* Statistical Metrics for Multisite Replication Studies* (

**Replicate**)

For a multisite replication project, computes metrics and confidence intervals representing: (1) the probability that the original study would observe an estimated effect size as extreme or more extreme than it actually did, if in fact the original study is statistically consistent with the replications; (2) the probability of a true effect of scientifically meaningful size in the same direction as the estimate the original study; and (3) the probability of a true effect of meaningful size in the direction opposite the original study’s estimate. Additionally computes older metrics used in replication projects (namely expected agreement in ‘statistical significance’ between an original study and replication studies as well as prediction intervals for the replication estimates). See Mathur and VanderWeele (in preparation) <https://…/> for details.

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**Matrix Reconstruction from a Few Entries****ROptSpace**)

Matrix reconstruction, also known as matrix completion, is the task of inferring missing entries of a partially observed matrix. This package provides a method called OptSpace, which was proposed by Keshavan, R.H., Oh, S., and Montanari, A. (2009) <doi:10.1109/ISIT.2009.5205567> for a case under low-rank assumption.

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**Sensory and Consumer Data Mapping****SensMap**)

Obtain external preference map to explain consumer preferences in function of sensory attributes of products (K.Greenhoff et al. (1994) <doi:10.1007/978-1-4615-2171-6_6>) with options in dimension reduction methods and prediction models from linear and non linear regressions. A smoothed version of the map is available and a comparison of maps stability from different features before and after smoothing is provided which may help industrials to make good decisions about characteristics of new product development. A ‘shiny’ application is included. It presents an easy GUI for the implemented functions as well as a comparative tool of fit models performance using several criteria. Basic analysis such as characterization of products, panelists and sessions likewise consumers segmentation are available.

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**Normalisation Tools for Inter-Condition Variability of ChIP-Seq Data****Brundle**)

Inter-sample condition variability is a key challenge of normalising ChIP-seq data. This implementation uses either spike-in or a second factor as a control for normalisation. Input can either be from ‘DiffBind’ or a matrix formatted for ‘DESeq2’. The output is either a ‘DiffBind’ object or the default ‘DESeq2’ output. Either can then be processed as normal. Supporting manuscript Guertin, Markowetz and Holding (2017) <doi:10.1101/182261>.

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**Distances on Directed Graphs****dodgr**)

Distances on dual-weighted directed graphs using priority-queue shortest paths. Weighted directed graphs have weights from A to B which may differ from those from B to A. Dual-weighted directed graphs have two sets of such weights. A canonical example is a street network to be used for routing in which routes are calculated by weighting distances according to the type of way and mode of transport, yet lengths of routes must be calculated from direct distances.

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**Wrapper for Alpha Vantage API****AlphaVantageClient**)

Download data from the Alpha Vantage API (<https://…/> ). Alpha Vantage is a RESTful API which provides various financial data, including stock prices and technical indicators. There is documentation for the underlying API available here: <https://…/>. To get access to this API, the user needs to first claim an API key: <https://…/>.