fixed documentation

R/lpcde/R/lpbwcde.R

@@ -1,5 +1,5 @@
 ##################################################################################################
-#' @title Data-driven Bandwidth Selection for Local Polynomial Conditional Density Estimators
+#' @title Data-driven bandwidth selection for local polynomial conditional density estimators
 #'
 #' @description  \code{\link{lpbwcde}} implements the bandwidth selection methods for local
 #'   polynomial based conditionaldensity (and derivatives) estimation proposed and studied
@@ -24,8 +24,8 @@
 #' \code{1} (default) for the density funtion, etc.
 #' @param nu Nonnegative integer, specifies the derivative with respect to \code{X} of the
 #' distribution function to be estimated.
-#' @param grid_spacing String. If equal to "quantile" will generate quantile-spaced grid evaluation points, otherwise will generate equally spaced points.
-#' @param ng int. number of grid points to be used in generating bandwidth estimates.
+#' @param grid_spacing String, If equal to "quantile" will generate quantile-spaced grid evaluation points, otherwise will generate equally spaced points.
+#' @param ng Int, number of grid points to be used in generating bandwidth estimates.
 #' @param kernel_type String, specifies the kernel function, should be one of
 #' \code{"triangular"}, \code{"uniform"} or \code{"epanechnikov"}.
 #' @param bw_type String, specifies the method for data-driven bandwidth selection. This option will be
@@ -49,7 +49,7 @@
 #'
 #' Xinwei Ma, University of California San Diego. \email{x1ma@ucsd.edu}.
 #'
-# @seealso Supported methods: \code{\link{coef.lpbwdensity}},
+#' @seealso Supported methods: \code{\link{coef.lpbwdensity}},
 # \code{\link{print.lpbwdensity}}, \code{\link{summary.lpbwdensity}}.
 #'
 #' @examples

R/lpcde/R/lpcde.R

@@ -2,7 +2,7 @@
 # This file contains code for generating conditional density estimate (External Functions)
 ############################################################################################
 
-#' @title Local Polynomial Conditional Density Estimation
+#' @title Local polynomial conditional density estimation
 #' @description \code{\link{lpcde}} implements the local polynomial regression based
 #' conditional density (and derivatives). The estimator proposed in
 #' \insertCite{bernoulli}{lpcde}.
@@ -85,7 +85,7 @@
 #'
 #' Xinwei Ma, University of California San Diego. \email{x1ma@ucsd.edu}.
 #'
-#'  Supported methods: \code{\link{coef.lpcde}}, \code{\link{confint.lpcde}},
+#' @seealso Supported methods: \code{\link{coef.lpcde}}, \code{\link{confint.lpcde}},
 #' \code{\link{plot.lpcde}}, \code{\link{print.lpcde}},
 #' \code{\link{summary.lpcde}}, \code{\link{vcov.lpcde}}
 #'

R/lpcde/R/lpcde_fns.R

@@ -2,26 +2,26 @@
 # This file contains code for generating point estimates (Internal Functions)
 #######################################################################################
 
-#' @title lpcde_fn: conditional density estimator.
+#' @title lpcde_fn: Conditional density estimator.
 #' @description Function for estimating the density function and its derivatives.
-#' @param y_data response variable dataset, vector.
-#' @param x_data covariate dataset, vector or matrix.
+#' @param y_data Response variable dataset, vector.
+#' @param x_data Covariate dataset, vector or matrix.
 #' @param y_grid Numeric vector, specifies the grid of evaluation points along y-direction.
 #' @param x Numeric vector or matrix, specifies the grid of evaluation points along x-direction.
-#' @param p polynomial order for y.
-#' @param q polynomial order for covariates.
+#' @param p Polynomial order for y.
+#' @param q Polynomial order for covariates.
 #' @param p_RBC Nonnegative integer, specifies the order of the local polynomial for \code{Y} used to
 #' construct bias-corrected point estimates. (Default is \code{p+1}.)
 #' @param q_RBC Nonnegative integer, specifies the order of the local polynomial for \code{X} used to
 #' construct bias-corrected point estimates. (Default is \code{q+1}.)
 #' @param bw Numeric, bandwidth vector.
-#' @param mu degree of derivative with respect to y.
-#' @param nu degree of derivative with respect to x.
-#' @param kernel_type kernel function choice.
+#' @param mu Degree of derivative with respect to y.
+#' @param nu Degree of derivative with respect to x.
+#' @param kernel_type Kernel function choice.
 #' @param rbc Boolean for whether to return RBC estimate and standard errors.
 # @param var_type String. type of variance estimator to implement.
 # choose from "ustat" and "asymp".
-#' @return conditional density estimate at all grid points.
+#' @return Conditional density estimate at all grid points.
 #' @keywords internal
 #' @importFrom Rdpack reprompt
 lpcde_fn = function(y_data, x_data, y_grid, x, p, q, p_RBC, q_RBC, bw, mu, nu,
@@ -114,19 +114,19 @@ lpcde_fn = function(y_data, x_data, y_grid, x, p, q, p_RBC, q_RBC, bw, mu, nu,
   return(est_result)
 }
 
-#' @title fhat: estimator
+#' @title Estimator construction
 #' @description Function for estimating the density function and its derivatives.
-#' @param y_data response variable dataset, vector.
-#' @param x_data covariate dataset, vector or matrix.
+#' @param y_data Response variable dataset, vector.
+#' @param x_data Covariate dataset, vector or matrix.
 #' @param y_grid Numeric vector, specifies the grid of evaluation points along y-direction.
 #' @param x Numeric vector or matrix, specifies the grid of evaluation points along x-direction.
-#' @param p polynomial order for y.
-#' @param q polynomial order for covariates.
+#' @param p Polynomial order for y.
+#' @param q Polynomial order for covariates.
 #' @param h Numeric, bandwidth vector.
-#' @param mu degree of derivative with respect to y.
-#' @param nu degree of derivative with respect to x.
-#' @param kernel_type kernel function choice.
-#' @return conditional density estimate at all grid points.
+#' @param mu Degree of derivative with respect to y.
+#' @param nu Degree of derivative with respect to x.
+#' @param kernel_type Kernel function choice.
+#' @return Conditional density estimate at all grid points.
 #' @keywords internal
 fhat = function(x_data, y_data, x, y_grid, p, q, mu, nu, h, kernel_type){
   # setting constants
@@ -314,19 +314,19 @@ fhat = function(x_data, y_data, x, y_grid, p, q, mu, nu, h, kernel_type){
 
 #' @title cov_hat: covariance estimator
 #' @description Function for estimating the variance-covariance matrix.
-#' @param y_data response variable dataset, vector.
-#' @param x_data covariate dataset, vector or matrix.
+#' @param y_data Response variable dataset, vector.
+#' @param x_data Covariate dataset, vector or matrix.
 #' @param y_grid Numeric vector, specifies the grid of evaluation points along
 #' y-direction.
 #' @param x Numeric vector or matrix, specifies the grid of evaluation points
 #' along x-direction.
-#' @param p polynomial order for y.
-#' @param q polynomial order for covariates.
+#' @param p Polynomial order for y.
+#' @param q Polynomial order for covariates.
 #' @param h Numeric, bandwidth vector.
-#' @param mu degree of derivative with respect to y.
-#' @param nu degree of derivative with respect to x.
-#' @param kernel_type kernel function choice.
-#' @return covariance matrix for all the grid points
+#' @param mu Degree of derivative with respect to y.
+#' @param nu Degree of derivative with respect to x.
+#' @param kernel_type Kernel function choice.
+#' @return Covariance matrix for all the grid points
 #' @keywords internal
 cov_hat = function(x_data, y_data, x, y_grid, p, q, mu, nu, h, kernel_type){
   # setting constants
@@ -618,13 +618,13 @@ cov_hat = function(x_data, y_data, x, y_grid, p, q, mu, nu, h, kernel_type){
 # Supplemental Functions
 #######################################################################################
 
-#' @title bx = constants for each data and evaluation point pair
+#' @title bx
 #' @description Function for estimating the constants in the estimation formula
-#' @param datavec dataset, vector.
-#' @param s_mat s_hat matrix.
-#' @param q polynomial order.
-#' @param kernel_type kernel function choice.
-#' @return vector of products for each data point.
+#' @param datavec Dataset, vector.
+#' @param s_mat S_hat matrix.
+#' @param q Polynomial order.
+#' @param kernel_type Kernel function choice.
+#' @return Vector of products for each data point.
 #' @keywords internal
 b_x = function(datavec, s_mat, e_vec, q, kernel_type){
   eff_n = length(datavec[,1])

R/lpcde/R/lpcde_methods.R

@@ -273,7 +273,7 @@ coef.lpcde = function(object, ...) {
 #' Vcov Method for Local Polynomial Density Conditional Estimation and Inference
 #'
 #' @title Variance-Covariance
-#' @description The vcov method for local polynomial conditionaldensity objects.
+#' @description The vcov method for local polynomial conditional density objects.
 #'
 #' @param object Class "lpdensity" object, obtained by calling \code{\link{lpcde}}.
 #' @param ... Additional options.
@@ -341,7 +341,7 @@ vcov.lpcde = function(object, ...) {
 #' @return
 #' \item{Estimate}{A matrix containing grid points, estimates and confidence interval end points using p- and q-th order local polynomials
 #' as well as bias-corrected estimates and corresponding confidence intervals.}
-#' \item{crit_val}{the critical value used in computing the confidence interval end points.}
+#' \item{crit_val}{The critical value used in computing the confidence interval end points.}
 #'
 #' @author
 #' Matias D. Cattaneo, Princeton University. \email{cattaneo@princeton.edu}.