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reckziegel

ffp:Fully Flexible Probabilities for Stress Testing and Portfolio Construction

Implements numerical entropy-pooling for portfolio construction and scenario analysis as described in Meucci, Attilio (2008) and Meucci, Attilio (2010) <doi:10.2139/ssrn.1696802>.

Maintained by Bernardo Reckziegel. Last updated 2 years ago.

bayesian-inferenceentropy-poolingflexible-probabilitiesportolio-optimizationrisk-managementscenariosviews

16 stars 5.71 score 32 scripts

r-forge

cops:Cluster Optimized Proximity Scaling

Multidimensional scaling (MDS) methods that aim at pronouncing the clustered appearance of the configuration (Rusch, Mair & Hornik, 2021, <doi:10.1080/10618600.2020.1869027>). They achieve this by transforming proximities/distances with explicit power functions and penalizing the fitting criterion with a clusteredness index, the OPTICS Cordillera (Rusch, Hornik & Mair, 2018, <doi:10.1080/10618600.2017.1349664>). There are two variants: One for finding the configuration directly (COPS-C) for any Minkowski distance with given explicit power transformations and implicit ratio, interval and nonmetric optimal scaling transformations (Borg & Groenen, 2005, ISBN:978-0-387-28981-6), and one for using the augmented fitting criterion to find optimal hyperparameters for the explicit transformations (P-COPS). The package contains various functions, wrappers, methods and classes for fitting, plotting and displaying a large number of different MDS models (most of the functionality in smacofx) in the COPS framework. The package further contains a function for pattern search optimization, the ``Adaptive Luus-Jaakola Algorithm'' (Rusch, Mair & Hornik, 2021,<doi:10.1080/10618600.2020.1869027>) and a functions to calculate the phi-distances for count data or histograms.

Maintained by Thomas Rusch. Last updated 3 months ago.

1 stars 4.48 score 23 scripts

fangshuye

OssaNMA:Optimal Sample Size and Allocation with a Network Meta-Analysis

A system for calculating the minimum total sample size needed to achieve a prespecified power or the optimal allocation for each treatment group with a fixed total sample size to maximize the power.

Maintained by Fangshu Ye. Last updated 2 years ago.

1 stars 3.70 score 3 scripts

reckziegel

uncorbets:Uncorrelated Bets via Minimum Torsion Algorithm

Implements Minimum Torsion for portfolio diversification as described in Meucci, Attilio (2013) <doi:10.2139/ssrn.2276632>.

Maintained by Bernardo Reckziegel. Last updated 1 years ago.

7 stars 3.54 score 5 scripts

skranz

phack:Detecting p-Hacking using Elliot et al. (2022)

Implements the tests from Elliot et al. (2022) for detecting p-Hacking. The package is essentially a simple wrapper to the code provided in the code and data supplement of the article, with some cosmetical changes. The original code can be found in the code and data supplement of the article. s References Elliott, G., Kudrin, N., & Wüthrich, K. (2022). Detecting p‐Hacking. Econometrica, 90(2), 887-906.

Maintained by Sebastian Kranz. Last updated 3 years ago.

2 stars 3.00 score

olgnaydn

compindexR:Calculates Composite Index

It uses the first-order sensitivity index to measure whether the weights assigned by the creator of the composite indicator match the actual importance of the variables. Moreover, the variance inflation factor is used to reduce the set of correlated variables. In the case of a discrepancy between the importance and the assigned weight, the script determines weights that allow adjustment of the weights to the intended impact of variables. If the optimised weights are unable to reflect the desired importance, the highly correlated variables are reduced, taking into account variance inflation factor. The final outcome of the script is the calculated value of the composite indicator based on optimal weights and a reduced set of variables, and the linear ordering of the analysed objects.

Maintained by Olgun Aydin. Last updated 2 years ago.

2 stars 3.00 score 1 scripts

sangillee

CBSr:Fits Cubic Bezier Spline Functions to Intertemporal and Risky Choice Data

Uses monotonically constrained Cubic Bezier Splines (CBS) to approximate latent utility functions in intertemporal choice and risky choice data. For more information, see Lee, Glaze, Bradlow, and Kable <doi:10.1007/s11336-020-09723-4>.

Maintained by Sangil Lee. Last updated 4 years ago.

openjdk

2.70 score

gitlzg

MarZIC:Marginal Mediation Effects with Zero-Inflated Compositional Mediator

A way to estimate and test marginal mediation effects for zero-inflated compositional mediators. Estimates of Natural Indirect Effect (NIE), Natural Direct Effect (NDE) of each taxon, as well as their standard errors and confident intervals, were provided as outputs. Zeros will not be imputed during analysis. See Wu et al. (2022) <doi:10.3390/genes13061049>.

Maintained by Zhigang Li. Last updated 1 years ago.

cpp

2.70 score 7 scripts

cran

mosqcontrol:Mosquito Control Resource Optimization

This project aims to make an accessible model for mosquito control resource optimization. The model uses data provided by users to estimate the mosquito populations in the sampling area for the sampling time period, and the optimal time to apply a treatment or multiple treatments.

Maintained by Travis Byrum. Last updated 5 years ago.

2.70 score

cran

eiCircles:Ecological Inference of RxC Tables by Overdispersed-Multinomial Models

Estimates RxC (R by C) vote transfer matrices (ecological contingency tables) from aggregate data using the model described in Forcina et al. (2012), as extension of the model proposed in Brown and Payne (1986). Allows incorporation of covariates. References: Brown, P. and Payne, C. (1986). ''Aggregate data, ecological regression and voting transitions''. Journal of the American Statistical Association, 81, 453–460. <DOI:10.1080/01621459.1986.10478290>. Forcina, A., Gnaldi, M. and Bracalente, B. (2012). ''A revised Brown and Payne model of voting behaviour applied to the 2009 elections in Italy''. Statistical Methods & Applications, 21, 109–119. <DOI:10.1007/s10260-011-0184-x>.

Maintained by Jose M. Pavía. Last updated 1 years ago.

1.00 score

hardikghosh911

BaseTempSeed:Estimation of Seed Germination Base Temperature in Thermal Modelling

All the seeds do not germinate at a single point in time due to physiological mechanisms determined by temperature which vary among individual seeds in the population. Seeds germinate by following accumulation of thermal time in degree days/hours, quantified by multiplying the time of germination with excess of base temperature required by each seed for its germination, which follows log-normal distribution. The theoretical germination course can be obtained by regressing the rate of germination at various fractions against temperature (Garcia et al., 1982), where the fraction-wise regression lines intersect the temperature axis at base temperature and the methodology of determining optimum base temperature has been described by Ellis et al. (1987). This package helps to find the base temperature of seed germination using algorithms of Garcia et al. (1982) and Ellis et al. (1982) <doi:10.1093/JXB/38.6.1033> <doi:10.1093/jxb/33.2.288>.

Maintained by Dr. Himadri Ghosh. Last updated 2 years ago.

1.00 score 3 scripts

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