diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml
index 55f889c..e17bc06 100644
--- a/.github/workflows/CI.yml
+++ b/.github/workflows/CI.yml
@@ -49,3 +49,6 @@ jobs:
       - uses: julia-actions/julia-runtest@v1
       - uses: julia-actions/julia-processcoverage@v1
       - uses: codecov/codecov-action@v3
+        env:
+            GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # If authenticating with GitHub Actions token
+            DOCUMENTER_KEY: ${{ secrets.CODECOV_TOKEN }} # If authenticating with SSH deploy key
diff --git a/src/fit.jl b/src/fit.jl
index 088999e..eb1d4a4 100644
--- a/src/fit.jl
+++ b/src/fit.jl
@@ -24,6 +24,26 @@ function init(config::EvoLinearRegressor;
 
 end
 
+
+"""
+    fit(config::EvoLinearRegressor;
+        x, y, w=nothing,
+        x_eval=nothing, y_eval=nothing, w_eval=nothing,
+        metric=:mse,
+        print_every_n=1,
+        tol=1e-5)
+
+`Provided a `config`, EvoLinear.fit` takes `x` and `y` as features and target inputs, plus optionally `w` as weights and train a Linear boosted model.
+
+# Arguments
+- `config::EvoLinearRegressor`: 
+
+# Keyword arguments
+- `x::AbstractMatrix`: Features matrix. Dimensions are `[nobs, num_features]`.
+- `y::AbstractVector`: Vector of observed targets.
+- `w=nothing`: Vector of weights. Can be be either a `Vector` or `nothing`. If `nothing`, assumes a vector of 1s. 
+- `metric=:mse`: Evaluation metric to be tracked through each iteration.
+"""
 function fit(config::EvoLinearRegressor;
     x, y, w=nothing,
     x_eval=nothing, y_eval=nothing, w_eval=nothing,
diff --git a/src/predict.jl b/src/predict.jl
index 0528fc5..15caceb 100644
--- a/src/predict.jl
+++ b/src/predict.jl
@@ -1,7 +1,34 @@
-function predict_linear(m, x)
+"""
+    predict_linear(m, x)
+
+Returns the predictions on the linear basis from model `m` using the features matrix `x`.
+
+# Arguments
+
+- `m::EvoLinearModel`
+- `x`
+"""
+function predict_linear(m::EvoLinearModel, x)
     p = x * m.coef .+ m.bias
     return p
 end
+
+"""
+    predict_proj(m, x)
+
+Returns the predictions on the projected basis from model `m` using the features matrix `x`.
+
+- `MSE`: pred_proj = pred_linear
+- `Logistic`: pred_proj = sigmoid(pred_linear)
+- `Poisson`: pred_proj = exp(pred_linear)
+- `Gamma`: pred_proj = exp(pred_linear)
+- `Tweedie`: pred_proj = exp(pred_linear)
+
+# Arguments
+
+- `m::EvoLinearModel`
+- `x`
+"""
 function predict_proj(m::EvoLinearModel{MSE}, x)
     p = predict_linear(m, x)
     return p