Programming language R contains multiple packages for time series analysis: 'hydroTSM' package is adapted to the time series used in Hydrology, package 'TSA' is used for general interpolation and statistical analysis, while the 'forecast' package includes exponential smoothing, all having outstanding ...
A feature that all programming communities have in common is the numerous debates about why their programming language of choice is better, more advanced, faster, holier etc. In today's data science community, it seems as if these discussions are omnipresent with advocates of SAS, SPSS, R, ...
DBI-based adapter for Presto for the statistical programming language R. Resources Readme License View license Code of conduct Code of conduct Activity Custom properties Stars 132 stars Watchers 14 watching Forks 43 forks Report repository Releases 16 v1.4.7 Latest Dec 22, 2024 + ...
Building predictive models, or carrying out data science research, depends on formulating a hypothesis and drawing conclusions using statistical tests. In this guide, you will learn about how to perform these tests using the statistical programming language, 'R'. The most widely used inferential stat...
Learn everything you need to know about the R programming language and discover why it is the most widely used language in data science.
All data analyses were performed using the statistical programming language R (R Core Team, 2018). Two reaction time (RT) trimming methods were used to remove extreme data points, absolute cut-off and per participant standard deviation (SD) trim. Therefore, RTs of each trial faster than 150...
Statistics Globe is an education platform providing free programming tutorials - R programming & Python - Statistics & data science.
Many Statistical Horizons seminars use R programming language. Here are some free resources to familiarize yourself with R so you can take one of these workshops.
This program is written in the statistical programming language R.doi:10.48550/arXiv.1102.4816Langovoy M. AWittich OEurandomM. Langovoy and O. Wittich. Computationally efficient algorithms for statistical image processing. Implementation in R. EURANDOM Report No. 2010-053. EURANDOM, Eindhoven, ...
All the examples are implemented in the statistical programming language R. Probabilistic Machine Learning: An Introduction (Kevin Murphy) This book is a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. It is written in an informal,...