{"package":"r-n1qn1","ecosystem":"conda","exists":true,"latest_version":"6.0.1_11","repository":"https://github.com/nlmixrdevelopment/n1qn1","license":"CECILL-2.0","description":"Provides 'Scilab' 'n1qn1', or Quasi-Newton BFGS \"qn\" without constraints and 'qnbd' or Quasi-Newton BFGS with constraints. This takes more memory than traditional L-BFGS.  The n1qn1 routine is useful since it allows prespecification of a Hessian. If the Hessian is near enough the truth in optimization it can speed up the optimization problem. Both algorithms are described in the 'Scilab' optimization documentation located at <http://www.scilab.org/content/download/250/1714/file/optimization_in_s","downloads_weekly":453,"deprecated":false,"health":{"score":67},"_cache":"db_only_bot","_partial":true,"_response_ms":0,"_powered_by":"depscope.dev — bot fast path (DB-only)","recommendation":{"action":"review"}}