R-universe search: exports:confidence

Showing 4 of total 4 results (show query)

tobiste

tectonicr:Analyzing the Orientation of Maximum Horizontal Stress

Models the direction of the maximum horizontal stress using relative plate motion parameters. Statistical algorithms to evaluate the modeling results compared with the observed data. Provides plots to visualize the results. Methods described in Stephan et al. (2023) <doi:10.1038/s41598-023-42433-2> and Wdowinski (1998) <doi:10.1016/S0079-1946(98)00091-3>.

Maintained by Tobias Stephan. Last updated 9 days ago.

geology structural-geology tectonics

7 stars 7.27 score 33 scripts

spsanderson

RandomWalker:Generate Random Walks Compatible with the 'tidyverse'

Generates random walks of various types by providing a set of functions that are compatible with the 'tidyverse'. The functions provided in the package make it simple to create random walks with a variety of properties, such as how many simulations to run, how many steps to take, and the distribution of random walk itself.

Maintained by Steven Sanderson. Last updated 2 months ago.

random-walk random-walks rpackages

5 stars 6.05 score 5 scripts 1 dependents

jrvanderdoes

fChange:Functional Change Point Detection and Analysis

Analyze functional data and its change points. Includes functionality to store and process data, summarize and validate assumptions, characterize and perform inference of change points, and provide visualizations. Data is stored as discretely collected observations without requiring the selection of basis functions. For more details see chapter 8 of Horvath and Rice (2024) <doi:10.1007/978-3-031-51609-2>. Additional papers are forthcoming. Focused works are also included in the documentation of corresponding functions.

Maintained by Jeremy VanderDoes. Last updated 5 days ago.

openblas cpp openmp

1 stars 4.04 score

luiscastro193

NonProbEst:Estimation in Nonprobability Sampling

Different inference procedures are proposed in the literature to correct for selection bias that might be introduced with non-random selection mechanisms. A class of methods to correct for selection bias is to apply a statistical model to predict the units not in the sample (super-population modeling). Other studies use calibration or Statistical Matching (statistically match nonprobability and probability samples). To date, the more relevant methods are weighting by Propensity Score Adjustment (PSA). The Propensity Score Adjustment method was originally developed to construct weights by estimating response probabilities and using them in Horvitz–Thompson type estimators. This method is usually used by combining a non-probability sample with a reference sample to construct propensity models for the non-probability sample. Calibration can be used in a posterior way to adding information of auxiliary variables. Propensity scores in PSA are usually estimated using logistic regression models. Machine learning classification algorithms can be used as alternatives for logistic regression as a technique to estimate propensities. The package 'NonProbEst' implements some of these methods and thus provides a wide options to work with data coming from a non-probabilistic sample.

Maintained by Luis Castro Martín. Last updated 5 years ago.

1.00 score 1 scripts