fixes for R CMD check

.Rbuildignore

@@ -7,6 +7,7 @@
 ^\.git
 ^\.github
 ^\.gitignore
+^\.covrignore
 ^codecov.yml
 ^src/Makevars$
 ^tests/testthat/__snaps$

.github/recipe/recipe.yaml

@@ -17,8 +17,12 @@ build:
 requirements:
   host:
     - r-base
+    - r-matrixstats
+    - r-rfast
   run:
     - r-base
+    - r-matrixstats
+    - r-rfast
 
 tests:
   - script:

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: colocboost
 Type: Package
 Date: 2024-09-01
-Title: Multi-context colocalization analysis tool for molecular QTL and GWAS studies
+Title: Multi-Context Colocalization Analysis Tool for Molecular QTL and GWAS Studies
 Version: 0.1.0
 Authors@R: c(
   person(given = "Xuewei", family = "Cao", email = "xc2270@cumc.columbia.edu", role = c("cre", "aut")),
@@ -12,7 +12,7 @@ Authors@R: c(
   person(given = "Kushal", family = "Dey", email = "deyk@mskcc.org", role = c("aut")),
   person(given = "Gao", family = "Wang", email = "gw2411@cumc.columbia.edu", role = c("aut"))
   )
-Maintainer: The package maintainer <xc2270@cumc.columbia.edu>
+Maintainer: Xuewei Cao <xc2270@cumc.columbia.edu>
 Description: This R package implements ColocBoost --- motivated and designed for colocalization analysis of multiple genetic association studies --- as a multi-task learning approach to variable selection regression with highly correlated predictors and sparse effects, based on frequentist statistical inference. It provides statistical evidence to identify which subsets of predictors have non-zero effects on which subsets of response variables.
 Encoding: UTF-8
 LazyData: true
@@ -33,4 +33,4 @@ Suggests:
 VignetteBuilder: knitr
 Roxygen: list(markdown = TRUE)
 Config/testthat/edition: 3
-License: MIT
+License: MIT + file LICENSE

LICENSE

@@ -0,0 +1,2 @@
+YEAR: 2025
+COPYRIGHT HOLDER: StatFunGen authors

NAMESPACE

@@ -7,3 +7,28 @@ export(colocboost_get_methods)
 export(colocboost_inits)
 export(colocboost_update)
 export(colocboost_workhorse)
+importFrom(grDevices,adjustcolor)
+importFrom(graphics,abline)
+importFrom(graphics,axis)
+importFrom(graphics,legend)
+importFrom(graphics,mtext)
+importFrom(graphics,par)
+importFrom(graphics,points)
+importFrom(graphics,text)
+importFrom(stats,as.dist)
+importFrom(stats,cor)
+importFrom(stats,cutree)
+importFrom(stats,density)
+importFrom(stats,dnorm)
+importFrom(stats,hclust)
+importFrom(stats,median)
+importFrom(stats,na.omit)
+importFrom(stats,pchisq)
+importFrom(stats,pnorm)
+importFrom(stats,predict)
+importFrom(stats,qnorm)
+importFrom(stats,quantile)
+importFrom(stats,smooth.spline)
+importFrom(stats,var)
+importFrom(utils,head)
+importFrom(utils,tail)

R/colocboost.R

@@ -98,6 +98,7 @@
 #' \item{vcp}{The variable colocalized probability for each variable.}
 #' \item{data_info}{A object with detailed information from input data}
 #'
+#' @importFrom stats na.omit
 #' @export
 #'
 #' @examples

R/colocboost_addhoc_utils.R

@@ -108,7 +108,7 @@ merge_cos_ucos <- function(cb_obj, out_cos, out_ucos, coverage = 0.95,
 
 }
 
-
+#' @importFrom stats na.omit
 merge_ucos <- function(cb_obj, past_out,
                        min_abs_corr = 0.5,
                        median_abs_corr = NULL,

R/colocboost_assemble.R

@@ -39,7 +39,7 @@ colocboost_assemble <- function(cb_obj,
                                 tol = 1e-9,
                                 output_level = 1){
 
-    if (class(cb_obj) != "colocboost"){
+    if (!inherits(cb_obj, "colocboost")){
         stop("Input must from colocboost object!")}
 
     # - data information

R/colocboost_assemble_cos.R

@@ -1,3 +1,4 @@
+#' @importFrom stats as.dist cutree hclust
 colocboost_assemble_cos <- function(cb_obj,
                                     coverage = 0.95,
                                     func_intw = "fun_R",
@@ -14,7 +15,7 @@ colocboost_assemble_cos <- function(cb_obj,
                                     between_purity = 0.8,
                                     tol = 1e-9){
 
-    if (class(cb_obj) != "colocboost"){
+    if (!inherits(cb_obj, "colocboost")){
         stop("Input must from colocboost function!")}
 
     cb_model <- cb_obj$cb_model

R/colocboost_assemble_ucos.R

@@ -1,7 +1,4 @@
-
-
-
-
+#' @importFrom stats as.dist cutree hclust
 colocboost_assemble_ucos <- function(cb_obj_single,
                                      coverage = 0.95,
                                      check_null = 0.1,
@@ -17,7 +14,7 @@ colocboost_assemble_ucos <- function(cb_obj_single,
                                      weaker_ucos = TRUE,
                                      tol = 1e-9){
 
-    if (class(cb_obj_single) != "colocboost"){
+    if (!inherits(cb_obj_single, "colocboost")){
         stop("Input must from colocboost function!")}
 
     cb_data <- cb_obj_single$cb_data

R/colocboost_check_update_jk.R

@@ -50,7 +50,7 @@ colocboost_check_update_jk <- function(cb_model, cb_model_para, cb_data,
 
 
 
-
+#' @importFrom stats median
 boost_check_update_jk_notarget <- function(cb_model, cb_model_para, cb_data,
                                             prioritize_jkstar = TRUE,
                                             jk_equiv_cor = 0.8, ##### more than 2 traits
@@ -534,7 +534,7 @@ boost_check_update_jk_target <- function(cb_model, cb_model_para, cb_data,
     return(cb_model_para)
 }
 
-
+#' @importFrom stats cor
 get_LD_jk1_jk2 <- function(jk1, jk2,
                            X = NULL, XtX = NULL, N = NULL,
                            remain_jk = NULL){
@@ -660,4 +660,4 @@ estimate_change_profile_res <- function(jk,
 }
 
 
-
+

R/colocboost_init.R

@@ -335,6 +335,7 @@ colocboost_init_model <- function(cb_data,
 
 #' @noRd
 #' @keywords cb_objects
+#' @importFrom utils tail
 colocboost_init_para <- function(cb_data, cb_model,tau=0.01,
                                  func_prior = "z2z",
                                  lambda = 0.5, lambda_target = 1,
@@ -508,7 +509,7 @@ get_lfsr <- function(z, miss_idx = NULL, ash_prior = "normal"){
     return(lfsr)
 }
 
-
+#' @importFrom stats pchisq
 get_lfdr <- function(z, miss_idx = NULL){
     P <- length(z)
     lambda_max <- 0.95
@@ -519,7 +520,7 @@ get_lfdr <- function(z, miss_idx = NULL){
             result <- try({
                 lfdr_nomissing <- qvalue(pchisq(drop(z^2), 1, lower.tail = FALSE), lambda = seq(0.05, lambda_max, 0.05))$lfdr
             }, silent = TRUE)
-            if(class(result) == "try-error") {
+            if(inherits(result, "try-error")) {
                 lambda_max <- lambda_max - 0.05 # Decrement lambda_max if error occurs
             } else {try_run = 0}
         }
@@ -532,7 +533,7 @@ get_lfdr <- function(z, miss_idx = NULL){
             result <- try({
                 lfdr <- qvalue(pchisq(drop(z^2), 1, lower.tail = FALSE), lambda = seq(0.05, lambda_max, 0.05))$lfdr
             }, silent = TRUE)
-            if(class(result) == "try-error") {
+            if(inherits(result, "try-error")) {
                 lambda_max <- lambda_max - 0.05 # Decrement lambda_max if error occurs
             } else {try_run = 0}
         }
@@ -541,7 +542,7 @@ get_lfdr <- function(z, miss_idx = NULL){
     return(lfdr)
 }
 
-                                                                                                                   
+#' @importFrom stats pchisq
 get_padj <- function(z, miss_idx = NULL, p.adjust.methods = "fdr"){ # test also p.adjust.methods = "BY"
     P <- length(z)
     if (length(miss_idx)!=0){

R/colocboost_output.R

@@ -594,7 +594,7 @@ get_summary_table_fm <- function(cb_output, outcome_names = NULL, gene_name = NU
 }
 
 
-
+#' @importFrom stats pchisq
 cos_pvalue_filter <- function(cos_results, data_info = NULL, pvalue_cutoff = 1e-4){
 
   if (is.null(data_info))

R/colocboost_plot.R

@@ -1,5 +1,6 @@
-
-
+#' @importFrom utils head tail
+#' @importFrom graphics abline axis legend mtext par points text
+#' @importFrom grDevices adjustcolor
 colocboost_plot <- function(cb_output, y = "log10p", 
                             gene_name = NULL,
                             outcome_idx = NULL, 
@@ -28,7 +29,7 @@ colocboost_plot <- function(cb_output, y = "log10p",
                             ...){
 
 
-    if (class(cb_output) != "colocboost"){
+    if (!inherits(cb_output, "colocboost")){
         stop("Input of colocboost_plot must be a 'colocboost' object!")}
 
     # get cb_plot_input data from colocboost results
@@ -372,7 +373,7 @@ get_input_plot <- function(cb_output, plot_cos_idx = NULL,
 }
 
 
-
+#' @importFrom stats pnorm
 plot_initial <- function(cb_plot_input, y = "log10p", 
                          points_color = "grey90", cos_color = NULL,
                          ylim_each = TRUE, gene_name = NULL,

R/colocboost_update.R

@@ -5,6 +5,7 @@
 #' @details
 #' The gradient boosting algorithm for multiple outcomes
 #'
+#' @importFrom utils head tail
 #' @return colocboost object after gradient boosting update
 #' @export
 colocboost_update <- function(cb_model, cb_model_para, cb_data,

R/colocboost_utils.R

@@ -36,6 +36,7 @@ get_cormat <- function(X, intercepte = FALSE){
 }
 
 
+#' @importFrom utils head tail
 check_null_post <- function(cb_obj, 
                             coloc_sets_temp,
                             coloc_outcomes,
@@ -191,8 +192,8 @@ check_null_post <- function(cb_obj,
   return(ll)
 }
 
-
-get_purity = function (pos, X=NULL, Xcorr=NULL, N = NULL, n = 100) {
+#' @importFrom stats na.omit
+get_purity <- function(pos, X=NULL, Xcorr=NULL, N = NULL, n = 100) {
     get_upper_tri = Rfast::upper_tri
     get_median    = Rfast::med
 
@@ -256,7 +257,7 @@ get_modularity <- function(Weight, B){
     }
 }
 
-
+#' @importFrom stats cutree
 get_n_cluster <- function(hc, Sigma, m=ncol(Sigma), between_cluster = 0.8){
     if (min(Sigma) > between_cluster){
         IND = 1
@@ -300,8 +301,9 @@ w_purity <- function(weights, X=NULL, Xcorr=NULL, N = NULL, n = 100, coverage =
 }
 
 
+#' @importFrom stats na.omit
 # - Calculate purity between two confidence sets
-get_between_purity = function (pos1, pos2, X=NULL, Xcorr=NULL, N = NULL, miss_idx = NULL, P = NULL){
+get_between_purity <- function(pos1, pos2, X=NULL, Xcorr=NULL, N = NULL, miss_idx = NULL, P = NULL){
 
     get_matrix_mult <- function(X_sub1, X_sub2){
 
@@ -338,7 +340,8 @@ get_between_purity = function (pos1, pos2, X=NULL, Xcorr=NULL, N = NULL, miss_id
     return(c(min(value), max(value), get_median(value)))
 }
 
-
+#' @importFrom stats var
+#' @importFrom utils tail
 get_cos_evidence <- function(cb_obj, coloc_out, data_info){
 
   get_cos_config <- function(w, config_idx, alpha = 1.5, coverage = 0.95){

R/colocboost_workhorse.R

@@ -37,7 +37,7 @@ colocboost_workhorse <- function(cb_data,
                                  outcome_names = NULL){
 
 
-    if (class(cb_data) != "colocboost"){
+    if (!inherits(cb_data, "colocboost")){
         stop("Input must from colocboost function!")}
 
     cb_model <- colocboost_init_model(cb_data,

R/qval.R

@@ -162,6 +162,8 @@ qvalue <- function(p, fdr.level = NULL, pfdr = FALSE, lfdr.out = TRUE, pi0 = NUL
 #' against the tuning parameter \eqn{\lambda}{lambda}. Optional.
 #' @param \dots Arguments passed from \code{\link{qvalue}} function.
 #'
+#' @importFrom stats predict quantile smooth.spline
+#'
 #' @details
 #' If no options are selected, then the method used to estimate \eqn{\pi_0}{pi_0} is
 #' the smoother method described in Storey and Tibshirani (2003). The
@@ -296,6 +298,8 @@ pi0est <- function(p, lambda = seq(0.05,0.95,0.05), pi0.method = c("smoother", "
 #' @description
 #' Estimate the local FDR values from p-values.
 #'
+#' @importFrom stats density dnorm predict qnorm smooth.spline
+#'
 #' @param p A vector of p-values (only necessary input).
 #' @param pi0 Estimated proportion of true null p-values. If NULL, then \code{\link{pi0est}} is called.
 #' @param trunc If TRUE, local FDR values >1 are set to 1. Default is TRUE.