Search Results for "using"

broom is part of the tidymodels ecosystem that converts statistical model outputs (e.g. from lm, glm, t.test, lme4, etc.) into tidy tibbles — standardized data frames — using functions tidy(), glance(), and augment(). These are easier to manipulate, visualize, and report programmatically.

The CausalImpact repository houses an R package that implements causal inference in time series using Bayesian structural time series models. Its goal is to estimate the effect of an intervention (e.g. a marketing campaign, policy change) on a time series outcome by predicting what would have happened in a counterfactual “no intervention” world. The package requires as input a response time series plus one or more control (covariate) time series that are assumed unaffected by the intervention, and it divides the time horizon into “pre-intervention” and “post-intervention” periods. ...

...The program can be used to describe information, production, organizational, and other processes. For graphical representation, the EdPM/EPM notation is used, which allowed us to implement: - structural-functional modeling using graphical methods; - the study of the efficiency of structural-functional models using simulation methods, that allow (e.g. unlike Petri nets) to process queries in groups, which is important for the study of the efficiency of using such methods as volumetric calendar planning and AI methods in process activities, since the operating time of these methods depends on the number of parameters and changes nonlinearly; - the study of multiprocess systems; - the results were obtained, that allow you to find efficient topologies of structural-functional models.

...There are also instructions provided in case you are running these analyses via a computing cluster/Slurm workload manager. You can choose to go through the workflow using either an Rmd script, an html file, or a PDF, or via the homepage link provided. If you have questions or concerns, please don't hesitate to reach out. Thanks!

...It was initially developed for miRNA-seq (small RNA, smRNA-seq; hence the name was miRNAselector), RNA-seq and qPCR, but can be applied for every problem where numeric features should be selected to counteract overfitting of the models. Using our tool, you can choose features, like miRNAs, with the most significant diagnostic potential (based on the results of miRNA-seq, for validation in qPCR experiments).

This repository hosts the source for Geocomputation with R, an open-source book covering spatial data analysis, visualization, and modeling using R. It teaches how to work with vector and raster data, coordinate systems, mapping, and geocomputation techniques using packages like sf, terra, tmap, and more. Actively maintained and updated for real-world geospatial workflows.

Scala and polyglot Spark library (Scala, PySpark, SparkSQL, ... ) focused on reading OpenStreetMap Pbf files.

This R package accompanies Richard McElreath’s Statistical Rethinking (2nd edition), offering utilities to fit and compare Bayesian models using both MAP estimation (quap) and Hamiltonian Monte Carlo via RStan (ulam). It supports specifying models via explicit distributional assumptions, providing flexibility for advanced statistical workflows.