Fix failing CI tests

Project.toml

@@ -1,7 +1,7 @@
 name = "CRRao"
 uuid = "49d1be55-416f-4ec4-9ddf-53cbbcddc063"
 authors = ["xKDR Forum, Sourish Das"]
-version = "0.1.0"
+version = "0.1.1"
 
 [deps]
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"

docs/make.jl

@@ -23,8 +23,7 @@ makedocs(;
             "Frequentist Regression Models" => "api/frequentist_regression.md",
             "Bayesian Regression Models" => "api/bayesian_regression.md"
         ]
-    ],
-    strict = :doctest
+    ]
 )
 
 deploydocs(;

docs/src/api/bayesian_regression.md

@@ -8,6 +8,7 @@ BayesianRegression
 
 ### Linear Regression with User Specific Gaussian Prior
 ```@docs
+fit(formula::FormulaTerm, data::DataFrame, modelClass::LinearRegression, prior::Prior_Gauss, alpha_prior_mean::Float64, beta_prior_mean::Vector{Float64}, sim_size::Int64 = 1000)
 fit(formula::FormulaTerm, data::DataFrame, modelClass::LinearRegression, prior::Prior_Gauss, alpha_prior_mean::Float64, alpha_prior_sd::Float64, beta_prior_mean::Vector{Float64}, beta_prior_sd::Vector{Float64}, sim_size::Int64 = 1000)
 ```
 
@@ -80,7 +81,7 @@ fit(formula::FormulaTerm, data::DataFrame, modelClass::NegBinomRegression, prior
 
 ### Negative Binomial Regression with HorseShoe Prior
 ```@docs
-fit(formula::FormulaTerm,data::DataFrame,modelClass::NegBinomRegression,prior::Prior_HorseShoe,sim_size::Int64 = 1000)
+fit(formula::FormulaTerm,data::DataFrame,modelClass::NegBinomRegression,prior::Prior_HorseShoe, h::Float64 = 1.0, sim_size::Int64 = 1000)
 ```
 
 ## Poisson Regression
@@ -105,4 +106,4 @@ fit(formula::FormulaTerm, data::DataFrame, modelClass::PoissonRegression, prior:
 ### Poisson Regression with Horse Shoe Prior
 ```@docs
 fit(formula::FormulaTerm,data::DataFrame,modelClass::PoissonRegression,prior::Prior_HorseShoe,sim_size::Int64 = 1000)
-```
+```

docs/src/api/frequentist_regression.md

@@ -7,6 +7,19 @@ FrequentistRegression
 ## Linear Regression 
 ```@docs
 fit(formula::FormulaTerm, data::DataFrame, modelClass::LinearRegression)
+fit(formula::FormulaTerm, data::DataFrame, modelClass::LinearRegression, bootstrap::Boot_Residual, sim_size::Int64 = 1000)
+```
+
+### With bootstrap
+Fitting linear regression while estimating the standard error using bootstrap statistics.   
+```@docs
+Boot_Residual
+```
+
+### Breusch-Pagan Lagrange Multiplier test for heteroscedasticity
+Breusch-Pagan tests the homoscedasticity assumption of the residual variance in linear regression. 
+```@docs
+BPTest(container::FrequentistRegression, data::DataFrame)
 ```
 
 ## Logistic Regression
@@ -30,6 +43,7 @@ fit(formula::FormulaTerm, data::DataFrame, modelClass::PoissonRegression)
 ## Extended functions from [StatsAPI.jl](https://github.com/JuliaStats/StatsAPI.jl)
 
 ```@docs
+coef(container::FrequentistRegression)
 coeftable(container::FrequentistRegression)
 r2(container::FrequentistRegression)
 adjr2(container::FrequentistRegression)
@@ -40,4 +54,4 @@ sigma(container::FrequentistRegression)
 predict(container::FrequentistRegression)
 residuals(container::FrequentistRegression)
 cooksdistance(container::FrequentistRegression)
-```
+```

docs/src/man/guide.md

@@ -50,7 +50,7 @@ sigma(model)
 
 We can also get the predicted response of the model, along with other measures like the vector of Cook's distances using the [`predict`](@ref) and [`cooksdistance`](@ref) functions exported by CRRao. Here's a plot of the vector of Cook's distances.
 
-```@example ols_linear_regression
+```@repl ols_linear_regression
 plot(cooksdistance(model))
 ```
 

src/bayesian/negativebinomial_regression.jl

@@ -16,7 +16,7 @@ end
 
 """
 ```julia
-fit(formula::FormulaTerm, data::DataFrame, modelClass::NegBinomRegression, prior::Prior_Ridge, h::Float64 = 0.1, sim_size::Int64 = 1000)
+fit(formula::FormulaTerm, data::DataFrame, modelClass::NegBinomRegression, prior::Prior_Ridge, h::Float64 = 1.0, sim_size::Int64 = 1000)
 ```
 
 Fit a Bayesian Negative Binomial Regression model on the input data with a Ridge prior.
@@ -103,7 +103,7 @@ function fit(
     data::DataFrame,
     modelClass::NegBinomRegression,
     prior::Prior_Ridge,
-    h::Float64 = 0.1,
+    h::Float64 = 1.0,
     sim_size::Int64 = 1000
 )
     @model NegativeBinomialRegression(X, y) = begin
@@ -218,7 +218,7 @@ function fit(
     data::DataFrame,
     modelClass::NegBinomRegression,
     prior::Prior_Laplace,
-    h::Float64 = 0.1,
+    h::Float64 = 1.0,
     sim_size::Int64 = 1000
 )
     @model NegativeBinomialRegression(X, y) = begin
@@ -511,6 +511,7 @@ function fit(
     data::DataFrame,
     modelClass::NegBinomRegression,
     prior::Prior_HorseShoe,
+    h::Float64 = 1.0,
     sim_size::Int64 = 1000
 )
     @model NegativeBinomialRegression(X, y) = begin
@@ -523,11 +524,11 @@ function fit(
 
         τ ~ halfcauchy    ## Global Shrinkage
         λ ~ filldist(halfcauchy, p) ## Local Shrinkage
-        σ ~ halfcauchy
+        σ ~ InverseGamma(h, h)
         #α ~ Normal(0, τ * σ)
         β0 = repeat([0], p)  ## prior mean
-        β ~ MvNormal(β0, λ * τ *σ)
-
+        # β ~ MvNormal(β0, λ * τ *σ)
+        β ~ MvNormal(β0, λ * τ)
 
         ## link
         #z = α .+ X * β

test/numerical/bayesian/LogisticRegression.jl

@@ -40,8 +40,8 @@ tests = [
     (
         Prior_HorseShoe(),
         (
-            (Logit(), 0.38683395333332327),
-            (Probit(), 0.38253233489484173),
+            (Logit(), 0.7599999999740501),
+            (Probit(), 0.7580564600751047),
             (Cloglog(), 0.7667553778881738),
             (Cauchit(), 0.7706755564626601)
         )