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briencj

growthPheno:Functional Analysis of Phenotypic Growth Data to Smooth and Extract Traits

Assists in the plotting and functional smoothing of traits measured over time and the extraction of features from these traits, implementing the SET (Smoothing and Extraction of Traits) method described in Brien et al. (2020) Plant Methods, 16. Smoothing of growth trends for individual plants using natural cubic smoothing splines or P-splines is available for removing transient effects and segmented smoothing is available to deal with discontinuities in growth trends. There are graphical tools for assessing the adequacy of trait smoothing, both when using this and other packages, such as those that fit nonlinear growth models. A range of per-unit (plant, pot, plot) growth traits or features can be extracted from the data, including single time points, interval growth rates and other growth statistics, such as maximum growth or days to maximum growth. The package also has tools adapted to inputting data from high-throughput phenotyping facilities, such from a Lemna-Tec Scananalyzer 3D (see <https://www.youtube.com/watch?v=MRAF_mAEa7E/> for more information). The package 'growthPheno' can also be installed from <http://chris.brien.name/rpackages/>.

Maintained by Chris Brien. Last updated 17 days ago.

6 stars 6.66 score 42 scripts

angelacar

TwoTimeScales:Analysis of Event Data with Two Time Scales

Analyse time to event data with two time scales by estimating a smooth hazard that varies over two time scales. If covariates are available, estimate a proportional hazards model with such a two-dimensional baseline hazard. Functions are provided to prepare the raw data for estimation, to estimate and to plot the two-dimensional smooth hazard. Extension to a competing risks model are implemented. For details about the method please refer to Carollo et al. (2024) <doi:10.1002/sim.10297>.

Maintained by Angela Carollo. Last updated 2 months ago.

9 stars 6.26 score 5 scripts

pauleilers

JOPS:Practical Smoothing with P-Splines

Functions and data to reproduce all plots in the book "Practical Smoothing. The Joys of P-splines" by Paul H.C. Eilers and Brian D. Marx (2021, ISBN:978-1108482950).

Maintained by Paul Eilers. Last updated 2 years ago.

1 stars 3.43 score 296 scripts 3 dependents

cran

refitME:Measurement Error Modelling using MCEM

Fits measurement error models using Monte Carlo Expectation Maximization (MCEM). For specific details on the methodology, see: Greg C. G. Wei & Martin A. Tanner (1990) A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms, Journal of the American Statistical Association, 85:411, 699-704 <doi:10.1080/01621459.1990.10474930> For more examples on measurement error modelling using MCEM, see the 'RMarkdown' vignette: "'refitME' R-package tutorial".

Maintained by Jakub Stoklosa. Last updated 4 years ago.

1.70 score

cran

NSAE:Nonstationary Small Area Estimation

Executes nonstationary Fay-Herriot model and nonstationary generalized linear mixed model for small area estimation.The empirical best linear unbiased predictor (EBLUP) under stationary and nonstationary Fay-Herriot models and empirical best predictor (EBP) under nonstationary generalized linear mixed model along with the mean squared error estimation are included. EBLUP for prediction of non-sample area is also included under both stationary and nonstationary Fay-Herriot models. This extension to the Fay-Herriot model that accounts for the presence of spatial nonstationarity was developed by Hukum Chandra, Nicola Salvati and Ray Chambers (2015) <doi:10.1093/jssam/smu026> and nonstationary generalized linear mixed model was developed by Hukum Chandra, Nicola Salvati and Ray Chambers (2017) <doi:10.1016/j.spasta.2017.01.004>. This package is dedicated to the memory of Dr. Hukum Chandra who passed away while the package creation was in progress.

Maintained by Saurav Guha. Last updated 3 years ago.

2 stars 1.30 score

mirrelijn

ecpc:Flexible Co-Data Learning for High-Dimensional Prediction

Fit linear, logistic and Cox survival regression models penalised with adaptive multi-group ridge penalties. The multi-group penalties correspond to groups of covariates defined by (multiple) co-data sources. Group hyperparameters are estimated with an empirical Bayes method of moments, penalised with an extra level of hyper shrinkage. Various types of hyper shrinkage may be used for various co-data. Co-data may be continuous or categorical. The method accommodates inclusion of unpenalised covariates, posterior selection of covariates and multiple data types. The model fit is used to predict for new samples. The name 'ecpc' stands for Empirical Bayes, Co-data learnt, Prediction and Covariate selection. See Van Nee et al. (2020) <arXiv:2005.04010>.

Maintained by Mirrelijn M. van Nee. Last updated 2 years ago.

1.00 score 9 scripts

shuo-yang-sysu

RGS:Recursive Gradient Scanning Algorithm

Provides a recursive gradient scanning algorithm for discretizing continuous variables in Logistic and Cox regression models. This algorithm is especially effective in identifying optimal cut-points for variables with U-shaped relationships to 'lnOR' (the natural logarithm of the odds ratio) or 'lnHR' (the natural logarithm of the hazard ratio), thereby enhancing model fit, interpretability, and predictive power. By iteratively scanning and calculating gradient changes, the method accurately pinpoints critical cut-points within nonlinear relationships, transforming continuous variables into categorical ones. This approach improves risk classification and regression analysis performance, increasing interpretability and practical relevance in clinical and risk management settings.

Maintained by Shuo Yang. Last updated 3 months ago.

1.00 score

shuo-yang-sysu

TCPMOR:Two Cut-Points with Maximum Odds Ratio

Enables the computation of the 'two cut-points with maximum odds ratio (OR) value method' for data analysis, particularly suited for binary classification tasks. Users can identify optimal cut-points in a continuous variable by maximizing the odds ratio while maintaining an equal risk level, useful for tasks such as medical diagnostics, risk assessment, or predictive modeling.

Maintained by Shuo Yang. Last updated 1 years ago.

1.00 score

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