d_coin_multiplicative()

Compute probability of observations given an exponential decay model 
dcoin_exponential_decay()

Compute probability of observations given an exponential decay model
The idea is that the coin before handling has 100% chance of heads, but each time it is picked up that probability will decrease (maybe it is bent by the statistician's mighty thumb). After halflife times handling it, the probability of heads is 50%, and it keeps dropping from there. 
dcoin_from_probability()

Compute probability of observations given a vector of probability of heads 
dcoin_linear()

Compute probability of observations given linear change model
This is essentially stats::dbinom() but allowing for the probability of heads to linearly change from the starting value. By default it increases by 10% per flip, but this can be set to other values. 
find_congruent_models()

Find congruent models to a simple binomial model
This will find the parameter values for other models that equal the likelihood for a simple binomial model. This may not be the MLE for these other models 
get_possibilities()

Exhaustively get all possible sets of outcomes that result in a specified number of heads out of a certain number of flips 
prob_heads_exponential_decay()

Compute the probability of heads with each flip given an exponential model
The model assumes 100% chance of heads before a coin is picked up and it drops exponentially each time the coin is handled. 
prob_heads_linear()

Compute the probability of heads with each flip given a linear change model. 
prob_heads_multiplicative()

Compute the probability of heads with each flip given a multiplier model
The model assumes 50% chance of heads before a coin is picked up and it changes as a percentage of the previous value each flip. i.e., the probability of heads is 101% of the probability the previous flip with a multiplier of 1.01. 
profile_exponential_decay_model()

Computes the likelihood for a range of values using an exponential coin model 
profile_linear_model()

Computes the likelihood for a range of values using a linear coin model 
try_many_vectors()

Compute probability of observations across many potential vectors
This will try (1/stepsize)^nflips possible vectors, computing the probability of the observation for each 