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Computing Log-Transformed Kernel Density Estimates for Positive Data
Computes log-transformed kernel density estimates for positive data using a variety of kernels. It follows the methods described in Jones, Nguyen and McLachlan (2018)
Extends Messages, Warnings and Errors by Adding Levels and Log Files
Provides new functions info(), warn() and error(), similar to message(), warning() and stop() respectively. However, the new functions can have a 'level' associated with them, so that when executed the global level option determines whether they are shown or not. This allows debug modes, outputting more information. The can also output all messages to a log file.
R and C++ Interfaces to 'spdlog' C++ Header Library for Logging
The mature and widely-used C++ logging library 'spdlog' by Gabi Melman provides many desirable features. This package bundles these header files for easy use by R packages from both their R and C or C++ code. Explicit use via 'LinkingTo:' is also supported. Also see the 'spdl' package which enhanced this package with a consistent R and C++ interface.
Clear Monitor and Graphing Software Processing Gaussian .log File
Self-Consistent Field(SCF) calculation method is one of the most important steps in the calculation methods of quantum chemistry. Ehrenreich, H., & Cohen, M. H. (1959).
Obtains the Log Likelihood for an Inverse Gamma Stochastic Volatility Model
Computes the log likelihood for an inverse gamma stochastic volatility model using a closed form expression of the likelihood. The details of the computation of this closed form expression are given in Gonzalez and Majoni (2023) < http://rcea.org/RePEc/pdf/wp23-11.pdf> . The closed form expression is obtained for a stationary inverse gamma stochastic volatility model by marginalising out the volatility. This allows the user to obtain the maximum likelihood estimator for this non linear non Gaussian state space model. In addition, the user can obtain the estimates of the smoothed volatility using the exact smoothing distributions.
Fitting Semi-Parametric Generalized log-Gamma Regression Models
Set of tools to fit a linear multiple or semi-parametric regression models with the possibility of non-informative random right-censoring. Under this setup, the localization parameter of the response variable distribution is modeled by using linear multiple regression or semi-parametric functions, whose non-parametric components may be approximated by natural cubic spline or P-splines. The supported distribution for the model error is a generalized log-gamma distribution which includes the generalized extreme value and standard normal distributions as important special cases. Inference is based on penalized likelihood and bootstrap methods. Also, some numerical and graphical devices for diagnostic of the fitted models are offered.
Log Fold Change Distribution Tools for Working with Ratios of Counts
Ratios of count data such as obtained from RNA-seq are modelled
using Bayesian statistics to derive posteriors for effects sizes. This
approach is described in Erhard & Zimmer (2015)
Log-Linear Poisson Graphical Model with Hot-Deck Multiple Imputation
Infer log-linear Poisson Graphical Model with an auxiliary data
set. Hot-deck multiple imputation method is used to improve the reliability
of the inference with an auxiliary dataset. Standard log-linear Poisson
graphical model can also be used for the inference and the Stability
Approach for Regularization Selection (StARS) is implemented to drive the
selection of the regularization parameter. The method is fully described in
Maximum Likelihood Estimation of a Log-Concave Density Based on Censored Data
Based on right or interval censored data, compute the maximum likelihood estimator of a (sub)probability density under the assumption that it is log-concave. For further information see Duembgen, Rufibach and Schuhmacher (2014)
Remove Automated and Repeated Downloads from 'RStudio' 'CRAN' Download Logs
Adjusts output of 'cranlogs' package to account for 'CRAN'-wide daily automated downloads and re-downloads caused by package updates.