我在dtwclust包中使用shape方法。当我运行以下代码时：
data(uciCT)
hc.sbd <- tsclust(CharTraj, type = "hierarchical",
k = 20L, distance = "sbd",
preproc = zscore, centroid = shape_extraction,
seed = 320L)
我有以下错误信息：
Error in eigen(M) : LAPACK routines cannot be loaded
In addition: Warning message:
In eigen(M) :
unable to load shared object '//PAPER/fchen4/R/R-3.3.2/modules/x64/lapack.dll':
`maximal number of DLLs reached...
使用库（mgcv）后，我得到了：
Error in eigen(M) : LAPACK routines cannot be loaded
有谁可以帮我解决如何解决此错误。我在R lapack routines cannot be loaded尝试了答案。但他们不适合我。我还更新了所有包。但仍然无法工作。
sessionInfo（）的信息是：
R version 3.3.2 (2016-10-31)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)
locale:
[1] LC_COLLATE=English_Australia.1252 LC_CTYPE=English_Australia.1252 LC_MONETARY=English_Australia.1252 LC_NUMERIC=C
[5] LC_TIME=English_Australia.1252
attached base packages:
[1] parallel splines stats graphics grDevices utils datasets methods base
other attached packages:
[1] mgcv_1.8-17 nlme_3.1-131 dtwclust_3.1.2 dtw_1.18-1 clue_0.3-53
[6] ROSE_0.0-3 scatterplot3d_0.3-39 plot3D_1.1 ggrepel_0.6.5 pdfCluster_1.0-2
[11] pastecs_1.3-18 boot_1.3-18 geosphere_1.5-5 sp_1.2-4 XLConnect_0.2-12
[16] XLConnectJars_0.2-12 ica_1.0-1 visNetwork_1.0.3 igraph_1.0.1 Barnard_1.8
[21] Kendall_2.2 pspearman_0.3-0 FSelector_0.21 dunn.test_1.3.4 randomUniformForest_1.1.5
[26] dbscan_1.1-1 Hmisc_4.0-2 Formula_1.2-1 xgboost_0.6-4 doParallel_1.0.10
[31] iterators_1.0.8 foreach_1.4.3 corrplot_0.77 gbm_2.1.3 survival_2.41-3
[36] AppliedPredictiveModeling_1.1-6 e1071_1.6-8 mlbench_2.1-1 caret_6.0-73 lattice_0.20-35
[41] fpc_2.1-10 devtools_1.12.0 lubridate_1.6.0 ggmap_2.6.1 gridExtra_2.2.1
[46] leaflet_1.1.0 qdap_2.2.5 RColorBrewer_1.1-2 qdapTools_1.3.1 qdapRegex_0.6.0
[51] qdapDictionaries_1.0.6 stringr_1.2.0 xtable_1.8-2 tidyr_0.6.1 scales_0.4.1
[56] plotly_4.5.6 ggplot2_2.2.1 psych_1.7.3.21 mxnet_0.9.4 randomForest_4.6-12
[61] cluster_2.0.6 pROC_1.9.1 openxlsx_4.0.17 proxy_0.4-17 dplyr_0.5.0
[66] plyr_1.8.4
loaded via a namespace (and not attached):
[1] backports_1.0.5 lazyeval_0.2.0 entropy_1.2.1 openNLP_0.2-6 crosstalk_1.0.0 digest_0.6.12 htmltools_0.3.5 gender_0.5.1
[9] gdata_2.17.0 magrittr_1.5 checkmate_1.8.2 memoise_1.0.0 xlsx_0.5.7 tm_0.7-1 wordcloud_2.5 jpeg_0.1-8
[17] colorspace_1.3-2 RWeka_0.4-33 RCurl_1.95-4.8 jsonlite_1.4 lme4_1.1-12 registry_0.3 gtable_0.2.0 MatrixModels_0.4-1
[25] car_2.1-4 kernlab_0.9-25 prabclus_2.2-6 DEoptimR_1.0-8 maps_3.1.1 SparseM_1.76 mvtnorm_1.0-6 rngtools_1.2.4
[33] DBI_0.6-1 Rcpp_0.12.10 CORElearn_1.50.3 plotrix_3.6-4 viridisLite_0.2.0 htmlTable_1.9 magic_1.5-6 foreign_0.8-67
[41] mapproj_1.2-4 mclust_5.2.3 stats4_3.3.2 htmlwidgets_0.8 httr_1.2.1 acepack_1.4.1 modeltools_0.2-21 XML_3.98-1.6
[49] rJava_0.9-8 flexmix_2.3-13 openNLPdata_1.5.3-2 nnet_7.3-12 venneuler_1.1-0 reshape2_1.4.2 munsell_0.4.3 tools_3.3.2
[57] geometry_0.3-6 knitr_1.15.1 ModelMetrics_1.1.0 robustbase_0.92-7 caTools_1.17.1 purrr_0.2.2 RgoogleMaps_1.4.1 mime_0.5
[65] quantreg_5.29 slam_0.1-40 compiler_3.3.2 flexclust_1.3-4 pbkrtest_0.4-7 png_0.1-7 tibble_1.3.0 stringi_1.1.5
[73] trimcluster_0.1-2 Matrix_1.2-8 nloptr_1.0.4 RWekajars_3.9.1-3 data.table_1.10.4 bitops_1.0-6 httpuv_1.3.3 R6_2.2.0
[81] latticeExtra_0.6-28 codetools_0.2-15 reports_0.1.4 MASS_7.3-45 gtools_3.5.0 assertthat_0.1 chron_2.3-50 proto_1.0.0
[89] xlsxjars_0.6.1 pkgmaker_0.22 rjson_0.2.15 withr_1.0.2 mnormt_1.5-5 diptest_0.75-7 grid_3.3.2 rpart_4.1-10
[97] class_7.3-14 minqa_1.2.4 misc3d_0.8-4 NLP_0.1-10 shiny_1.0.1 base64enc_0.1-3