Fix Rd links and resubmit as 0.9.8

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: quanteda.textmodels
 Type: Package
 Title: Scaling Models and Classifiers for Textual Data
-Version: 0.9.7
+Version: 0.9.8
 Description: Scaling models and classifiers for sparse matrix objects representing 
     textual data in the form of a document-feature matrix.  Includes original 
     implementations of 'Laver', 'Benoit', and Garry's (2003) <doi:10.1017/S0003055403000698>,
@@ -51,7 +51,7 @@ License: GPL-3
 Encoding: UTF-8
 LazyData: true
 Language: en-GB
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 Roxygen: list(markdown = TRUE)
 Collate: 
     'RcppExports.R'

R/textmodel_affinity.R

@@ -2,9 +2,9 @@
 #'
 #' `textmodel_affinity()` implements the maximum likelihood supervised text
 #' scaling method described in Perry and Benoit (2017).
-#' @param x the [dfm] or [bootstrap_dfm] object on which the model
-#'   will be fit.  Does not need to contain only the training documents, since
-#'   the index of these will be matched automatically.
+#' @param x the [dfm][quanteda::dfm] or [bootstrap_dfm][quanteda::bootstrap_dfm]
+#'   object on which the model will be fit.  Does not need to contain only the
+#'   training documents, since the index of these will be matched automatically.
 #' @param y vector of training classes/scores associated with each document
 #'   identified in `data`
 #' @param exclude a set of words to exclude from the model

R/textmodel_ca.R

@@ -1,13 +1,14 @@
 #' Correspondence analysis of a document-feature matrix
 #'
 #' `textmodel_ca` implements correspondence analysis scaling on a
-#' [dfm].  The method is a fast/sparse version of function [ca][ca::ca].
+#' [dfm][quanteda::dfm].  The method is a fast/sparse version of function
+#' [ca][ca::ca].
 #' @param x the dfm on which the model will be fit
 #' @param smooth a smoothing parameter for word counts; defaults to zero.
 #' @param nd  Number of dimensions to be included in output; if `NA` (the
 #'   default) then the maximum possible dimensions are included.
 #' @param sparse retains the sparsity if set to `TRUE`; set it to
-#'   `TRUE` if `x` (the [dfm]) is too big to be allocated after
+#'   `TRUE` if `x` (the [dfm][quanteda::dfm]) is too big to be allocated after
 #'   converting to dense
 #' @param residual_floor specifies the threshold for the residual matrix for
 #'   calculating the truncated svd.Larger value will reduce memory and time cost
@@ -22,7 +23,7 @@
 #' @note You may need to set `sparse = TRUE`) and
 #'   increase the value of `residual_floor` to ignore less important
 #'   information and hence to reduce the memory cost when you have a very big
-#'   [dfm].
+#'   [dfm][quanteda::dfm].
 #'   If your attempt to fit the model fails due to the matrix being too large,
 #'   this is probably because of the memory demands of computing the \eqn{V
 #'   \times V} residual matrix.  To avoid this, consider increasing the value of

R/textmodel_lr.R

@@ -6,12 +6,13 @@
 #' elasticnet penalty at a grid of values for the regularization parameter
 #' lambda.  This is done automatically by testing on several folds of the data
 #' at estimation time.
-#' @param x the \link{dfm} on which the model will be fit.  Does not need to
-#'   contain only the training documents.
+#' @param x the [dfm][quanteda::dfm] on which the model will be fit.  Does not
+#'   need to contain only the training documents.
 #' @param y vector of training labels associated with each document identified
 #'   in \code{train}.  (These will be converted to factors if not already
 #'   factors.)
-#' @param ... additional arguments passed to [`cv.glmnet()`][glmnet::cv.glmnet()]
+#' @param ... additional arguments passed to
+#'   [`cv.glmnet()`][glmnet::cv.glmnet()]
 #' @returns an object of class `textmodel_lr`, a list containing:
 #' * `x`, `y` the input model matrix and input training class labels
 #' * `algorithm` character; the type and family of logistic regression model used in calling

R/textmodel_lsa.R

@@ -1,8 +1,8 @@
 #' Latent Semantic Analysis
 #'
-#' Fit the Latent Semantic Analysis scaling model to a [dfm], which may be
-#' weighted (for instance using [quanteda::dfm_tfidf()]).
-#' @param x the [dfm] on which the model will be fit
+#' Fit the Latent Semantic Analysis scaling model to a [dfm][quanteda::dfm],
+#' which may be weighted (for instance using [quanteda::dfm_tfidf()]).
+#' @param x the [dfm][quanteda::dfm] on which the model will be fit
 #' @param nd  the number of dimensions to be included in output
 #' @param margin margin to be smoothed by the SVD
 #' @returns a `textmodel_lsa` class object, a list containing:

R/textmodel_nb.R

@@ -2,8 +2,8 @@
 #'
 #' Fit a multinomial or Bernoulli Naive Bayes model, given a dfm and some
 #' training labels.
-#' @param x the [dfm] on which the model will be fit.  Does not need to
-#'   contain only the training documents.
+#' @param x the [dfm][quanteda::dfm] on which the model will be fit.  Does not
+#'   need to contain only the training documents.
 #' @param y vector of training labels associated with each document identified
 #'   in `train`.  (These will be converted to factors if not already
 #'   factors.)

R/textmodel_svm.R

@@ -2,8 +2,8 @@
 #'
 #' Fit a fast linear SVM classifier for texts, using the
 #' \pkg{LiblineaR} package.
-#' @param x the [dfm] on which the model will be fit.  Does not need to
-#'   contain only the training documents.
+#' @param x the [dfm][quanteda::dfm] on which the model will be fit.  Does not
+#'   need to contain only the training documents.
 #' @param y vector of training labels associated with each document identified
 #'   in `train`.  (These will be converted to factors if not already
 #'   factors.)
@@ -213,7 +213,7 @@ print.predict.textmodel_svm <- function(x, ...) {
 #' convert a dfm into a matrix.csr from SparseM package
 #'
 #' Utility to convert a dfm into a [matrix.csr][SparseM::matrix.csr] from the \pkg{SparseM} package.
-#' @param x input [dfm]
+#' @param x input [dfm][quanteda::dfm]
 #' @param ... unused
 #' @importFrom SparseM as.matrix.csr
 #' @importFrom methods new

R/textmodel_svmlin.R

@@ -11,8 +11,8 @@
 #' way that translates into more standard SVM parameters that we understand. Use
 #' with caution after reading the Sindhwani and Keerthi (2006) paper.
 #'
-#' @param x the [dfm] on which the model will be fit.  Does not need to contain
-#'   only the training documents.
+#' @param x the [dfm][quanteda::dfm] on which the model will be fit.  Does not
+#'   need to contain only the training documents.
 #' @param y vector of training labels associated with each document identified
 #'   in `train`.  (These will be converted to factors if not already factors.)
 #' @param intercept logical; if `TRUE`, add an intercept to the data

R/textmodel_wordscores.R

@@ -10,7 +10,7 @@
 #' unknown texts are obtained using the `predict()` method to score
 #' documents from a fitted `textmodel_wordscores` object.
 #'
-#' @param x the [dfm] on which the model will be trained
+#' @param x the [dfm][quanteda::dfm] on which the model will be trained
 #' @param y vector of training scores associated with each document
 #'   in `x`
 #' @param smooth a smoothing parameter for word counts; defaults to zero to

R/utils.R

@@ -7,7 +7,7 @@ catm <- quanteda:::catm
 #'
 #' Takes a dfm and a set of features, and makes them match the features listed
 #' in the set.
-#' @param x input [dfm]
+#' @param x input [dfm][quanteda::dfm]
 #' @param features character; a vector of feature names
 #' @param force logical; if `TRUE`, make the new dfm conform to the vector of
 #'   features, otherwise return an error message

cran-comments.md

@@ -1,14 +1,13 @@
 ## Purpose
 
-To fix the compilation errors caused by changes to the TBB library and RcppParallel. This version links to quanteda v4.0 that does not rely on RcppParallel.
-
-Also fixes some minor method class and methods signature issues caught by the improved CRAN checks in the forthcoming R v4.
+To rescue quanteda.textmodels from CRAN archives, after fixing the issues with
+it that led to its archiving on 25 August 2024.
 
 # Checks
 
 ## Test environments
 
-* local macOS 14.2.1, R 4.3.3
+* local macOS 14.4.1, R 4.4.1
 * Windows release via devtools::check_win_release()
 * Windows devel via devtools::check_win_devel()
 * Windows oldrelease via devtools::check_win_oldrelease()
@@ -23,25 +22,6 @@ No ERRORs, NOTEs, or WARNINGs produced, except:
     data   3.8Mb
     libs   3.0Mb
 
-On devtools::check_win_oldrelease(), we saw this false positive:
-
-* using log directory 'd:/RCompile/CRANguest/R-oldrelease/quanteda.textmodels.Rcheck'
-* using R version 4.2.3 (2023-03-15 ucrt)
-* using platform: x86_64-w64-mingw32 (64-bit)
-* using session charset: UTF-8
-* checking for file 'quanteda.textmodels/DESCRIPTION' ... OK
-* checking extension type ... Package
-* this is package 'quanteda.textmodels' version '0.9.7'
-* package encoding: UTF-8
-* checking CRAN incoming feasibility ... NOTE
-Maintainer: 'Kenneth Benoit <kbenoit@lse.ac.uk>'
-
-Found the following (possibly) invalid DOIs:
-  DOI: 10.1111/j.1540-5907.2008.00338.x
-    From: DESCRIPTION
-    Status: Forbidden
-    Message: 403
-
 ## Reverse dependency and other package conflicts
 
 None, according to revdepcheck::revdep_check().