sima | MPD_sima is an non interactive MPD autoqueue client

a friend told me that it is a problem with kotlin reflection (see the very last lines of exception).

Check it out: https://kotlinlang.org/docs/reference/reflection.html

My problem was solved by adding this dependency in my pom.xml

Looking at a portion of the error message:

unexpected EOF while looking for matching `\"'

It is referring to the field separator assigned by: -F

You need to, in this case, both single-quote and escape the single double-quote.

For sorted labels, we can use np.add.reduceat -

Move most of the logic from within the setInterval into its own function which you can call separately when the page loads. This will ensure that the initial notification isn't blank.

From the SeleniumLibrary document it says that the Get Matching Xpath Count keyword is DEPRECATED in SeleniumLibrary 3.2. Use Get Element Count instead. So you should try to use "Get Element Count" keyword instead. The error you mentioned means your input xpath is wrong. Probably try this xpath=//td[text()="Div1"]

Function survreg() from the survival package fits parameteric accelerated failure time (AFT) models. AFT models are the analogue of simple linear regression applied to survival times (actually the log survival times), and accounting for censoring in the estimation. Because these are parametric models, indeed when you use them you’re making an assumption for the distribution of your survival times.

Function coxph() fits the semi-parametric Cox proportional hazards model. This is a model for the hazard function, and assumes a multiplicative effect of the covariates in the hazard scale. This models does not assume any particular distribution for your survival times, but it does make the proportional hazards assumption (i.e., the effect of covariates in the hazard scale is constant over time).

Function survdiff() perorms the log-rank test, and the Gehan-Wilcoxon test (if you set argument rho to 1). These tests test if the distribution of survival times of specific groups (e.g., males vs females) are equal. The log-rank test is equivalent to score test of a Cox model in which you including the group variable as a covariate.

The problem is actually with Thickness; it's easy to verify that

You need to create two different sheets with two different arrays holding boys and girls information and then export like below

You can use any with a list comprehension to check if 'Anna' exists as either a key in the dictionary or appears as an element in a subdictionary: