Showing 7 of total 7 results (show query)
uupharmacometrics
xpose4:Diagnostics for Nonlinear Mixed-Effect Models
A model building aid for nonlinear mixed-effects (population) model analysis using NONMEM, facilitating data set checkout, exploration and visualization, model diagnostics, candidate covariate identification and model comparison. The methods are described in Keizer et al. (2013) <doi:10.1038/psp.2013.24>, and Jonsson et al. (1999) <doi:10.1016/s0169-2607(98)00067-4>.
Maintained by Andrew C. Hooker. Last updated 1 years ago.
diagnosticsnonmempharmacometricspopulation-modelxpose
250.5 match 35 stars 7.30 score 315 scriptsuupharmacometrics
xpose:Diagnostics for Pharmacometric Models
Diagnostics for non-linear mixed-effects (population) models from 'NONMEM' <https://www.iconplc.com/solutions/technologies/nonmem/>. 'xpose' facilitates data import, creation of numerical run summary and provide 'ggplot2'-based graphics for data exploration and model diagnostics.
Maintained by Benjamin Guiastrennec. Last updated 2 months ago.
diagnosticsggplot2nonmempharmacometricsxpose
88.4 match 62 stars 11.02 score 183 scripts 6 dependents
jprybylski
xpose.xtras:Extra Functionality for the 'xpose' Package
Adding some at-present missing functionality, or functions unlikely to be added to the base 'xpose' package. This includes some diagnostic plots that have been missing in translation from 'xpose4', but also some useful features that truly extend the capabilities of what can be done with 'xpose'. These extensions include the concept of a set of 'xpose' objects, and diagnostics for likelihood-based models.
Maintained by John Prybylski. Last updated 4 months ago.
53.7 match 6.01 score 5 scripts
kestrel99
xpose.nlmixr2:Graphical Diagnostics for Pharmacometric Models: Extension to 'nlmixr2'
Extension to 'xpose' to support 'nlmixr2'. Provides functions to import 'nlmixr2' fit data into an 'xpose' data object, allowing the use of 'xpose' for 'nlmixr2' model diagnostics.
Maintained by Justin Wilkins. Last updated 3 years ago.
44.0 match 3.38 score 40 scripts 1 dependents
certara-jcraig
Certara.Xpose.NLME:Enhances 'xpose' Diagnostics for Pharmacometric Models from 'Certara.RsNLME' and Phoenix NLME
Facilitates the creation of 'xpose' data objects from Nonlinear Mixed Effects (NLME) model outputs produced by 'Certara.RsNLME' or Phoenix NLME. This integration enables users to utilize all 'ggplot2'-based plotting functions available in 'xpose' for thorough model diagnostics and data visualization. Additionally, the package introduces specialized plotting functions tailored for covariate model evaluation, extending the analytical capabilities beyond those offered by 'xpose' alone.
Maintained by James Craig. Last updated 2 months ago.
42.3 match 1.70 score
wrathematics
float:32-Bit Floats
R comes with a suite of utilities for linear algebra with "numeric" (double precision) vectors/matrices. However, sometimes single precision (or less!) is more than enough for a particular task. This package extends R's linear algebra facilities to include 32-bit float (single precision) data. Float vectors/matrices have half the precision of their "numeric"-type counterparts but are generally faster to numerically operate on, for a performance vs accuracy trade-off. The internal representation is an S4 class, which allows us to keep the syntax identical to that of base R's. Interaction between floats and base types for binary operators is generally possible; in these cases, type promotion always defaults to the higher precision. The package ships with copies of the single precision 'BLAS' and 'LAPACK', which are automatically built in the event they are not available on the system.
Maintained by Drew Schmidt. Last updated 5 days ago.
float-matrixhpclinear-algebramatrixfortranopenblasopenmp
3.3 match 46 stars 10.53 score 228 scripts 42 dependentscertara-jcraig
Certara.ModelResults:Generate Diagnostics for Pharmacometric Models Using 'shiny'
Utilize the 'shiny' interface to generate Goodness of Fit (GOF) plots and tables for Non-Linear Mixed Effects (NLME / NONMEM) pharmacometric models. From the interface, users can customize model diagnostics and generate the underlying R code to reproduce the diagnostic plots and tables outside of the 'shiny' session. Model diagnostics can be included in a 'rmarkdown' document and rendered to desired output format.
Maintained by James Craig. Last updated 12 days ago.
6.9 match 1.70 score