Found 131 packages in 0.24 seconds
Flexible Estimation of Conditional Survival Functions Using Machine Learning
Tools for flexible estimation of conditional survival
functions using off-the-shelf machine learning tools. Implements both
global and local survival stacking. See Wolock CJ, Gilbert PB,
Simon N, and Carone M (2024)
Partial Orders and Relations
Finds equivalence classes corresponding to a symmetric relation or undirected graph. Finds total order consistent with partial order or directed graph (so-called topological sort).
Aster Models
Aster models (Geyer, Wagenius, and Shaw, 2007,
Advanced 'tryCatch()' and 'try()' Functions
Advanced tryCatch() and try() functions for better error handling (logging, stack trace with source code references and support for post-mortem analysis via dump files).
K-Means with Simultaneous Outlier Detection
An implementation of the 'k-means--' algorithm proposed
by Chawla and Gionis, 2013 in their paper,
"k-means-- : A unified approach to clustering and outlier detection.
SIAM International Conference on Data Mining (SDM13)",
Continuous Time SEM - 'OpenMx' Based Functions
Original 'ctsem' (continuous time structural equation modelling)
functionality, based on the 'OpenMx' software, as described in
Driver, Oud, Voelkle (2017)
Inference and Prediction of Generic Physiologically-Based Kinetic Models
Fit and simulate any kind of
physiologically-based kinetic ('PBK') models whatever the number of compartments.
Moreover, it allows to account for any link between pairs of compartments, as
well as any link of each of the compartments with the external medium. Such
generic PBK models have today applications in pharmacology (PBPK models) to
describe drug effects, in toxicology and ecotoxicology (PBTK models) to describe
chemical substance effects. In case of exposure to a parent compound (drug or
chemical) the 'rPBK' package allows to consider metabolites, whatever their number
and their phase (I, II, ...). Last but not least, package 'rPBK' can also be used for
dynamic flux balance analysis (dFBA) to deal with metabolic networks. See also
Charles et al. (2022)
Interface to 'Python' Package 'StepMix'
This is an interface for the 'Python' package 'StepMix'. It is a 'Python' package following the scikit-learn API for model-based clustering and generalized mixture modeling (latent class/profile analysis) of continuous and categorical data. 'StepMix' handles missing values through Full Information Maximum Likelihood (FIML) and provides multiple stepwise Expectation-Maximization (EM) estimation methods based on pseudolikelihood theory. Additional features include support for covariates and distal outcomes, various simulation utilities, and non-parametric bootstrapping, which allows inference in semi-supervised and unsupervised settings.
Elevation and GPS Data Visualisation
Simpler processing of digital elevation model and GPS trace data for use with the 'rayshader' package.
Univariate and Multivariate Model-Based Clustering in Group-Specific Functional Subspaces
The funHDDC algorithm allows to cluster functional univariate (Bouveyron and Jacques, 2011,