#R언어
2024-06-17 Note notes rproject rstudio통계 컴퓨팅 The Comprehensive R Archive Network - cran.rstudio.com
설치 방법 - [리눅스: 이맥스: 개발 환경 구성 방법 : 나우]
데이터 과학을 위한 통계 - 파이썬 R언어 실용주의 통계학 (2021)
R을 사용한 질적 연구 비교 분석: 초보자 가이드(2021)
“sporedata/researchdesigneR - 연구 자료 R언어 위키”
주제별 방대한 자료 다음 참고
(“Sporedata/researchdesigneR - R언어 위키” [2019] 2024) Decision support for the design of patient-centered research, allowing you to blend different methods an answer novel and meaningful patient-centered questions.
- Welcome to the researchdesigneR wiki! Below are the main research method categories.
#00.Datasets
#01.Association
#02.Causation
#03.Computer vision
#04.Data collection
#05.Data linkage
#06.Diagnostics
#07.Health economics
#08.Latent variable modeling
#09.Machine learning
#10.Natural language processing
#11.Meta-analysis
#12.Operations
#13.Qualitative
#14.Randomization
#15.Sensors
#16.Social network analysis
#17.Spatial analysis
#18.Time series
#19.Visualization
All other methods and use cases (methods applied in a specific patient-centered design) are under the categories above. The goal of this section is to allow you to search for the best methods to blend your data and clinical research questions/use cases. A special emphasis is given to complementary methods that allow for the maximum extraction of clinical insights from your data, allowing you to focus on patients’ needs. Last, the list of methods above is not comprehensive but instead focuses on an opinionated version of methods that are most commonly used in practice. For example, we only display the Bayesian version of multilevel models, since their frequentist counterpart is probably less useful. We have made these choices based on our experience with how clinical researchers react to the explanation of each method as well as to whether they see the value-add in these ways of exploring their data. Of course, you are welcome to add other models that you might use in your daily practice.
Of importance, this classification is focused on being operational, meaning that it should assist data scientists in selecting an analytical strategy that fits a patient-centered research question. As a result, this classification is neither comprehensive nor set in stone. Suggestions for modifications are certainly welcome.