* Treatment Choice Cox Model* (

**tccox**)

Builds time-varying covariate terms needed and fits Treatment Choice Cox models (Parametric Treatment Choice, Hybrid Treatment Choice, or Interval Treatment Choice) for observational time-to-event studies. See Troendle, JF, Leifer, E, Zhang Z, Yang, S, and Tewes H (2017) <doi:10.1002/sim.7377>.

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**Discovering Latent Treatments in Text Corpora and Estimating Their Causal Effects****texteffect**)

Implements the approach described in Fong and Grimmer (2016) <https://…/P16-1151.pdf> for automatically discovering latent treatments from a corpus and estimating the average marginal component effect (AMCE) of each treatment. The data is divided into a training and test set. The supervised Indian Buffet Process (sibp) is used to discover latent treatments in the training set. The fitted model is then applied to the test set to infer the values of the latent treatments in the test set. Finally, Y is regressed on the latent treatments in the test set to estimate the causal effect of each treatment.

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**Interim Monitoring Using Adaptively Weighted Log-Rank Test in Clinical Trials****YPInterimTesting**)

Provide monitoring boundaries for interim testing using the adaptively weighted log-rank test developed by Yang and Prentice (2010 <doi:10.1111/j.1541-0420.2009.01243.x>). The package use a re-sampling method to obtain stopping boundaries in sequential designs. The output consists of stopping boundaries at the interim looks along with nominal p-values defined as the probability of the test exceeding the specific observed value or critical value, regardless of the test behavior at other looks. The asymptotic distribution of the test statistics of the adaptively weighted log-rank test at the interim looks is examined in Yang (2017, pre-print).

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**Plackett-Luce Models for Rankings****PlackettLuce**)

Functions to prepare rankings data and fit the Plackett-Luce model jointly attributed to Plackett (1975) <doi:10.2307/2346567> and Luce (1959, ISBN:0486441369). The standard Plackett-Luce model is generalized to accommodate ties of any order in the ranking. Partial rankings, in which only a subset of items are ranked in each ranking, are also accommodated in the implementation. Disconnected/weakly connected networks implied by the rankings are handled by adding pseudo-rankings with a hypothetical item. Methods are provided to estimate standard errors or quasi-standard errors for inference as well as to fit Plackett-Luce trees. See the package website or vignette for full details.

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**Toggl.com’ Api for ‘Rstudio’****togglr**)

Use the <http://toggl.com> time tracker api through R.

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**Joint Segmentation of Multivariate (Copy Number) Signals****jointseg**)

Methods for fast segmentation of multivariate signals into piecewise constant profiles and for generating realistic copy-number profiles. A typical application is the joint segmentation of total DNA copy numbers and allelic ratios obtained from Single Nucleotide Polymorphism (SNP) microarrays in cancer studies. The methods are described in Pierre-Jean, Rigaill and Neuvial (2015) <doi:10.1093/bib/bbu026>.