Review fixes

.travis.yml

@@ -16,5 +16,4 @@ git:
 #  - julia -e 'Pkg.clone(pwd()); Pkg.build("StatsBase"); Pkg.test("StatsBase"; coverage=true)'
 after_success:
   - julia -e 'cd(Pkg.dir("StatsBase")); Pkg.add("Coverage"); using Coverage; Coveralls.submit(Coveralls.process_folder())'
-  - julia -e 'Pkg.add("Documenter")'
-  - julia -e 'cd(Pkg.dir("StatsBase")); include(joinpath("docs", "make.jl"))'
+  - julia -e 'Pkg.add("Documenter"); cd(Pkg.dir("StatsBase")); include(joinpath("docs", "make.jl"))'

docs/src/statmodels.md

@@ -14,6 +14,8 @@ coeftable
 confint
 deviance
 dof
+fit
+fit!
 loglikelihood
 nobs
 nulldeviance

src/counts.jl

@@ -10,7 +10,7 @@
 
 #### functions for counting a single list of integers (1D)
 """
-    addcounts!(r, x, levels, [wv])
+    addcounts!(r, x, levels::UnitRange{<:Int}, [wv::AbstractWeights])
 
 Add the number of occurrences in `x` of each value in `levels` to an existing
 array `r`. If a weighting vector `wv` is specified, the sum of weights is used
@@ -55,23 +55,26 @@ end
 
 
 """
-    counts(x, levels=span(x), [wv::AbstractWeights])
+    counts(x, [wv::AbstractWeights])
+    counts(x, levels::UnitRange{<:Integer}, [wv::AbstractWeights])
+    counts(x, k::Integer, [wv::AbstractWeights])
+
+Count the number of times each value in `x` occurs. If `levels` is provided, only values
+falling in that range will be considered (the others will be ignored without
+raising an error or a warning). If an integer `k` is provided, only values in the
+range `1:k` will be considered.
 
-Count the number of times that values in the range `levels` occur in
-`x`. The output is a vector of length `length(levels)`. If a weighting
-vector `wv` is specified, the sum of the weights is used rather than the
+If a weighting vector `wv` is specified, the sum of the weights is used rather than the
 raw counts.
+
+The output is a vector of length `length(levels)`.
 """
+function counts end
+
 counts(x::IntegerArray, levels::IntUnitRange) =
     addcounts!(zeros(Int, length(levels)), x, levels)
 counts(x::IntegerArray, levels::IntUnitRange, wv::AbstractWeights) =
     addcounts!(zeros(eltype(wv), length(levels)), x, levels, wv)
-
-"""
-    counts(x, k::Integer, [wv::AbstractWeights])
-
-Count the number of times integers in the range 1 to `k` occur in `x`.
-"""
 counts(x::IntegerArray, k::Integer) = counts(x, 1:k)
 counts(x::IntegerArray, k::Integer, wv::AbstractWeights) = counts(x, 1:k, wv)
 counts(x::IntegerArray) = counts(x, span(x))

src/misc.jl

@@ -140,7 +140,6 @@ julia> indicatormat([1 2 2], 2)
 2×3 Array{Bool,2}:
   true  false  false
  false   true   true
-
 ```
 """
 function indicatormat(x::IntegerArray, k::Integer; sparse::Bool=false)

src/sampling.jl

@@ -126,11 +126,12 @@ Pseudo-code:
 n = length(a)
 k = length(x)
 
-create an array of index inds = [1:n]
+# Create an array of the indices
+inds = collect(1:n)
 
 for i = 1:k
-    swap inds[i] with a random one in inds[i:n]
-    set x[i] = a[inds[i]]
+    # swap element `i` with another random element in inds[i:n]
+    # set element `i` in `x`
 end
 ```
 

src/statmodels.jl

@@ -86,7 +86,14 @@ Return the variance-covariance matrix for the coefficients of the model.
 """
 vcov(obj::StatisticalModel) = error("vcov is not defined for $(typeof(obj)).")
 
+"""
+Fit a statistical model.
+"""
 fit(obj::StatisticalModel, args...) = error("fit is not defined for $(typeof(obj)).")
+
+"""
+Fit a statistical model in-place.
+"""
 fit!(obj::StatisticalModel, args...) = error("fit! is not defined for $(typeof(obj)).")
 
 """

src/weights.jl

@@ -484,8 +484,8 @@ Compute the weighted median of `x`, using weights given by a weight vector `w`
 (of type `AbstractWeights`). The weight and data vectors must have the same length.
 
 The weighted median ``x_k`` is the element of `x` that satisfies
-``\sum_{x_i < x_k} w_i \le \frac{1}{2} \sum_{j} w_j`` and
-``\sum_{x_i > x_k} w_i \le \frac{1}{2} \sum_{j} w_j``.
+``\\sum_{x_i < x_k} w_i \\le \\frac{1}{2} \\sum_{j} w_j`` and
+``\\sum_{x_i > x_k} w_i \\le \\frac{1}{2} \\sum_{j} w_j``.
 
 If a weight has value zero, then its associated data point is ignored.
 If none of the weights are positive, an error is thrown.
@@ -563,9 +563,9 @@ values `p`, using weights given by a weight vector `w` (of type `AbstractWeights
 Weights must not be negative. The weights and data vectors must have the same length.
 
 The quantile for `p` is defined as follows. Denoting
-``S_k = (k-1)w_k + (n-1) \sum_{i<k}w_i``, define ``x_{k+1}`` the smallest element of `x`
+``S_k = (k-1)w_k + (n-1) \\sum_{i<k}w_i``, define ``x_{k+1}`` the smallest element of `x`
 such that ``S_{k+1}/S_{n}`` is strictly superior to `p`. The function returns
-``(1-\gamma) x_k + \gamma x_{k+1}`` with  ``\gamma = (pS_n- S_k)/(S_{k+1}-S_k)``.
+``(1-\\gamma) x_k + \\gamma x_{k+1}`` with  ``\\gamma = (pS_n- S_k)/(S_{k+1}-S_k)``.
 
 This corresponds to  R-7, Excel, SciPy-(1,1) and Maple-6 when `w` contains only ones
 (see [Wikipedia](https://en.wikipedia.org/wiki/Quantile)).