Deprecate AIC, AICc, BIC, R² and R2 in favor of lower-case names (Jul…

…iaLang#180)

src/StatsBase.jl

@@ -150,11 +150,11 @@ module StatsBase
     StatisticalModel,
     RegressionModel,
 
-    adjR2,
-    adjR²,
-    AIC,
-    AICc,
-    BIC,
+    adjr2,
+    adjr²,
+    aic,
+    aicc,
+    bic,
     coef,
     coeftable,
     confint,
@@ -173,8 +173,8 @@ module StatsBase
     predict,
     predict!,
     residuals,
-    R2,
-    R²,
+    r2,
+    r²,
     model_response
 
     # source files

src/deprecates.jl

@@ -12,3 +12,13 @@ import Base.varm, Base.stdm
 @deprecate _moment4(v::RealArray, m::Real, wv::WeightVec) _moment4(v, wv, m)
 @deprecate _momentk(v::RealArray, k::Int, m::Real, wv::WeightVec) _momentk(v, k, wv, m)
 @deprecate moment(v::RealArray, k::Int, m::Real, wv::WeightVec) moment(v, k, wv, m)
+
+@deprecate AIC(obj::StatisticalModel) aic(obj)
+@deprecate AICc(obj::StatisticalModel) aicc(obj)
+@deprecate BIC(obj::StatisticalModel) bic(obj)
+
+@deprecate R2(obj::StatisticalModel, variant::Symbol) r2(obj, variant)
+@deprecate R²(obj::StatisticalModel, variant::Symbol) r²(obj, variant)
+@deprecate adjR2(obj::StatisticalModel, variant::Symbol) adjr2(obj, variant)
+@deprecate adjR²(obj::StatisticalModel, variant::Symbol) adjr²(obj, variant)
+

src/statmodels.jl

@@ -47,39 +47,39 @@ fit(obj::StatisticalModel, data...) = error("fit is not defined for $(typeof(obj
 fit!(obj::StatisticalModel, data...) = error("fit! is not defined for $(typeof(obj)).")
 
 """
-    AIC(obj::StatisticalModel)
+    aic(obj::StatisticalModel)
 
 Akaike's Information Criterion, defined as `-2 log L + 2k`, with `L` the likelihood
 of the model, and `k` its number of consumed degrees of freedom (as returned by `df`).
 """
-AIC(obj::StatisticalModel) = -2loglikelihood(obj) + 2df(obj)
+aic(obj::StatisticalModel) = -2loglikelihood(obj) + 2df(obj)
 
 """
-    AICc(obj::StatisticalModel)
+    aicc(obj::StatisticalModel)
 
 Corrected Akaike's Information Criterion for small sample sizes (Hurvich and Tsai 1989),
 defined as `-2 log L + 2k + 2k(k-1)/(n-k-1)`, with `L` the likelihood of the model,
 `k` its number of consumed degrees of freedom (as returned by `df`), and `n` the number
 of observations (as returned by `nobs`).
 """
-function AICc(obj::StatisticalModel)
+function aicc(obj::StatisticalModel)
     k = df(obj)
     n = nobs(obj)
     -2loglikelihood(obj) + 2k + 2k*(k+1)/(n-k-1)
 end
 
 """
-    BIC(obj::StatisticalModel)
+    bic(obj::StatisticalModel)
 
-Akaike's Information Criterion, defined as `-2 log L + k log n`, with `L`
+Bayesian Information Criterion, defined as `-2 log L + k log n`, with `L`
 the likelihood of the model,  `k` its number of consumed degrees of freedom
 (as returned by `df`), and `n` the number of observations (as returned by `nobs`).
 """
-BIC(obj::StatisticalModel) = -2loglikelihood(obj) + df(obj)*log(nobs(obj))
+bic(obj::StatisticalModel) = -2loglikelihood(obj) + df(obj)*log(nobs(obj))
 
 """
-    R2(obj::StatisticalModel, variant::Symbol)
-    R²(obj::StatisticalModel, variant::Symbol)
+    r2(obj::StatisticalModel, variant::Symbol)
+    r²(obj::StatisticalModel, variant::Symbol)
 
 Coefficient of determination (R-squared).
 
@@ -97,7 +97,7 @@ In the above formulas, ``L`` is the likelihood of the model, ``L0`` that of the
 (the model including only the intercept). These two quantities are taken to be minus half
 `deviance` of the corresponding models.
 """
-function R2(obj::StatisticalModel, variant::Symbol)
+function r2(obj::StatisticalModel, variant::Symbol)
     ll = -deviance(obj)/2
     ll0 = -nulldeviance(obj)/2
 
@@ -112,11 +112,11 @@ function R2(obj::StatisticalModel, variant::Symbol)
     end
 end
 
-const R² = R2
+const r² = r2
 
 """
-    adjR2(obj::StatisticalModel, variant::Symbol)
-    adjR²(obj::StatisticalModel, variant::Symbol)
+    adjr2(obj::StatisticalModel, variant::Symbol)
+    adjr²(obj::StatisticalModel, variant::Symbol)
 
 Adjusted coefficient of determination (adjusted R-squared).
 
@@ -131,7 +131,7 @@ In this formula, `L` is the likelihood of the model, `L0` that of the null model
 `deviance` of the corresponding models. `k` is the number of consumed degrees of freedom
 of the model (as returned by `df`).
 """
-function adjR2(obj::StatisticalModel, variant::Symbol)
+function adjr2(obj::StatisticalModel, variant::Symbol)
     ll = -deviance(obj)/2
     ll0 = -nulldeviance(obj)/2
     k = df(obj)
@@ -143,7 +143,7 @@ function adjR2(obj::StatisticalModel, variant::Symbol)
     end
 end
 
-const adjR² = adjR2
+const adjr² = adjr2
 
 abstract RegressionModel <: StatisticalModel