Merge pull request #141 from stan-dev/bugfix/lowercase-poisson

Use Poisson spelling with capital P

Author: mitzimorris

src/functions-reference/unbounded_discrete_distributions.Rmd

@@ -340,10 +340,10 @@ $30 \log 2$. For a description of argument and return types, see section
 ## Poisson-Log Generalised Linear Model (Poisson Regression) {#poisson-log-glm}
 
 Stan also supplies a single primitive for a Generalised Linear Model
-with poisson likelihood and log link function, i.e. a primitive for a
-poisson regression. This should provide a more efficient
-implementation of poisson regression than a manually written
-regression in terms of a poisson likelihood and matrix multiplication.
+with Poisson likelihood and log link function, i.e. a primitive for a
+Poisson regression. This should provide a more efficient
+implementation of Poisson regression than a manually written
+regression in terms of a Poisson likelihood and matrix multiplication.
 
 ### Probability Mass Function
 
@@ -367,7 +367,7 @@ dropping constant additive terms.
 \index{{\tt \bfseries poisson\_log\_glm\_lpmf }!{\tt (int[] y \textbar\ matrix x, real alpha, vector beta): real}|hyperpage}
 
 `real` **`poisson_log_glm_lpmf`**`(int[] y | matrix x, real alpha, vector beta)`<br>\newline
-The log poisson probability mass of y given log-rate `alpha+x*beta`,
+The log Poisson probability mass of y given log-rate `alpha+x*beta`,
 where a constant intercept `alpha` is used for all observations. The
 number of rows of the independent variable matrix `x` needs to match
 the length of the dependent variable vector `y` and the number of
@@ -377,7 +377,7 @@ columns of `x` needs to match the length of the weight vector `beta`.
 \index{{\tt \bfseries poisson\_log\_glm\_lpmf }!{\tt (int[] y \textbar\ matrix x, vector alpha, vector beta): real}|hyperpage}
 
 `real` **`poisson_log_glm_lpmf`**`(int[] y | matrix x, vector alpha, vector beta)`<br>\newline
-The log poisson probability mass of y given log-rate `alpha+x*beta`,
+The log Poisson probability mass of y given log-rate `alpha+x*beta`,
 where an intercept `alpha` is used that is allowed to vary with the
 different observations. The number of rows of the independent variable
 matrix `x` needs to match the length of the dependent variable vector