Improve prior names

docs/src/api/bayesian_regression.md

@@ -8,101 +8,101 @@ BayesianRegression
 
 ### Linear Regression with User Specific Gaussian Prior
 ```@docs
-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)
+fit(formula::FormulaTerm, data::DataFrame, modelClass::LinearRegression, prior::Gauss, alpha_prior_mean::Float64, alpha_prior_sd::Float64, beta_prior_mean::Vector{Float64}, beta_prior_sd::Vector{Float64}, sim_size::Int64 = 1000)
 ```
 
 ### Linear Regression with Ridge Prior
 ```@docs
-fit(formula::FormulaTerm, data::DataFrame, modelClass::LinearRegression, prior::Prior_Ridge, h::Float64 = 0.01, sim_size::Int64 = 1000)
+fit(formula::FormulaTerm, data::DataFrame, modelClass::LinearRegression, prior::Ridge, h::Float64 = 0.01, sim_size::Int64 = 1000)
 ```
 
 ### Linear Regression with Laplace Prior
 ```@docs
-fit(formula::FormulaTerm, data::DataFrame, modelClass::LinearRegression, prior::Prior_Laplace, h::Float64 = 0.01, sim_size::Int64 = 1000)
+fit(formula::FormulaTerm, data::DataFrame, modelClass::LinearRegression, prior::Laplace, h::Float64 = 0.01, sim_size::Int64 = 1000)
 ```
 ### Linear Regression with Cauchy Prior
 ```@docs
-fit(formula::FormulaTerm, data::DataFrame, modelClass::LinearRegression, prior::Prior_Cauchy, sim_size::Int64 = 1000)
+fit(formula::FormulaTerm, data::DataFrame, modelClass::LinearRegression, prior::Cauchy, sim_size::Int64 = 1000)
 ```
 ### Linear Regression with T-distributed Prior
 ```@docs
-fit(formula::FormulaTerm, data::DataFrame, modelClass::LinearRegression, prior::Prior_TDist, h::Float64 = 2.0, sim_size::Int64 = 1000)
+fit(formula::FormulaTerm, data::DataFrame, modelClass::LinearRegression, prior::TDist, h::Float64 = 2.0, sim_size::Int64 = 1000)
 ```
 ### Linear Regression with Horse Shoe Prior
 ```@docs
-fit(formula::FormulaTerm,data::DataFrame,modelClass::LinearRegression,prior::Prior_HorseShoe,sim_size::Int64 = 1000)
+fit(formula::FormulaTerm,data::DataFrame,modelClass::LinearRegression,prior::HorseShoe,sim_size::Int64 = 1000)
 ```
 
 ## Logistic Regression
 
 ### Logistic Regression with Ridge Prior
 ```@docs
-fit(formula::FormulaTerm, data::DataFrame, modelClass::LogisticRegression, Link::CRRaoLink, prior::Prior_Ridge, h::Float64 = 0.1, level::Float64 = 0.95, sim_size::Int64 = 1000)
+fit(formula::FormulaTerm, data::DataFrame, modelClass::LogisticRegression, Link::CRRaoLink, prior::Ridge, h::Float64 = 0.1, level::Float64 = 0.95, sim_size::Int64 = 1000)
 ```
 ### Logistic Regression with Laplace Prior
 ```@docs
-fit(formula::FormulaTerm, data::DataFrame, modelClass::LogisticRegression, Link::CRRaoLink, prior::Prior_Laplace, h::Float64 = 0.1, level::Float64 = 0.95, sim_size::Int64 = 1000)
+fit(formula::FormulaTerm, data::DataFrame, modelClass::LogisticRegression, Link::CRRaoLink, prior::Laplace, h::Float64 = 0.1, level::Float64 = 0.95, sim_size::Int64 = 1000)
 ```
 ### Logistic Regression with Cauchy Prior
 ```@docs
-fit(formula::FormulaTerm, data::DataFrame, modelClass::LogisticRegression, Link::CRRaoLink, prior::Prior_Cauchy, h::Float64 = 0.1, level::Float64 = 0.95, sim_size::Int64 = 1000)
+fit(formula::FormulaTerm, data::DataFrame, modelClass::LogisticRegression, Link::CRRaoLink, prior::Cauchy, h::Float64 = 0.1, level::Float64 = 0.95, sim_size::Int64 = 1000)
 ```
 ### Logistic Regression with T-Distributed Prior
 ```@docs
-fit(formula::FormulaTerm, data::DataFrame, modelClass::LogisticRegression, Link::CRRaoLink, prior::Prior_TDist, h::Float64 = 1.0, level::Float64 = 0.95, sim_size::Int64 = 1000)
+fit(formula::FormulaTerm, data::DataFrame, modelClass::LogisticRegression, Link::CRRaoLink, prior::TDist, h::Float64 = 1.0, level::Float64 = 0.95, sim_size::Int64 = 1000)
 ```
 
 ### Logistic Regression with Horse Shoe Prior
 ```@docs
-fit(formula::FormulaTerm,data::DataFrame,modelClass::LogisticRegression,Link::CRRaoLink,prior::Prior_HorseShoe,level::Float64 = 0.95,sim_size::Int64 = 1000)
+fit(formula::FormulaTerm,data::DataFrame,modelClass::LogisticRegression,Link::CRRaoLink,prior::HorseShoe,level::Float64 = 0.95,sim_size::Int64 = 1000)
 ```
 
 ## Negative Binomial Regression
 
 ### Negative Binomial Regression with Ridge Prior
 ```@docs
-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::Ridge, h::Float64 = 0.1, sim_size::Int64 = 1000)
 ```
 
 ### Negative Binomial Regression with Laplace Prior
 ```@docs
-fit(formula::FormulaTerm, data::DataFrame, modelClass::NegBinomRegression, prior::Prior_Laplace, h::Float64 = 0.01, sim_size::Int64 = 1000)
+fit(formula::FormulaTerm, data::DataFrame, modelClass::NegBinomRegression, prior::Laplace, h::Float64 = 0.01, sim_size::Int64 = 1000)
 ```
 ### Negative Binomial Regression with Cauchy Prior
 ```@docs
-fit(formula::FormulaTerm, data::DataFrame, modelClass::NegBinomRegression, prior::Prior_Cauchy, h::Float64 = 1.0, sim_size::Int64 = 1000)
+fit(formula::FormulaTerm, data::DataFrame, modelClass::NegBinomRegression, prior::Cauchy, h::Float64 = 1.0, sim_size::Int64 = 1000)
 ```
 
 ### Negative Binomial Regression with T-Distributed Prior
 ```@docs
-fit(formula::FormulaTerm, data::DataFrame, modelClass::NegBinomRegression, prior::Prior_TDist, h::Float64 = 1.0, sim_size::Int64 = 1000)
+fit(formula::FormulaTerm, data::DataFrame, modelClass::NegBinomRegression, prior::TDist, h::Float64 = 1.0, sim_size::Int64 = 1000)
 ```
 
 ### 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::HorseShoe,sim_size::Int64 = 1000)
 ```
 
 ## Poisson Regression
 
 ### Poisson Regression with Ridge Prior
 ```@docs
-fit(formula::FormulaTerm, data::DataFrame, modelClass::PoissonRegression, prior::Prior_Ridge, h::Float64 = 0.1, sim_size::Int64 = 1000)
+fit(formula::FormulaTerm, data::DataFrame, modelClass::PoissonRegression, prior::Ridge, h::Float64 = 0.1, sim_size::Int64 = 1000)
 ```
 ### Poisson Regression with Laplace Prior
 ```@docs
-fit(formula::FormulaTerm, data::DataFrame, modelClass::PoissonRegression, prior::Prior_Laplace, h::Float64 = 0.1, sim_size::Int64 = 1000)
+fit(formula::FormulaTerm, data::DataFrame, modelClass::PoissonRegression, prior::Laplace, h::Float64 = 0.1, sim_size::Int64 = 1000)
 ```
 ### Poisson Regression with Cauchy Prior
 ```@docs
-fit(formula::FormulaTerm, data::DataFrame, modelClass::PoissonRegression, prior::Prior_Cauchy, h::Float64 = 1.0, sim_size::Int64 = 1000)
+fit(formula::FormulaTerm, data::DataFrame, modelClass::PoissonRegression, prior::Cauchy, h::Float64 = 1.0, sim_size::Int64 = 1000)
 ```
 ### Poisson Regression with T-Distributed Prior
 ```@docs
-fit(formula::FormulaTerm, data::DataFrame, modelClass::PoissonRegression, prior::Prior_TDist, h::Float64 = 2.0, sim_size::Int64 = 1000)
+fit(formula::FormulaTerm, data::DataFrame, modelClass::PoissonRegression, prior::TDist, h::Float64 = 2.0, sim_size::Int64 = 1000)
 ```
 
 ### Poisson Regression with Horse Shoe Prior
 ```@docs
-fit(formula::FormulaTerm,data::DataFrame,modelClass::PoissonRegression,prior::Prior_HorseShoe,sim_size::Int64 = 1000)
+fit(formula::FormulaTerm,data::DataFrame,modelClass::PoissonRegression,prior::HorseShoe,sim_size::Int64 = 1000)
 ```

docs/src/api/interface.md

@@ -30,12 +30,12 @@ It should be noted that not all model classes support every type of signature. T
     - [`Cauchit`](@ref)
 
 5. CRRao also supports Bayesian models, and the priors to be can be specified while calling `fit`. Currently CRRao supports six different kinds of priors, and the type of the `prior` parameter must be one of the following.
-    - [`Prior_Gauss`](@ref)
-    - [`Prior_Ridge`](@ref)
-    - [`Prior_Laplace`](@ref)
-    - [`Prior_Cauchy`](@ref)
-    - [`Prior_TDist`](@ref)
-    - [`Prior_HorseShoe`](@ref)
+    - [`Gauss`](@ref)
+    - [`Ridge`](@ref)
+    - [`Laplace`](@ref)
+    - [`Cauchy`](@ref)
+    - [`TDist`](@ref)
+    - [`HorseShoe`](@ref)
 
 
 ## Model Classes and Data Models
@@ -59,12 +59,12 @@ Cauchit
 ## Prior Distributions
 
 ```@docs
-Prior_Gauss
-Prior_Ridge
-Prior_Laplace
-Prior_Cauchy
-Prior_TDist
-Prior_HorseShoe
+Gauss
+Ridge
+Laplace
+Cauchy
+TDist
+HorseShoe
 ```
 
 ## Setting Random Number Generators

docs/src/man/guide.md

@@ -58,7 +58,7 @@ To understand more about these functions and in general how frequentist models w
 
 ## Tutorial: Bayesian Logistic Regression
 
-Next, let's see an example of doing bayesian statistical inference with CRRao. In this example, we will perform bayesian logistic regression on the `turnout` dataset from R's [Zelig](https://zeligproject.org/). Further, we will use the [`Logit`](@ref) link function with a Ridge prior ([`Prior_Ridge`](@ref)).
+Next, let's see an example of doing bayesian statistical inference with CRRao. In this example, we will perform bayesian logistic regression on the `turnout` dataset from R's [Zelig](https://zeligproject.org/). Further, we will use the [`Logit`](@ref) link function with a Ridge prior ([`Ridge`](@ref)).
 
 With this example, we'll also showcase how to use random number generators to get reproducible results. For this, we will use the [StableRNGs](https://github.com/JuliaRandom/StableRNGs.jl) package (although any random number generator can be used). So, first we import the required modules.
 
@@ -90,5 +90,5 @@ turnout = dataset("Zelig", "turnout")
 And finally, we do the inference using our proposed model.
 
 ```@repl bayesian_logistic_regression
-model = fit(@formula(Vote ~ Age + Race + Income + Educate), turnout, LogisticRegression(), Logit(), Prior_Ridge())
+model = fit(@formula(Vote ~ Age + Race + Income + Educate), turnout, LogisticRegression(), Logit(), Ridge())
 ```

src/CRRao.jl

@@ -102,9 +102,11 @@ Type representing Residual Bootstrap.
 """
 struct Boot_Residual end
 
+abstract type Prior end
+
 """
 ```julia
-Prior_Gauss
+Gauss
 ```
 Type representing the Gaussian Prior. Users have specific 
 prior mean and standard deviation, for ``\\alpha`` and ``\\beta`` 
@@ -133,11 +135,11 @@ y_i \\sim N(\\mu_i,\\sigma),
 + ``\\mathbf{E}(y_i)=g(\\mu_i)``, and 
 + ``Var(y_i)=\\sigma^2``.
 """
-struct Prior_Gauss end
+struct Gauss <: Prior end
 
 """
 ```julia
-Prior_Ridge
+Ridge
 ```
 Type representing the Ridge Prior.
 
@@ -167,11 +169,11 @@ y_i \\sim D(\\mu_i,\\sigma),
 + ``\\mathbf{E}(y_i)=g(\\mu_i)``, and 
 + ``Var(y_i)=\\sigma^2``.
 """
-struct Prior_Ridge end
+struct Ridge <: Prior end
 
 """
 ```julia
-Prior_Laplace
+Laplace
 ```
 Type representing the Laplace Prior.
 
@@ -200,11 +202,11 @@ y_i \\sim D(\\mu_i,\\sigma),
 + ``\\mathbf{E}(y_i)=g(\\mu_i)``, and 
 + ``Var(y_i)=\\sigma^2``.
 """
-struct Prior_Laplace end
+struct Laplace <: Prior end
 
 """
 ```julia
-Prior_Cauchy
+Cauchy
 ```
 Type representing the Cauchy Prior.
 
@@ -231,11 +233,11 @@ y_i \\sim D(\\mu_i,\\sigma),
 + ``\\mathbf{E}(y_i)=g(\\mu_i)``, and 
 + ``Var(y_i)=\\sigma^2``.
 """
-struct Prior_Cauchy end
+struct Cauchy <: Prior end
 
 """
 ```julia
-Prior_TDist
+TDist
 ```
 Type representing the T-Distributed Prior.
 
@@ -266,12 +268,12 @@ y_i \\sim D(\\mu_i,\\sigma),
 + ``Var(y_i)=\\sigma^2``. 
 + The ``t(v)`` is ``t`` distribution with ``v`` degrees of freedom.
 """
-struct Prior_TDist end
+struct TDist <: Prior end
 
 
 """
 ```julia
-Prior_HorseShoe
+HorseShoe
 ```
 Type representing the HorseShoe Prior.
 
@@ -305,7 +307,7 @@ y_i \\sim D(\\mu_i,\\sigma), i=1,2,\\cdots,n
 + ``Var(y_i)=\\sigma^2``, and 
 + ``\\beta``=(``\\beta_1,\\beta_2,\\cdots,\\beta_p``)
 """
-struct Prior_HorseShoe end
+struct HorseShoe <: Prior end
 
 """
 ```julia
@@ -393,7 +395,7 @@ end
 Cauchit() = Cauchit(Cauchit_Link)
 
 export LinearRegression, LogisticRegression, PoissonRegression, NegBinomRegression, Boot_Residual
-export Prior_Ridge, Prior_Laplace, Prior_Cauchy, Prior_TDist, Prior_HorseShoe, Prior_Gauss
+export Ridge, Laplace, Cauchy, TDist, HorseShoe, Gauss
 export CRRaoLink, Logit, Probit, Cloglog, Cauchit, fit
 export coef, coeftable, r2, adjr2, loglikelihood, aic, bic, sigma, predict, residuals, cooksdistance, BPTest, pvalue
 export FrequentistRegression, BayesianRegression