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Helpers for Parameters in Black-Box Optimization, Tuning and Machine Learning
Functions for parameter descriptions and operations in black-box optimization, tuning and machine learning. Parameters can be described (type, constraints, defaults, etc.), combined to parameter sets and can in general be programmed on. A useful OptPath object (archive) to log function evaluations is also provided.
A Fast Way to Calculate the p-Value of One or Multiple Log-Rank-Tests
A very fast Log-Rank-Test implementation that is several orders of magnitude faster than the implementation in the 'survival' package. Log-Rank-Tests can be computed individually or concurrently using threading.
Estimate a Log-Concave Probability Mass Function from Discrete i.i.d. Observations
Given independent and identically distributed observations X(1), ..., X(n), allows to compute the maximum likelihood estimator (MLE) of probability mass function (pmf) under the assumption that it is log-concave, see Weyermann (2007) and Balabdaoui, Jankowski, Rufibach, and Pavlides (2012). The main functions of the package are 'logConDiscrMLE' that allows computation of the log-concave MLE, 'logConDiscrCI' that computes pointwise confidence bands for the MLE, and 'kInflatedLogConDiscr' that computes a mixture of a log-concave PMF and a point mass at k.
Hawkes and Log-Gaussian Cox Point Processes Using Template Model Builder
Fit Hawkes and log-Gaussian Cox process models with extensions. Introduced in Hawkes (1971)
Fitting a Log-Binomial Model using the Bekhit-Schöpe-Wagenpfeil (BSW) Algorithm
Implements a modified Newton-type algorithm (BSW algorithm) for solving the maximum likelihood estimation problem in fitting a log-binomial model under linear inequality constraints.
Bradley-Terry Model with Exponential Time Decayed Log-Likelihood and Adaptive Lasso
We utilize the Bradley-Terry Model to estimate the abilities of teams using paired comparison data. For dynamic approximation of current rankings, we employ the Exponential Decayed Log-likelihood function, and we also apply the Lasso penalty for variance reduction and grouping. The main algorithm applies the Augmented Lagrangian Method described by Masarotto and Varin (2012)
Interim Monitoring Using Adaptively Weighted Log-Rank Test in Clinical Trials
For any spending function specified by the user, this
package provides corresponding boundaries for interim testing using
the adaptively weighted log-rank test developed by Yang and Prentice
(2010
Tree-Based Models for the Analysis of Log Files from Computer-Based Assessments
Enables researchers to model log-file data from computer-based assessments using machine-learning techniques. It allows researchers to generate new knowledge by comparing the performance of three tree-based classification models (i.e., decision trees, random forest, and gradient boosting) to predict student's outcome. It also contains a set of handful functions for the analysis of the features' influence on the modeling. Data from the Climate control item from the 2012 Programme for International Student Assessment (PISA, < https://www.oecd.org/pisa/>) is available for an illustration of the package's capability. He, Q., & von Davier, M. (2015)
Stochastic Gradient Descent log-Likelihood Estimation in Cox Proportional Hazards Model
Estimate coefficients of Cox proportional hazards model using stochastic gradient descent algorithm for batch data.
Utilities from 'Seminar fuer Statistik' ETH Zurich
Useful utilities ['goodies'] from Seminar fuer Statistik ETH Zurich, some of which were ported from S-plus in the 1990s. For graphics, have pretty (Log-scale) axes eaxis(), an enhanced Tukey-Anscombe plot, combining histogram and boxplot, 2d-residual plots, a 'tachoPlot()', pretty arrows, etc. For robustness, have a robust F test and robust range(). For system support, notably on Linux, provides 'Sys.*()' functions with more access to system and CPU information. Finally, miscellaneous utilities such as simple efficient prime numbers, integer codes, Duplicated(), toLatex.numeric() and is.whole().