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Estimating Software for Heavy ConstructionBuilt by an estimator, SharpeSoft Estimator is a fully comprehensive software that allows for a more efficient and quicker job-winning bids. Ideal for civil, utility, heavy/highway, grading, excavating, paving, and pipeline contractors, SharpeSoft Estimator offers advanced features such as Item Master, Subcontractor Comparison, Materials Comparison, Grouped Items, Trench Profiler, Haul Calculations, What-if Scenarios, Batch Reports, and more.
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Complete Data Management for NonprofitsNewOrg is a robust platform built with enhanced features to help non-profit organizations that capture and integrate the information from all of their operational areas to better manage volunteers, clients, programs, outcome reporting, activity sign-ups & scheduling, communications, surveys, fundraising activities and Development campaigns. NewOrg can truly deliver an intuitive product that will help manage your Committees, Donors, Events, and Memberships so that the organization runs efficiently.
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knowceans
Utility classes from maps to search engine to random samplers
Collection of several multi-purpose Java libraries. --- knowceans-tools = collection of Java utility classes. --- Highlights: --- org.knowceans.util: IndexQuickSort, TableList: apply order of one array/list to others +++ Vectors, ArrayUtils: array convenience +++ RandomSamplers, CokusRandom, ArmSampler, Densities: random sampling and distributions +++ Arguments: command line parser +++ StopWatch, Which, ExternalProcess: runtime stuff +++ ParallelFor: OpenMP workalike +++ PatternString, NamedGroupRegex: regex convenience --- org.knowceans.corpus: CorpusSearcher: full-text search engine +++ LabelNumCorpus: svmlight corpus storage and filtering +++ NIPS corpus with text, authors, labels and citations --- org.knowceans.map: InvertibleHashMultiMap, BijectiveHashMap: implement n:m and 1:1 relations. --- Other libs: knowceans-arms = port of the Adaptive Rejection Metropolis Sampler (ARMS) for arbitrary distributions +++ lda-j = port of lda-c, implementing Latent Dirichlet Allocation (LDA)
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