* using log directory ‘/data/gannet/ripley/R/packages/tests-LENGTH1/sparsebn.Rcheck’
* using R Under development (unstable) (2022-03-20 r81945)
* using platform: x86_64-pc-linux-gnu (64-bit)
* using session charset: UTF-8
* using option ‘--no-stop-on-test-error’
* checking for file ‘sparsebn/DESCRIPTION’ ... OK
* this is package ‘sparsebn’ version ‘0.1.2’
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for executable files ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘sparsebn’ can be installed ... [5s/10s] OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking loading without being on the library search path ... OK
* checking use of S3 registration ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... [5s/16s] OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd line widths ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking LazyData ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking installed files from ‘inst/doc’ ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... [6s/24s] ERROR
Running examples in ‘sparsebn-Ex.R’ failed
The error most likely occurred in:

> ### Name: estimate.covariance
> ### Title: Covariance estimation
> ### Aliases: estimate.covariance estimate.precision
> 
> ### ** Examples
> 
> 
> data(cytometryContinuous)
> dat <- sparsebnData(cytometryContinuous$data, type = "c", ivn = cytometryContinuous$ivn)
> estimate.covariance(dat) # estimate covariance
 ----------- FAILURE REPORT -------------- 
 --- failure: length > 1 in coercion to logical ---
 --- srcref --- 
: 
 --- package (from environment) --- 
ccdrAlgorithm
 --- call from context --- 
ccdr_singleR(cors, pp, nn, nj, indexj, betas, sigmas, lambdas[i], 
    weights, gamma = gamma, eps = eps, maxIters = maxIters, alpha = alpha, 
    verbose = verbose)
 --- call from argument --- 
weights < -1 || weights > 1
 --- R stacktrace ---
where 1: ccdr_singleR(cors, pp, nn, nj, indexj, betas, sigmas, lambdas[i], 
    weights, gamma = gamma, eps = eps, maxIters = maxIters, alpha = alpha, 
    verbose = verbose)
where 2: ccdr_gridR(cors, as.integer(pp), as.integer(nn), as.integer(nj), 
    as.integer(indexj), betas, as.numeric(sigmas), as.numeric(lambdas), 
    as.integer(weights), as.numeric(gamma), as.numeric(error.tol), 
    as.integer(max.iters), as.numeric(alpha), verbose)
where 3: ccdr_call(data = data_matrix, ivn = ivn_list, betas = betas, 
    sigmas = sigmas, lambdas = lambdas, lambdas.length = lambdas.length, 
    whitelist = whitelist, blacklist = blacklist, gamma = gamma, 
    error.tol = error.tol, rlam = NULL, max.iters = max.iters, 
    alpha = alpha, verbose = verbose)
where 4: ccdrAlgorithm::ccdr.run(data = data, lambdas = lambdas, lambdas.length = lambdas.length, 
    whitelist = whitelist, blacklist = blacklist, gamma = concavity, 
    error.tol = error.tol, max.iters = max.iters, alpha = alpha, 
    verbose = verbose)
where 5: estimate.dag(data, ...)
where 6: estimate.covariance(dat)

 --- value of length: 121 type: logical ---
  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[121] FALSE
 --- function from context --- 
function (cors, pp, nn, nj = NULL, indexj = NULL, betas, sigmas, 
    lambda, weights, gamma, eps, maxIters, alpha, verbose = FALSE) 
{
    if (!is.integer(pp) || !is.integer(nn)) 
        stop("Both pp and nn must be integers!")
    if (pp <= 0 || nn <= 0) 
        stop("Both pp and nn must be positive!")
    if (is.null(indexj)) 
        indexj <- rep(0L, pp + 1)
    if (is.null(nj)) 
        nj <- as.integer(rep(nn, pp))
    if (!is.vector(indexj)) 
        stop("Index vector for cors is not a vector.")
    if (length(indexj) > pp + 1) 
        stop(sprintf("Index vector for cors is too long, expected to be no greater than %d, the number of columns of data.", 
            pp))
    if (!is.integer(indexj)) 
        stop("Index vector for cors has non-integer component(s).")
    if (any(is.na(indexj) | is.null(indexj))) 
        stop("Index vector cannot have missing or NULL values.")
    if (any(indexj < 0 | indexj > pp + 1)) 
        stop(sprintf("Index vector for cors has out-of-range component(s), expected to be between 0 and %d.", 
            pp))
    if (!is.vector(nj)) 
        stop("Intervention times vector is not a vector.")
    if (length(nj) != pp) 
        stop(sprintf("Length of intervention times vector is %d, expected to match the number of columns of data = %d", 
            length(nj), pp))
    if (!is.integer(nj)) 
        stop("Intervention times vector has non-integer component(s).")
    if (any(is.na(nj) | is.null(nj))) 
        stop("Intervention times vector cannot have missing or NULL values.")
    if (any(nj < 0 | nj > nn)) 
        stop(sprintf("Intervention times vector has out-of-range component(s), expected to be between 0 and %d.", 
            nn))
    if (!is.numeric(cors)) 
        stop("cors must be a numeric vector!")
    if (length(cors) != length(unique(indexj)) * pp * (pp + 1)/2) 
        stop(paste0("cors has incorrect length: Expected length = ", 
            length(unique(indexj)) * pp * (pp + 1)/2, " input length = ", 
            length(cors)))
    aj <- nj/nn
    if (sparsebnUtils::check_if_matrix(betas)) {
        betas <- .init_sbm(betas, rep(0, pp))
        betas <- reIndexC(betas)
    }
    else if (!is.SparseBlockMatrixR(betas)) {
        stop("Incompatible data passed for betas parameter: Should be either matrix or list in SparseBlockMatrixR format.")
    }
    if (!is.numeric(sigmas)) 
        stop("sigmas must be numeric!")
    if (length(sigmas) != pp) 
        stop(sprintf("sigmas must have length = %d!", pp))
    if (any(sigmas < 0)) {
        if (any(sigmas != -1)) {
            stop("sigmas must be > 0!")
        }
    }
    if (!is.numeric(lambda)) 
        stop("lambda must be numeric!")
    if (lambda < 0) 
        stop("lambda must be >= 0!")
    if (length(weights) != pp * pp) 
        stop(sprintf("weights must have length p^2 = %d!", pp * 
            pp))
    if (!is.numeric(weights)) 
        stop("weights must be numeric!")
    if (weights < -1 || weights > 1) 
        stop("weights out of bounds!")
    if (!is.numeric(gamma)) 
        stop("gamma must be numeric!")
    if (gamma < 0 && gamma != -1) 
        stop("gamma must be >= 0 (MCP) or = -1 (Lasso)!")
    if (!is.numeric(eps)) 
        stop("eps must be numeric!")
    if (eps <= 0) {
        if (eps < 0) 
            stop("eps must be >= 0!")
        if (eps == 0) 
            warning("eps is set to zero: This may cause the algorithm to fail to converge, and maxIters will be used to terminate the algorithm.")
    }
    if (!is.integer(maxIters)) 
        stop("maxIters must be an integer!")
    if (maxIters <= 0) 
        stop("maxIters must be > 0!")
    t1.ccdr <- proc.time()[3]
    ccdr.out <- singleCCDr(cors, betas, sigmas, nj, indexj, aj, 
        lambda, weights, c(gamma, eps, maxIters, alpha), verbose = verbose)
    t2.ccdr <- proc.time()[3]
    ccdr.out <- list(sbm = SparseBlockMatrixR(list(rows = ccdr.out$rows, 
        vals = ccdr.out$vals, blocks = ccdr.out$blocks, sigmas = ccdr.out$sigmas, 
        start = 0)), lambda = ccdr.out$lambda, nedge = ccdr.out$length, 
        pp = pp, nn = nn, time = t2.ccdr - t1.ccdr)
    ccdr.out$sbm <- sparsebnUtils::reIndexR(ccdr.out$sbm)
    ccdr.out
}
<bytecode: 0xa6055f8>
<environment: namespace:ccdrAlgorithm>
 --- function search by body ---
Function ccdr_singleR in namespace ccdrAlgorithm has this body.
 ----------- END OF FAILURE REPORT -------------- 
Fatal error: length > 1 in coercion to logical
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘testthat.R’ [6s/16s]
 [6s/16s] ERROR
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(sparsebn)
  Loading required package: sparsebnUtils
  Loading required package: ccdrAlgorithm
  Loading required package: discretecdAlgorithm
  
  sparsebn v0.1, Copyright (c) 2016-2020
  	Bryon Aragam, University of Chicago
  	Jiaying Gu, University of California, Los Angeles
  	Dacheng Zhang, University of California, Los Angeles
  	Qing Zhou, University of California, Los Angeles
  	Fei Fu
  
  Please cite our work! Type citation("sparsebn") for details.
  ---> Bugs? Please report any bugs at https://github.com/itsrainingdata/sparsebn/issues.
  
  > 
  > test_check("sparsebn")
   ----------- FAILURE REPORT -------------- 
   --- failure: length > 1 in coercion to logical ---
   --- srcref --- 
  : 
   --- package (from environment) --- 
  ccdrAlgorithm
   --- call from context --- 
  ccdr_singleR(cors, pp, nn, nj, indexj, betas, sigmas, lambdas[i], 
      weights, gamma = gamma, eps = eps, maxIters = maxIters, alpha = alpha, 
      verbose = verbose)
   --- call from argument --- 
  weights < -1 || weights > 1
   --- R stacktrace ---
  where 1: ccdr_singleR(cors, pp, nn, nj, indexj, betas, sigmas, lambdas[i], 
      weights, gamma = gamma, eps = eps, maxIters = maxIters, alpha = alpha, 
      verbose = verbose)
  where 2: ccdr_gridR(cors, as.integer(pp), as.integer(nn), as.integer(nj), 
      as.integer(indexj), betas, as.numeric(sigmas), as.numeric(lambdas), 
      as.integer(weights), as.numeric(gamma), as.numeric(error.tol), 
      as.integer(max.iters), as.numeric(alpha), verbose)
  where 3: ccdr_call(data = data_matrix, ivn = ivn_list, betas = betas, 
      sigmas = sigmas, lambdas = lambdas, lambdas.length = lambdas.length, 
      whitelist = whitelist, blacklist = blacklist, gamma = gamma, 
      error.tol = error.tol, rlam = NULL, max.iters = max.iters, 
      alpha = alpha, verbose = verbose)
  where 4: ccdrAlgorithm::ccdr.run(data = data, lambdas = lambdas, lambdas.length = lambdas.length, 
      whitelist = whitelist, blacklist = blacklist, gamma = concavity, 
      error.tol = error.tol, max.iters = max.iters, alpha = alpha, 
      verbose = verbose)
  where 5 at test-bwlist.R#22: estimate.dag(dat_cts, lambdas.length = nlambda, whitelist = blocks)
  where 6: eval(code, test_env)
  where 7: eval(code, test_env)
  where 8: withCallingHandlers({
      eval(code, test_env)
      if (!handled && !is.null(test)) {
          skip_empty()
      }
  }, expectation = handle_expectation, skip = handle_skip, warning = handle_warning, 
      message = handle_message, error = handle_error)
  where 9: doTryCatch(return(expr), name, parentenv, handler)
  where 10: tryCatchOne(expr, names, parentenv, handlers[[1L]])
  where 11: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
  where 12: doTryCatch(return(expr), name, parentenv, handler)
  where 13: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]), 
      names[nh], parentenv, handlers[[nh]])
  where 14: tryCatchList(expr, classes, parentenv, handlers)
  where 15: tryCatch(withCallingHandlers({
      eval(code, test_env)
      if (!handled && !is.null(test)) {
          skip_empty()
      }
  }, expectation = handle_expectation, skip = handle_skip, warning = handle_warning, 
      message = handle_message, error = handle_error), error = handle_fatal, 
      skip = function(e) {
      })
  where 16: test_code(desc, code, env = parent.frame(), reporter = reporter)
  where 17 at test-bwlist.R#21: test_that("White lists work OK", {
      dags <- estimate.dag(dat_cts, lambdas.length = nlambda, whitelist = blocks)
      expect_equal(num.edges(dags), rep(len_saturate, nlambda))
      dags <- estimate.dag(dat_disc, lambdas.length = nlambda, 
          whitelist = blocks)
      expect_equal(num.edges(dags), rep(len_saturate, nlambda))
      from <- "x"
      to <- "y"
      from.idx <- match(from, node_names)
      to.idx <- match(to, node_names)
      white <- rbind(c(from, to))
      dags <- estimate.dag(dat_cts, lambdas.length = nlambda, whitelist = white)
      check_edge <- all(unlist(lapply(dags, function(x) from.idx %in% 
          as.list(x$edges)[[to]])))
      expect_true(check_edge)
      dags <- estimate.dag(dat_disc, lambdas.length = nlambda, 
          whitelist = white)
      check_edge <- all(unlist(lapply(dags, function(x) from.idx %in% 
          as.list(x$edges)[[to]])))
      expect_true(check_edge)
      from <- "b"
      to <- "z"
      from.idx <- match(from, node_names)
      to.idx <- match(to, node_names)
      white <- rbind(c("x", "y"), c(from, to))
      dags <- estimate.dag(dat_cts, lambdas.length = nlambda, whitelist = white)
      check_edge <- all(unlist(lapply(dags, function(x) from.idx %in% 
          as.list(x$edges)[[to]])))
      expect_true(check_edge)
      dags <- estimate.dag(dat_disc, lambdas.length = nlambda, 
          whitelist = white)
      check_edge <- all(unlist(lapply(dags, function(x) from.idx %in% 
          as.list(x$edges)[[to]])))
      expect_true(check_edge)
  })
  where 18: eval(code, test_env)
  where 19: eval(code, test_env)
  where 20: withCallingHandlers({
      eval(code, test_env)
      if (!handled && !is.null(test)) {
          skip_empty()
      }
  }, expectation = handle_expectation, skip = handle_skip, warning = handle_warning, 
      message = handle_message, error = handle_error)
  where 21: doTryCatch(return(expr), name, parentenv, handler)
  where 22: tryCatchOne(expr, names, parentenv, handlers[[1L]])
  where 23: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
  where 24: doTryCatch(return(expr), name, parentenv, handler)
  where 25: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]), 
      names[nh], parentenv, handlers[[nh]])
  where 26: tryCatchList(expr, classes, parentenv, handlers)
  where 27: tryCatch(withCallingHandlers({
      eval(code, test_env)
      if (!handled && !is.null(test)) {
          skip_empty()
      }
  }, expectation = handle_expectation, skip = handle_skip, warning = handle_warning, 
      message = handle_message, error = handle_error), error = handle_fatal, 
      skip = function(e) {
      })
  where 28: test_code(NULL, exprs, env)
  where 29: source_file(path, child_env(env), wrap = wrap)
  where 30: FUN(X[[i]], ...)
  where 31: lapply(test_paths, test_one_file, env = env, wrap = wrap)
  where 32: doTryCatch(return(expr), name, parentenv, handler)
  where 33: tryCatchOne(expr, names, parentenv, handlers[[1L]])
  where 34: tryCatchList(expr, classes, parentenv, handlers)
  where 35: tryCatch(code, testthat_abort_reporter = function(cnd) {
      cat(conditionMessage(cnd), "\n")
      NULL
  })
  where 36: with_reporter(reporters$multi, lapply(test_paths, test_one_file, 
      env = env, wrap = wrap))
  where 37: test_files(test_dir = test_dir, test_package = test_package, 
      test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, 
      env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, 
      wrap = wrap, load_package = load_package)
  where 38: test_files(test_dir = path, test_paths = test_paths, test_package = package, 
      reporter = reporter, load_helpers = load_helpers, env = env, 
      stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, 
      wrap = wrap, load_package = load_package, parallel = parallel)
  where 39: test_dir("testthat", package = package, reporter = reporter, 
      ..., load_package = "installed")
  where 40: test_check("sparsebn")
  
   --- value of length: 25 type: logical ---
   [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
  [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
  [25] FALSE
   --- function from context --- 
  function (cors, pp, nn, nj = NULL, indexj = NULL, betas, sigmas, 
      lambda, weights, gamma, eps, maxIters, alpha, verbose = FALSE) 
  {
      if (!is.integer(pp) || !is.integer(nn)) 
          stop("Both pp and nn must be integers!")
      if (pp <= 0 || nn <= 0) 
          stop("Both pp and nn must be positive!")
      if (is.null(indexj)) 
          indexj <- rep(0L, pp + 1)
      if (is.null(nj)) 
          nj <- as.integer(rep(nn, pp))
      if (!is.vector(indexj)) 
          stop("Index vector for cors is not a vector.")
      if (length(indexj) > pp + 1) 
          stop(sprintf("Index vector for cors is too long, expected to be no greater than %d, the number of columns of data.", 
              pp))
      if (!is.integer(indexj)) 
          stop("Index vector for cors has non-integer component(s).")
      if (any(is.na(indexj) | is.null(indexj))) 
          stop("Index vector cannot have missing or NULL values.")
      if (any(indexj < 0 | indexj > pp + 1)) 
          stop(sprintf("Index vector for cors has out-of-range component(s), expected to be between 0 and %d.", 
              pp))
      if (!is.vector(nj)) 
          stop("Intervention times vector is not a vector.")
      if (length(nj) != pp) 
          stop(sprintf("Length of intervention times vector is %d, expected to match the number of columns of data = %d", 
              length(nj), pp))
      if (!is.integer(nj)) 
          stop("Intervention times vector has non-integer component(s).")
      if (any(is.na(nj) | is.null(nj))) 
          stop("Intervention times vector cannot have missing or NULL values.")
      if (any(nj < 0 | nj > nn)) 
          stop(sprintf("Intervention times vector has out-of-range component(s), expected to be between 0 and %d.", 
              nn))
      if (!is.numeric(cors)) 
          stop("cors must be a numeric vector!")
      if (length(cors) != length(unique(indexj)) * pp * (pp + 1)/2) 
          stop(paste0("cors has incorrect length: Expected length = ", 
              length(unique(indexj)) * pp * (pp + 1)/2, " input length = ", 
              length(cors)))
      aj <- nj/nn
      if (sparsebnUtils::check_if_matrix(betas)) {
          betas <- .init_sbm(betas, rep(0, pp))
          betas <- reIndexC(betas)
      }
      else if (!is.SparseBlockMatrixR(betas)) {
          stop("Incompatible data passed for betas parameter: Should be either matrix or list in SparseBlockMatrixR format.")
      }
      if (!is.numeric(sigmas)) 
          stop("sigmas must be numeric!")
      if (length(sigmas) != pp) 
          stop(sprintf("sigmas must have length = %d!", pp))
      if (any(sigmas < 0)) {
          if (any(sigmas != -1)) {
              stop("sigmas must be > 0!")
          }
      }
      if (!is.numeric(lambda)) 
          stop("lambda must be numeric!")
      if (lambda < 0) 
          stop("lambda must be >= 0!")
      if (length(weights) != pp * pp) 
          stop(sprintf("weights must have length p^2 = %d!", pp * 
              pp))
      if (!is.numeric(weights)) 
          stop("weights must be numeric!")
      if (weights < -1 || weights > 1) 
          stop("weights out of bounds!")
      if (!is.numeric(gamma)) 
          stop("gamma must be numeric!")
      if (gamma < 0 && gamma != -1) 
          stop("gamma must be >= 0 (MCP) or = -1 (Lasso)!")
      if (!is.numeric(eps)) 
          stop("eps must be numeric!")
      if (eps <= 0) {
          if (eps < 0) 
              stop("eps must be >= 0!")
          if (eps == 0) 
              warning("eps is set to zero: This may cause the algorithm to fail to converge, and maxIters will be used to terminate the algorithm.")
      }
      if (!is.integer(maxIters)) 
          stop("maxIters must be an integer!")
      if (maxIters <= 0) 
          stop("maxIters must be > 0!")
      t1.ccdr <- proc.time()[3]
      ccdr.out <- singleCCDr(cors, betas, sigmas, nj, indexj, aj, 
          lambda, weights, c(gamma, eps, maxIters, alpha), verbose = verbose)
      t2.ccdr <- proc.time()[3]
      ccdr.out <- list(sbm = SparseBlockMatrixR(list(rows = ccdr.out$rows, 
          vals = ccdr.out$vals, blocks = ccdr.out$blocks, sigmas = ccdr.out$sigmas, 
          start = 0)), lambda = ccdr.out$lambda, nedge = ccdr.out$length, 
          pp = pp, nn = nn, time = t2.ccdr - t1.ccdr)
      ccdr.out$sbm <- sparsebnUtils::reIndexR(ccdr.out$sbm)
      ccdr.out
  }
  <bytecode: 0x59ac488>
  <environment: namespace:ccdrAlgorithm>
   --- function search by body ---
  Function ccdr_singleR in namespace ccdrAlgorithm has this body.
   ----------- END OF FAILURE REPORT -------------- 
  Fatal error: length > 1 in coercion to logical
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes in ‘inst/doc’ ... OK
* checking re-building of vignette outputs ... [7s/16s] ERROR
Error(s) in re-building vignettes:
--- re-building ‘sparsebn-vignette.Rmd’ using rmarkdown
Loading required package: sparsebnUtils
Loading required package: ccdrAlgorithm
Loading required package: discretecdAlgorithm

sparsebn v0.1, Copyright (c) 2016-2020
	Bryon Aragam, University of Chicago
	Jiaying Gu, University of California, Los Angeles
	Dacheng Zhang, University of California, Los Angeles
	Qing Zhou, University of California, Los Angeles
	Fei Fu

Please cite our work! Type citation("sparsebn") for details.
---> Bugs? Please report any bugs at https://github.com/itsrainingdata/sparsebn/issues.

A list of interventions was not specified: Assuming data is purely observational.
 ----------- FAILURE REPORT -------------- 
 --- failure: length > 1 in coercion to logical ---
 --- srcref --- 
: 
 --- package (from environment) --- 
ccdrAlgorithm
 --- call from context --- 
ccdr_singleR(cors, pp, nn, nj, indexj, betas, sigmas, lambdas[i], 
    weights, gamma = gamma, eps = eps, maxIters = maxIters, alpha = alpha, 
    verbose = verbose)
 --- call from argument --- 
weights < -1 || weights > 1
 --- R stacktrace ---
where 1: ccdr_singleR(cors, pp, nn, nj, indexj, betas, sigmas, lambdas[i], 
    weights, gamma = gamma, eps = eps, maxIters = maxIters, alpha = alpha, 
    verbose = verbose)
where 2: ccdr_gridR(cors, as.integer(pp), as.integer(nn), as.integer(nj), 
    as.integer(indexj), betas, as.numeric(sigmas), as.numeric(lambdas), 
    as.integer(weights), as.numeric(gamma), as.numeric(error.tol), 
    as.integer(max.iters), as.numeric(alpha), verbose)
where 3: ccdr_call(data = data_matrix, ivn = ivn_list, betas = betas, 
    sigmas = sigmas, lambdas = lambdas, lambdas.length = lambdas.length, 
    whitelist = whitelist, blacklist = blacklist, gamma = gamma, 
    error.tol = error.tol, rlam = NULL, max.iters = max.iters, 
    alpha = alpha, verbose = verbose)
where 4: ccdrAlgorithm::ccdr.run(data = data, lambdas = lambdas, lambdas.length = lambdas.length, 
    whitelist = whitelist, blacklist = blacklist, gamma = concavity, 
    error.tol = error.tol, max.iters = max.iters, alpha = alpha, 
    verbose = verbose)
where 5: estimate.dag(data)
where 6: eval(expr, envir, enclos)
where 7: eval(expr, envir, enclos)
where 8: eval_with_user_handlers(expr, envir, enclos, user_handlers)
where 9: withVisible(eval_with_user_handlers(expr, envir, enclos, user_handlers))
where 10: withCallingHandlers(withVisible(eval_with_user_handlers(expr, 
    envir, enclos, user_handlers)), warning = wHandler, error = eHandler, 
    message = mHandler)
where 11: handle(ev <- withCallingHandlers(withVisible(eval_with_user_handlers(expr, 
    envir, enclos, user_handlers)), warning = wHandler, error = eHandler, 
    message = mHandler))
where 12: timing_fn(handle(ev <- withCallingHandlers(withVisible(eval_with_user_handlers(expr, 
    envir, enclos, user_handlers)), warning = wHandler, error = eHandler, 
    message = mHandler)))
where 13: evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos, 
    debug = debug, last = i == length(out), use_try = stop_on_error != 
        2L, keep_warning = keep_warning, keep_message = keep_message, 
    output_handler = output_handler, include_timing = include_timing)
where 14: evaluate::evaluate(...)
where 15: evaluate(code, envir = env, new_device = FALSE, keep_warning = !isFALSE(options$warning), 
    keep_message = !isFALSE(options$message), stop_on_error = if (is.numeric(options$error)) options$error else {
        if (options$error && options$include) 
            0L
        else 2L
    }, output_handler = knit_handlers(options$render, options))
where 16: in_dir(input_dir(), evaluate(code, envir = env, new_device = FALSE, 
    keep_warning = !isFALSE(options$warning), keep_message = !isFALSE(options$message), 
    stop_on_error = if (is.numeric(options$error)) options$error else {
        if (options$error && options$include) 
            0L
        else 2L
    }, output_handler = knit_handlers(options$render, options)))
where 17: eng_r(options)
where 18: block_exec(params)
where 19: call_block(x)
where 20: process_group.block(group)
where 21: process_group(group)
where 22: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), 
    error = function(e) {
        setwd(wd)
        cat(res, sep = "\n", file = output %n% "")
        message("Quitting from lines ", paste(current_lines(i), 
            collapse = "-"), " (", knit_concord$get("infile"), 
            ") ")
    })
where 23: process_file(text, output)
where 24: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
where 25: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), 
    output_dir = getwd(), ...)
where 26: vweave_rmarkdown(...)
where 27: engine$weave(file, quiet = quiet, encoding = enc)
where 28: doTryCatch(return(expr), name, parentenv, handler)
where 29: tryCatchOne(expr, names, parentenv, handlers[[1L]])
where 30: tryCatchList(expr, classes, parentenv, handlers)
where 31: tryCatch({
    engine$weave(file, quiet = quiet, encoding = enc)
    setwd(startdir)
    output <- find_vignette_product(name, by = "weave", engine = engine)
    if (!have.makefile && vignette_is_tex(output)) {
        texi2pdf(file = output, clean = FALSE, quiet = quiet)
        output <- find_vignette_product(name, by = "texi2pdf", 
            engine = engine)
    }
    outputs <- c(outputs, output)
}, error = function(e) {
    thisOK <<- FALSE
    fails <<- c(fails, file)
    message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", 
        file, conditionMessage(e)))
})
where 32: tools:::buildVignettes(dir = "/data/gannet/ripley/R/packages/tests-LENGTH1/sparsebn.Rcheck/vign_test/sparsebn")

 --- value of length: 11881 type: logical ---
    [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
   [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
   [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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 [1765] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [1777] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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 [1801] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [1813] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [1825] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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 [3085] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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 [3121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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 [3253] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3277] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3313] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3325] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3337] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3349] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3385] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3397] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3433] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3445] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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 [3481] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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 [3577] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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 [3661] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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 --- function from context --- 
function (cors, pp, nn, nj = NULL, indexj = NULL, betas, sigmas, 
    lambda, weights, gamma, eps, maxIters, alpha, verbose = FALSE) 
{
    if (!is.integer(pp) || !is.integer(nn)) 
        stop("Both pp and nn must be integers!")
    if (pp <= 0 || nn <= 0) 
        stop("Both pp and nn must be positive!")
    if (is.null(indexj)) 
        indexj <- rep(0L, pp + 1)
    if (is.null(nj)) 
        nj <- as.integer(rep(nn, pp))
    if (!is.vector(indexj)) 
        stop("Index vector for cors is not a vector.")
    if (length(indexj) > pp + 1) 
        stop(sprintf("Index vector for cors is too long, expected to be no greater than %d, the number of columns of data.", 
            pp))
    if (!is.integer(indexj)) 
        stop("Index vector for cors has non-integer component(s).")
    if (any(is.na(indexj) | is.null(indexj))) 
        stop("Index vector cannot have missing or NULL values.")
    if (any(indexj < 0 | indexj > pp + 1)) 
        stop(sprintf("Index vector for cors has out-of-range component(s), expected to be between 0 and %d.", 
            pp))
    if (!is.vector(nj)) 
        stop("Intervention times vector is not a vector.")
    if (length(nj) != pp) 
        stop(sprintf("Length of intervention times vector is %d, expected to match the number of columns of data = %d", 
            length(nj), pp))
    if (!is.integer(nj)) 
        stop("Intervention times vector has non-integer component(s).")
    if (any(is.na(nj) | is.null(nj))) 
        stop("Intervention times vector cannot have missing or NULL values.")
    if (any(nj < 0 | nj > nn)) 
        stop(sprintf("Intervention times vector has out-of-range component(s), expected to be between 0 and %d.", 
            nn))
    if (!is.numeric(cors)) 
        stop("cors must be a numeric vector!")
    if (length(cors) != length(unique(indexj)) * pp * (pp + 1)/2) 
        stop(paste0("cors has incorrect length: Expected length = ", 
            length(unique(indexj)) * pp * (pp + 1)/2, " input length = ", 
            length(cors)))
    aj <- nj/nn
    if (sparsebnUtils::check_if_matrix(betas)) {
        betas <- .init_sbm(betas, rep(0, pp))
        betas <- reIndexC(betas)
    }
    else if (!is.SparseBlockMatrixR(betas)) {
        stop("Incompatible data passed for betas parameter: Should be either matrix or list in SparseBlockMatrixR format.")
    }
    if (!is.numeric(sigmas)) 
        stop("sigmas must be numeric!")
    if (length(sigmas) != pp) 
        stop(sprintf("sigmas must have length = %d!", pp))
    if (any(sigmas < 0)) {
        if (any(sigmas != -1)) {
            stop("sigmas must be > 0!")
        }
    }
    if (!is.numeric(lambda)) 
        stop("lambda must be numeric!")
    if (lambda < 0) 
        stop("lambda must be >= 0!")
    if (length(weights) != pp * pp) 
        stop(sprintf("weights must have length p^2 = %d!", pp * 
            pp))
    if (!is.numeric(weights)) 
        stop("weights must be numeric!")
    if (weights < -1 || weights > 1) 
        stop("weights out of bounds!")
    if (!is.numeric(gamma)) 
        stop("gamma must be numeric!")
    if (gamma < 0 && gamma != -1) 
        stop("gamma must be >= 0 (MCP) or = -1 (Lasso)!")
    if (!is.numeric(eps)) 
        stop("eps must be numeric!")
    if (eps <= 0) {
        if (eps < 0) 
            stop("eps must be >= 0!")
        if (eps == 0) 
            warning("eps is set to zero: This may cause the algorithm to fail to converge, and maxIters will be used to terminate the algorithm.")
    }
    if (!is.integer(maxIters)) 
        stop("maxIters must be an integer!")
    if (maxIters <= 0) 
        stop("maxIters must be > 0!")
    t1.ccdr <- proc.time()[3]
    ccdr.out <- singleCCDr(cors, betas, sigmas, nj, indexj, aj, 
        lambda, weights, c(gamma, eps, maxIters, alpha), verbose = verbose)
    t2.ccdr <- proc.time()[3]
    ccdr.out <- list(sbm = SparseBlockMatrixR(list(rows = ccdr.out$rows, 
        vals = ccdr.out$vals, blocks = ccdr.out$blocks, sigmas = ccdr.out$sigmas, 
        start = 0)), lambda = ccdr.out$lambda, nedge = ccdr.out$length, 
        pp = pp, nn = nn, time = t2.ccdr - t1.ccdr)
    ccdr.out$sbm <- sparsebnUtils::reIndexR(ccdr.out$sbm)
    ccdr.out
}
<bytecode: 0x5498650>
<environment: namespace:ccdrAlgorithm>
 --- function search by body ---
Function ccdr_singleR in namespace ccdrAlgorithm has this body.
 ----------- END OF FAILURE REPORT -------------- 
Fatal error: length > 1 in coercion to logical

* checking PDF version of manual ... OK
* checking for non-standard things in the check directory ... OK
* checking for detritus in the temp directory ... OK
* DONE

Status: 3 ERRORs
See
  ‘/data/gannet/ripley/R/packages/tests-LENGTH1/sparsebn.Rcheck/00check.log’
for details.

Command exited with non-zero status 1
Time 3:08.86, 57.24 + 8.93