=flat` when creating the model:
 
 
+```R
+# R
+m <- prophet(df, growth='flat')
+```
 ```python
 # Python
 m = Prophet(growth='flat')
 ```
-This is currently implemented only in the Python version of Prophet. Note that if this is used on a time series that doesn't have a constant trend, any trend will be fit with the noise term and so there will be high predictive uncertainty in the forecast.
+Note that if this is used on a time series that doesn't have a constant trend, any trend will be fit with the noise term and so there will be high predictive uncertainty in the forecast.
 
 
 

docs/_docs/contributing.md

@@ -17,9 +17,9 @@ The following sections will describe how you can submit a pull request for addin
 
 ## 1. Forking the Prophet Repo
 
-You will need your own fork to work on the code. Go to the fbprophet project
-page https://github.com/facebook/prophet and hit the ``Fork`` button. You will
-want to clone your fork to your machine::
+You will need your own fork to work on the code. Go to the [fbprophet project
+page](https://github.com/facebook/prophet) and hit the ``Fork`` button. You will
+want to clone your fork to your machine:
 
 ```
 $ git clone https://github.com/your-user-name/prophet.git
@@ -36,7 +36,7 @@ development environment. This should contain the required dependencies.
 
 ### Python
 
-- Install either Anaconda (https://www.anaconda.com/download/) or miniconda (https://conda.io/miniconda.html)
+- Install either Anaconda [anaconda](https://www.anaconda.com/download/) or [miniconda](https://conda.io/miniconda.html)
 - Make sure your conda is up to date (``conda update conda``)
 - ``cd`` to the *prophet* source directory that you have cloned
 
@@ -56,7 +56,7 @@ $ pip install -r requirements.txt
 
 ### R
 
-Dependencies can be managed through ``Packrat`` (https://rstudio.github.io/packrat/) or ``renv`` (https://rstudio.github.io/renv/articles/renv.html).
+Dependencies can be managed through [``Packrat``](https://rstudio.github.io/packrat/) or [``renv``](https://rstudio.github.io/renv/articles/renv.html).
 
 For ``renv``, you must first initialise a new project local environment. 
 ```R
@@ -110,7 +110,7 @@ $ conda info -e
 $ conda deactivate
 ```
 
-See the full conda docs here http://conda.pydata.org/docs.
+See the full conda docs [here](http://conda.pydata.org/docs).
 
 ### R
 

docs/_docs/seasonality,_holiday_effects,_and_regressors.md

@@ -316,7 +316,7 @@ fig = m.plot_components(forecast)
 
 
 
-Seasonalities are estimated using a partial Fourier sum. See [the paper](https://peerj.com/preprints/3190/) for complete details, and [this figure on Wikipedia](https://en.wikipedia.org/wiki/Fourier_series#/media/File:Fourier_Series.svg) for an illustration of how a partial Fourier sum can approximate an arbitrary periodic signal. The number of terms in the partial sum (the order) is a parameter that determines how quickly the seasonality can change. To illustrate this, consider the Peyton Manning data from the [Quickstart](https://facebook.github.io/prophet/docs/quick_start.html). The default Fourier order for yearly seasonality is 10, which produces this fit:
+Seasonalities are estimated using a partial Fourier sum. See [the paper](https://peerj.com/preprints/3190/) for complete details, and [this figure on Wikipedia](https://en.wikipedia.org/wiki/Fourier_series#/media/File:Fourier_Series.svg) for an illustration of how a partial Fourier sum can approximate an aribtrary periodic signal. The number of terms in the partial sum (the order) is a parameter that determines how quickly the seasonality can change. To illustrate this, consider the Peyton Manning data from the [Quickstart](https://facebook.github.io/prophet/docs/quick_start.html). The default Fourier order for yearly seasonality is 10, which produces this fit:
 
 
 ```R