Update documentation

DESCRIPTION

@@ -34,7 +34,8 @@ Imports:
     progressr (>= 0.6.0),
     plyr (>= 1.8.6),
     furrr (>= 0.2.0),
-    Matrix (>= 1.2.18)
+    Matrix (>= 1.2.18),
+    methods (>= 3.6.1)
 Suggests:
     knitr (>= 1.28),
     rmarkdown (>= 2.0),
@@ -44,6 +45,7 @@ Suggests:
     Rcplex (>= 0.3.3),
     limSolve (>= 1.5.6),
     lpSolveAPI (>= 5.5.2.0.17.4),
-    spelling (>= 2.1)
+    spelling (>= 2.1),
+    kableExtra (>= 1.2.1)
 VignetteBuilder: knitr
 Language: en-US

NAMESPACE

@@ -11,7 +11,6 @@ S3method(print,lpmodel)
 S3method(print,lpmodel.natural)
 S3method(print,mincriterion)
 S3method(print,subsample)
-S3method(sigma,summation)
 S3method(summary,bisection.print)
 S3method(summary,chorussell)
 S3method(summary,dkqs)
@@ -124,6 +123,7 @@ export(post.bs)
 export(pval)
 export(quan.stat)
 export(rcplex.optim)
+export(sigma.summation)
 export(smatrixconvert)
 export(standard.form)
 export(standard.lpmodel)
@@ -136,7 +136,6 @@ export(tau.constraints)
 import(boot)
 import(expm)
 import(furrr)
-import(lpSolveAPI)
 import(plyr)
 import(progressr)
 import(scales)
@@ -145,4 +144,7 @@ importFrom(Matrix,norm)
 importFrom(Matrix,t)
 importFrom(Matrix,which)
 importFrom(expm,sqrtm)
+importFrom(methods,as)
+importFrom(methods,is)
 importFrom(pracma,ceil)
+importFrom(utils,tail)

R/checks.R

@@ -540,7 +540,7 @@ check.solver <- function(x, name.var, norm = 2 , qc = FALSE) {
       } else if (requireNamespace("cplexAPI", quietly = TRUE) == TRUE) {
         solver <- cplexapi.optim
         x <- "cplexAPI"
-      } else if (requireNamespace("lpsolveAPI", quietly = TRUE) == TRUE) {
+      } else if (requireNamespace("lpSolveAPI", quietly = TRUE) == TRUE) {
         solver <- lpsolveapi.optim
         x <- "lpSolveAPI"
       }
@@ -944,6 +944,8 @@ check.lpobjects <- function(data, mat, mat.name, mat.cat, R) {
 
 #' Check function: matrix
 #'
+#' @importFrom methods is
+#'
 #' @description This function checks if the matrix objects in the
 #'    \code{lpmodel} object are in the correct format.
 #'
@@ -978,7 +980,7 @@ check.matrix <- function(mat, mat.name, mat.cat, inside.list) {
     return(list(mat.update = mat.update,
                 err.ind = 0,
                 dim = dim(mat.update)))
-  } else if (isTRUE(is(mat, "sparseMatrix"))) {
+  } else if (isTRUE(methods::is(mat, "sparseMatrix"))) {
     return(list(mat.update = mat,
                 err.ind = 0,
                 dim = dim(mat)))
@@ -1009,6 +1011,8 @@ check.matrix <- function(mat, mat.name, mat.cat, inside.list) {
 
 #' Check function: vector
 #'
+#' @importFrom methods is
+#'
 #' @description This function checks if the matrix objects in \code{lpmodel}
 #'    are in the correct format. If not, an error message is displayed.
 #'
@@ -1025,7 +1029,7 @@ check.vector <- function(vec, vec.name, inside.list) {
   msg.vector <- paste0("The object '%s' in 'lpmodel' has to be a %s.")
 
   # Turn it into a matrix if it is not a list
-  if (!is.list(vec) | !is(vec, "sparseMatrix")) {
+  if (!is.list(vec) | !methods::is(vec, "sparseMatrix")) {
     vec <- as.matrix(vec)
   }
   if (nrow(vec) != 1 & ncol(vec) != 1) {

R/estbounds.R

@@ -184,6 +184,8 @@ estbounds <- function(data = NULL, lpmodel, kappa = 0, norm = 2,
 
 #' Computes the true bounds subjected to shape constraints
 #'
+#' @importFrom methods is
+#'
 #' @description This function computes the true bounds subjected to the shape
 #'    constraints without approximation.
 #'
@@ -221,7 +223,7 @@ estbounds.original <- function(data, lpmodel, original.sense, solver) {
   # Matrices
   A.obs.hat <- lpmodel.eval(data, lpmodel$A.obs, 1)
   A.original <- rbind(A.obs.hat, A.shp.matrix)
-  if (!is.matrix(A.original) & !is(A.original, "sparseMatrix")) {
+  if (!is.matrix(A.original) & !methods::is(A.original, "sparseMatrix")) {
     A.original <- matrix(A.original, nrow = 1)
   }
 
@@ -309,6 +311,8 @@ estbounds.original <- function(data, lpmodel, original.sense, solver) {
 
 #' Estimates the bounds with shape constraints (stage 2 with 1-norm)
 #'
+#' @importFrom methods is
+#'
 #' @description This function evaluates the solution to stage 2 of the
 #'    two-step procedure to obtain the estimated bound with the 1-norm.
 #'
@@ -356,7 +360,7 @@ estbounds2.L1 <- function(data, firststepsoln, lpmodel, modelsense, kappa,
   # Update the linear constraint
   c <- larg$bf
   A.step2 <- rbind(larg$A, c)
-  if (!is.matrix(A.step2) & !is(A.step2, "sparseMatrix")) {
+  if (!is.matrix(A.step2) & !methods::is(A.step2, "sparseMatrix")) {
     A.step2 <- matrix(A.step2, nrow = 1)
   }
   b.step2 <- Reduce(rbind, c(larg$rhs, Qhat * (1 + kappa)))

R/fsst.R

@@ -587,6 +587,7 @@ full.beta.bs <- function(lpmodel, beta.tgt, beta.obs.bs, R) {
 #'   \eqn{\hat{\beta}_{{\rm obs}, n}}.
 #'
 #' @import furrr progressr
+#' @importFrom utils tail
 #'
 #' @inheritParams dkqs.bs
 #' @inheritParams dkqs.bs.fn
@@ -644,7 +645,7 @@ fsst.beta.bs <- function(n, data, beta.obs.hat, lpmodel, R, maxR, progress,
       } else {
          bs.list <- iseq
          i0 <- bs.list[1]
-         i1 <- tail(bs.list, n = 1)
+         i1 <- utils::tail(bs.list, n = 1)
       }
 
       # Set the default for progress bar
@@ -838,7 +839,9 @@ fsst.weight.matrix <- function(weight.matrix, beta.obs.hat, beta.sigma) {
 #' @return Returns the estimator of the asymptotic variance.
 #'     \item{sigma.mat}{The estimator of the asymptotic variance.}
 #'
-#' @export
+#' @usage sigma.summation(n, beta.bs.list, progress, eval.count)
+#'
+#' @export sigma.summation
 #'
 sigma.summation <- function(n, beta.bs.list, progress, eval.count) {
    beta.obs.hat <- beta.bs.list[[1]]
@@ -1001,6 +1004,8 @@ beta.star.qp <- function(data, lpmodel, beta.tgt, weight.mat, beta.obs.hat,
 
 #' Computes the solution to the cone problem
 #'
+#' @importFrom methods as
+#'
 #' @description This function computes the solution to the cone problem.
 #'
 #' @importFrom Matrix t
@@ -1043,9 +1048,9 @@ fsst.cone.lp <- function(n, omega.i, beta.n, beta.star, lpmodel, indicator,
 
    # Construct the constraints matrix
    A <- rbind(lpmodel$A.obs, lpmodel$A.shp, lpmodel$A.tgt)
-   A.mat1 <- as(cbind(omega.i, -diag(p), diag(p)), "sparseMatrix")
+   A.mat1 <- methods::as(cbind(omega.i, -diag(p), diag(p)), "sparseMatrix")
    A.mat2 <- cbind(zero.p, ones.p, ones.p)
-   A.mat3 <- as(cbind(Matrix::t(A), zero.dp, zero.dp), "sparseMatrix")
+   A.mat3 <- methods::as(cbind(Matrix::t(A), zero.dp, zero.dp), "sparseMatrix")
    A.mat <- rbind(A.mat1, A.mat2, A.mat3)
 
    # Construct RHS vector
@@ -1078,7 +1083,7 @@ fsst.cone.lp <- function(n, omega.i, beta.n, beta.star, lpmodel, indicator,
       # Update constraints matrix
       A.mat.ext1 <- asmat(cbind(A.mat, zero.Am))
       A.mat.ext2 <- asmat(cbind(diag(p), zero.pp, zero.pp, -A))
-      A.mat.ext <- as(rbind(A.mat.ext1, A.mat.ext2), "sparseMatrix")
+      A.mat.ext <- methods::as(rbind(A.mat.ext1, A.mat.ext2), "sparseMatrix")
 
       # Update RHS vector
       rhs.ext <- Reduce(rbind, c(rhs.mat, zero.p1))
@@ -1499,7 +1504,7 @@ fsst.range <- function(n, beta.obs.hat, x.star, lpmodel, weight.mat.root) {
    # ---------------- #
    # Step 1: Compute the matrix inside the norm
    # ---------------- #
-   A.obs.hat <- lpmodel.eval(data, lpmodel$A.obs, 1)
+   A.obs.hat <- lpmodel$A.obs
    beta.obs.star <- A.obs.hat %*% x.star
    range.arg <- sqrt(n) * weight.mat.root %*% (beta.obs.hat - beta.obs.star)
 
@@ -1904,6 +1909,8 @@ fsst.pval <- function(range.n, cone.n, range.n.list, cone.n.list, R,
 
 #' Checks and updates the input in \code{fsst}
 #'
+#' @importFrom methods is
+#'
 #' @description This function checks and updates the input from the user in the
 #'    \code{\link[lpinfer]{fsst}} function. If there is any invalid input,
 #'    the function will be terminated and error messages will be printed.
@@ -2088,7 +2095,7 @@ fsst.check <- function(data, lpmodel, beta.tgt, R, Rmulti, lambda, rho, n,
          # It can be a square 'data.frame', 'matrix' or a 'sparseMatrix'.
          omega.i <- previous.output$omega.i
          if (!(is.matrix(omega.i) | is.data.frame(omega.i) |
-             is(omega.i, "sparseMatrix"))) {
+             methods::is(omega.i, "sparseMatrix"))) {
             omega.i <- NA
             warning(paste0("The class of the 'omega.i' matrix in the list ",
                            "'previous.output' has to be one of the ",

R/invertci.R

@@ -696,6 +696,7 @@ print.invertci_single <- function(x, ...) {
 #'    function \code{\link[lpinfer]{invertci}}.
 #'
 #' @inheritParams print.invertci
+#' @inheritParams summary.invertci
 #'
 #' @return Nothing is returned
 #'
@@ -870,13 +871,14 @@ summary.invertci_multiple <- function(x, alphas, msg.bound, ...) {
 #'    the results in each step of the bisection method.
 #'
 #' @inheritParams bisec.print
+#' @inheritParams print.invertci
 #' @param i The row number that we want to print for \code{df_bis}.
 #'
 #' @return Nothing is returned.
 #'
 #' @export
 #'
-summary.bisection.print <- function(df_bis, i) {
+summary.bisection.print <- function(df_bis, i, ...) {
   # ---------------- #
   # Step 1: Data cleaning
   # ---------------- #

R/lpmodel.R

@@ -1,5 +1,7 @@
 #' Evaluates an object inside \code{lpmodel}
 #'
+#' @importFrom methods is
+#'
 #' @description This function returns the matrix or vector depending on the
 #'   class of the variable in the \code{lpmodel} object. In the design of
 #'   the \code{lpinfer} module, objects in \code{lpmodel} can have three
@@ -27,7 +29,8 @@ lpmodel.eval <- function(data, obj, i) {
     obj.eval <- obj(data)
   } else if (class(obj) == "list") {
     obj.eval <- obj[[i]]
-  } else if (!is.matrix(obj) & !is.data.frame(obj) & !is(obj, "sparseMatrix")) {
+  } else if (!is.matrix(obj) & !is.data.frame(obj) &
+             !methods::is(obj, "sparseMatrix")) {
     obj.eval <- matrix(obj, nrow = 1)
   } else if (class(obj) == "data.frame") {
     obj.eval <- as.matrix(obj)
@@ -222,6 +225,8 @@ lpmodel.natural <- function(A.obs = NULL, A.shp = NULL, A.tgt = NULL,
 
 #' Print the \code{lpmodel} or \code{lpmodel.natural} object
 #'
+#' @importFrom methods is
+#'
 #' @description This function prints the details of the components that are
 #'    contained in the \code{lpmodel} or \code{lpmodel.natural} object.
 #'
@@ -278,7 +283,7 @@ lpm.print <- function(x, lpm.string, data = NULL, ...) {
             dimension.tmp <- paste0(dimension.str[1], "x", dimension.str[2])
           }
         } else if (class.tmp %in% c("data.frame", "matrix", "numeric") |
-                   is(obj, "sparseMatrix")) {
+                   methods::is(obj, "sparseMatrix")) {
           dim.obj <- dim(obj)
           if (is.null(dim.obj)) {
             dimension.tmp <- paste0("1x", length(obj))

R/misc.R

@@ -188,6 +188,8 @@ error.id.match <- function(error.list, df.error) {
 
 #' Coerces a \code{sparseMatrix} as a \code{matrix}
 #'
+#' @importFrom methods is
+#'
 #' @description This function coerces a \code{sparseMatrix} as a
 #'   \code{matrix}. This function is used specifically in the
 #'   \code{\link[lpinfer]{gurobi.optim}} function to ensure that the
@@ -200,7 +202,7 @@ error.id.match <- function(error.list, df.error) {
 #' @export
 #'
 smatrixconvert <- function(mat) {
-  if (is(mat, "sparseMatrix") | is(mat, "Matrix")) {
+  if (methods::is(mat, "sparseMatrix") | methods::is(mat, "Matrix")) {
     return(as.matrix(mat))
   } else {
     return(mat)
@@ -209,6 +211,9 @@ smatrixconvert <- function(mat) {
 
 #' Coerces a \code{dgeMatrix} as a \code{sparseMatrix}
 #'
+#' @importFrom methods is
+#' @importFrom methods as
+#'
 #' @description This function coerces a \code{dgeMatrix} as a
 #'   \code{sparseMatrix}.This function is used specifically in the
 #'   \code{\link[lpinfer]{gurobi.optim}} function to ensure that the
@@ -221,15 +226,17 @@ smatrixconvert <- function(mat) {
 #' @export
 #'
 dmatrixconvert <- function(mat) {
-  if (is(mat, "dgeMatrix")) {
-    return(as(mat, "sparseMatrix"))
+  if (methods::is(mat, "dgeMatrix")) {
+    return(methods::as(mat, "sparseMatrix"))
   } else {
     return(mat)
   }
 }
 
 #' Coerces non-\code{sparseMatrix} objects as \code{matrix}
 #'
+#' @importFrom methods is
+#'
 #' @description This function coerse non-\code{sparseMatrix} objects as
 #'   \code{matrix} and keeps \code{sparseMatrix} unchanged.
 #'
@@ -240,7 +247,7 @@ dmatrixconvert <- function(mat) {
 #' @export
 #'
 asmat <- function(obj) {
-  if (is(obj, "sparseMatrix")) {
+  if (methods::is(obj, "sparseMatrix")) {
     return(obj)
   } else {
     return(as.matrix(obj))

R/optim.R

@@ -127,8 +127,8 @@ cplexapi.optim <- function(Af, bf, nf, A, rhs, sense, modelsense, lb,
   # Step 2: Update the notations
   # ---------------- #
   # Model sense
-  modelsense[modelsense == "min"] <- CPX_MIN
-  modelsense[modelsense == "max"] <- CPX_MAX
+  modelsense[modelsense == "min"] <- cplexAPI::CPX_MIN
+  modelsense[modelsense == "max"] <- cplexAPI::CPX_MAX
 
   # Inequality/equality signs
   sense[sense == "<="] <- "L"
@@ -139,7 +139,7 @@ cplexapi.optim <- function(Af, bf, nf, A, rhs, sense, modelsense, lb,
   # Bounds
   lb[lb == Inf] <- cplexAPI::CPX_INFBOUND
   lb[lb == -Inf] <- -cplexAPI::CPX_INFBOUND
-  ub <- rep(CPX_INFBOUND, length(lb))
+  ub <- rep(cplexAPI::CPX_INFBOUND, length(lb))
 
   # ---------------- #
   # Step 3: cplexAPI environment
@@ -162,7 +162,7 @@ cplexapi.optim <- function(Af, bf, nf, A, rhs, sense, modelsense, lb,
   # Step 4: Solve the problem
   # ---------------- #
   # A linear program is identified if obj2 == NULL
-  if (is.null(obj2) == TRUE) {
+  if (is.null(objective_return$obj2) == TRUE) {
     # Solving linear program
     cplexAPI::copyLpwNamesCPLEX(env,
                                 prob,
@@ -217,7 +217,7 @@ rcplex.optim <- function(Af, bf, nf, A, rhs, sense, modelsense, lb,
   # ---------------- #
   # Step 1: Obtain the coefficients of the objective function
   # ---------------- #
-  objective_return = objective.function(Af, bf, nf, weight)
+  objective_return <- objective.function(Af, bf, nf, weight)
 
   # ---------------- #
   # Step 2: Update vectors and sense
@@ -468,9 +468,7 @@ objective.function <- function(A, b, n, weight = NULL) {
 #' LP solver by \code{lpSolveAPI}
 #'
 #' @description This function computes the solution to the linear program
-#'    using the \code{lpsolveAPI} package.
-#'
-#' @import lpSolveAPI
+#'    using the \code{lpSolveAPI} package.
 #'
 #' @inheritParams gurobi.optim
 #' @inheritParams dkqs
@@ -488,7 +486,7 @@ objective.function <- function(A, b, n, weight = NULL) {
 #' @export
 #'
 lpsolveapi.optim <- function(Af, bf, nf, A, rhs, sense, modelsense, lb,
-                             weight = diag(length(b))) {
+                             weight = NULL, ...) {
   # ---------------- #
   # Step 1: Obtain the coefficients of the objective function
   # ---------------- #