Showing 5 of total 5 results (show query)
tidymodels
recipes:Preprocessing and Feature Engineering Steps for Modeling
A recipe prepares your data for modeling. We provide an extensible framework for pipeable sequences of feature engineering steps provides preprocessing tools to be applied to data. Statistical parameters for the steps can be estimated from an initial data set and then applied to other data sets. The resulting processed output can then be used as inputs for statistical or machine learning models.
Maintained by Max Kuhn. Last updated 3 hours ago.
586 stars 18.81 score 7.2k scripts 384 dependents
stathin
ggm:Graphical Markov Models with Mixed Graphs
Provides functions for defining mixed graphs containing three types of edges, directed, undirected and bi-directed, with possibly multiple edges. These graphs are useful because they capture fundamental independence structures in multivariate distributions and in the induced distributions after marginalization and conditioning. The package is especially concerned with Gaussian graphical models for (i) ML estimation for directed acyclic graphs, undirected and bi-directed graphs and ancestral graph models (ii) testing several conditional independencies (iii) checking global identification of DAG Gaussian models with one latent variable (iv) testing Markov equivalences and generating Markov equivalent graphs of specific types.
Maintained by Giovanni M. Marchetti. Last updated 1 years ago.
7.11 score 295 scripts 29 dependents
molina-valero
FORTLS:Automatic Processing of Terrestrial-Based Technologies Point Cloud Data for Forestry Purposes
Process automation of point cloud data derived from terrestrial-based technologies such as Terrestrial Laser Scanner (TLS) or Mobile Laser Scanner. 'FORTLS' enables (i) detection of trees and estimation of tree-level attributes (e.g. diameters and heights), (ii) estimation of stand-level variables (e.g. density, basal area, mean and dominant height), (iii) computation of metrics related to important forest attributes estimated in Forest Inventories at stand-level, and (iv) optimization of plot design for combining TLS data and field measured data. Documentation about 'FORTLS' is described in Molina-Valero et al. (2022, <doi:10.1016/j.envsoft.2022.105337>).
Maintained by Juan Alberto Molina-Valero. Last updated 14 days ago.
forest-inventoryforest-monitoringlidar-point-cloudcpp
23 stars 6.48 score 11 scripts
biometris
statgenGxE:Genotype by Environment (GxE) Analysis
Analysis of multi environment data of plant breeding experiments following the analyses described in Malosetti, Ribaut, and van Eeuwijk (2013), <doi:10.3389/fphys.2013.00044>. One of a series of statistical genetic packages for streamlining the analysis of typical plant breeding experiments developed by Biometris. Some functions have been created to be used in conjunction with the R package 'asreml' for the 'ASReml' software, which can be obtained upon purchase from 'VSN' international (<https://vsni.co.uk/software/asreml-r/>).
Maintained by Bart-Jan van Rossum. Last updated 7 months ago.
geneticsgxegxe-modellingmulti-trial-analysis
10 stars 5.53 score 17 scripts
bkeller2
mlmpower:Power Analysis and Data Simulation for Multilevel Models
A declarative language for specifying multilevel models, solving for population parameters based on specified variance-explained effect size measures, generating data, and conducting power analyses to determine sample size recommendations. The specification allows for any number of within-cluster effects, between-cluster effects, covariate effects at either level, and random coefficients. Moreover, the models do not assume orthogonal effects, and predictors can correlate at either level and accommodate models with multiple interaction effects.
Maintained by Brian T. Keller. Last updated 5 months ago.
3 stars 4.65 score 3 scripts