Top 10 bmcmedresmethodol.biomedcentral.com Competitors

statsdirect.com

statsdirect statistical software for biomedical and public health research. easy to use. advanced algorithms. well-documented. established for over 25 years.

andrewheiss.com

Define what marginal effects even are, and then explore the subtle differences between average marginal effects, marginal effects at the mean, and marginal effects at representative values with the marginaleffects and emmeans R packages

stefvanbuuren.name

Flexible Imputation of Missing Data, Second Edition

ncss.com

easy to use statistical software for professionals, researchers, and academia. comprehensive, accurate, and intuitive. get your free trial of ncss or pass today!

bookdown.org

This is the documentation of the GDP ETL script used for automatic collection and transformation of GDP data for UNCTADstat.

r-bloggers.com

sentiment.aiMore powerful sentiment analysis using deep learning models made simple..See github.io page herehttps://benwiseman.github.io/sentiment.ai/OverviewKorn Ferry Institute's AITMI team made sentiment.ai for researchers and tinkerers who want a straight-forward way to use powerful, open source deep learning models to improve their sentiment analyses. Our approach is relatively simple and out performs the current best offerings on CRAN and even Microsoft's Azure Cognitive Services. Given that we felt the current norm for sentiment analysis isn't quite good enough, we decided to open-source our simplified interface to turn Universal Sentence Encoder embedding vectors into sentiment scores.We've wrapped a lot of the underlying hassle up to make the process as simple as possible. In addition to just being cool, this approach solves several problems with traditional sentiment analysis, namely:More robust, can handle spelling mitsakes and mixed case, and can be applied to dieciséis (16) languages!Doesn't need a ridged lexicon, rather it matches to an embedding vector (reduces language to a vector of numbers that capture the information, kind of like a PCA). This means you can get scores for words that are not in the lexicon but are similar to existing words!Choose the context for what negative and positive mean using the sentiment_match() function. For example, you could set positive to mean "high quality" and negative to mean "low quality" when looking at product reviews.Power Because it draws from language embedding models trained on billions of texts, news articles, and wikipedia entries, it is able to detect things such as "I learned so much on my trip to Hiroshima museum last year!" is associated with something positive and that "What happeded to the people of Hiroshima in 1945" is associated with something negative.The power is yours We've designed sentiment.ai such that the community can contribute sentiment models via github. This way, it's easier for the co

statisticalhorizons.com

Statistical Horizons offers live online statistics courses, trainings, & seminars cover topics like Machine Learning, Econometrics, LDA, & SEM.

researchgate.net

Access 160+ million publication pages and connect with 25+ million researchers. Join for free and gain visibility by uploading your research.

tandfonline.com

search and explore the millions of quality, peer-reviewed journal articles published under the taylor & francis and routledge imprints.